Author: Jean Chalopin

The Metaverse and Its Ingredients

Posted on by Jean Chalopin

Since the early inception of the metaverse, starting with the 1982 book by Neil Stevenson, “Snow Crash,” the idea of an immersive world without limitations intrigued many. This interest has been boosted by the increased online presence characterizing the Covid pandemic and Facebook’s name change to “Meta.” 

There remain several questions about the metaverse, and we have addressed many of these with our previous article examining the metaverse’s relationship with programmable data. Yet there remain several more questions: Can the metaverse live up to its hype? What is it good for? And how is it distinguishable from any other virtual reality-based world? 

The Metaverse’s Core Idea

The metaverse is, at its core, just a named version of the internet’s evolution to a more social, economically sophisticated, and immersive system. The tech world has two perspectives by which they believe this can be accomplished, which are in opposition to each other.

  1. The decentralized approach: An open and interoperable system owned by the communities that maintain it.  
  2. The centralized approach: A centralized system that is closed and controlled by corporate mandates. This is like the current “Web 2.0” system that demands economic rents from creators, donors, and residents. Think of the Apple store that prevents some apps from being distributed and demands a cut of every app’s revenue.

Open against closed is the distinction separating these two perspectives.

A closed metaverse is a world created by a single entity and is controlled by them. They dictate rules, enforce those rules, and can decide who is excluded and why they are. We can easily imagine Meta developing this example.    

In an open metaverse, individuals govern their own identities, and the collective will enforces property rights and benefits for the users. Transparent, interoperable, and permissionless, an open metaverse enables users to freely build a metaverse of their choice. 

A true metaverse is an open one, where in a Web 3.0 style, the users determine what’s best.

The Seven Ingredients

We have identified seven ingredients that are required to build an open metaverse. 

1.    Decentralized

The overarching fundamental requirement of a healthy metaverse is that it must be decentralized. Centralized networks start as friendly and cooperative places to attract new users and developers.  However, as the growth curve slows, they transition to a competitive system extracting more and demanding a zero-sum game. 

Powerful intermediaries become involved in repeated violations of users’ rights and then may ultimately de-platform, or phase out a version of a metaverse, of their metaverse entirely. A decentralized platform avoids this by propagating user ownership and a healthy community. 

Decentralization is critical. A centralized network stifles innovation while the opposite remains true for a decentralized counterpart. Maintaining decentralization offers the best protection against a failed metaverse. 

2.    Autonomous

The next ingredient of an open metaverse is the self. The first thing you should have in a virtual world is yourself. A person’s identity must persist when crossing the real-to-virtual threshold and across the metaverse. 

Identification is established by authentication, confirming who we are, what we can access, and what information we can provide. This is currently done through an intermediary that conducts the process using solutions like single sign-on (SSO).

Leading tech giants of today (i.e., Google and Meta) built their companies on user data. They collected it by analyzing people’s activity and developing models to provide more relevant and effective marketing. 

Cryptography, which is at the heart of Web 3.0, allows users to authenticate without relying on a central intermediary. Users govern their identity directly or with the support of a chosen service. Crypto wallets (i.e., Metamask or Phantom) can be used for identity authentication. Open-source protocols such as EIP-4361 (Sign-in with Ethereum) or ENS (Ethereum Name Service) can be used by projects to build a decentralized system securing identity. 

3.    Property Rights 

The most popular video games of today make money from the sale of in-game items: skins, weapons, emotes, and other digital things. People who buy these are not really purchasing them but instead renting them. If the game shuts down or unilaterally changes the rules, the players will lose access to their purchases. 

While we are used to this Web 2.0-based system, digital assets could be genuinely “owned,” transferred, sold, and or taken outside of games. The same logic of what is owned in the physical world can be applied to the digital world. When you buy something, you take ownership. It really is that simple. 

These ownership rights should be enforced in the same fashion that courts enforce them in the real world. Digital property rights were not a possibility before the advent of encryption, blockchain, and complementary advances like NFTs.  The metaverse can turn a digital serf into a landowner.  

4.    Flexibility

The mixing and matching of software components in the same way that Legos can be combined is called composability. Each software component is written once, and then it is reused. 

This system is analogous to Moore’s law, or the way interest compounds. The exponential potential that such a system provides has shaped the worlds of finance and computing. It can be applied to the metaverse. 

Promoting metaverse composability, which is closely related to interoperability, requires a high-quality foundation with open technical standards. With Web 2.0, developers build digital goods and novel experiences using a system’s foundational components, like those found in Roblox and Minecraft. 

However, using those goods or experiences outside their native settings is more complicated or impossible. Companies offering embeddable services like Twilio’s communications or Stripe’s payments work across multiple websites and apps, but don’t allow developers to change or alter their code. Composability enables developers to use and modify the underlying codes, similar to open source. 

Decentralized finance (DeFi) is a fairly good example of composability and interoperability. Anyone can adapt, change, recycle, or import the underlying code. Further, engineers can work on live programs, such as Uniswap’s automated market-making exchanges or Compound’s lending protocols, using the memory of Ethereum’s shared virtual computing system. 

5.    Open Source

Composability is not possible without open source. The finest programmers and producers, not platforms, deserve absolute control so that they may be truly innovative. This way, developers can achieve their goals of creating more sophisticated and trustworthy experiences when codebases, algorithms, protocols, and marketplaces are accessible to all.

This openness produces better software, heightened transparency with economic arrangements, and closes information gaps. All these features aid in the development of more egalitarian and equitable systems that align all network participants. Such systems can make many securities laws obsolete, which were designed to address the principal-agent dilemma and asymmetric commercial knowledge.  

6.    Community Ownership 

When a single entity owns and controls a virtual world, it provides limited escapism without offering a truly virtual experience—like a theme park. Users and programmers must not be forced to adhere to the possibly arbitrary rules of centralized management. All stakeholders should have a say in a metaverse’s governance. 

Community ownership is the ingredient that brings together all network players: builders, investors, creators, and consumers. The metaverse, using blockchain and ownership tokens, can provide this level of coordination. 

Web 3.0’s decentralized autonomous organizations (DAOs) have taken this idea to heart. They are moving away from the rigidity of corporate institutions and toward more flexible, democratic, and informal governance models. DAO communities can be constructed, governed, and pushed forward by their users rather than through centralized bodies.  

7.    Total Social Involvement 

Tech companies would like you to believe that virtual reality (VR) and augmented reality (AR) hardware are essential components of the metaverse. They are not necessary but are instead modern Trojan Horses. The tech giants see this hardware as their pathway to being your primary provider of 3D. Yet, the theme park analogy also applies here.

The metaverse does not require VR or AR. The best manifestation of the metaverse demands social immersion. The metaverse enables activities and interactions that are more important than the hardware. People are there to interact, mingle, cooperate, and have fun from anywhere in the real world, as is done with Twitter Spaces, Discord, and Clubhouse.  

Covid-19 showed us the need for more immersive experiences. For example, zoom replaced text chatting. FaceTime and Google Meet entered the market by storm. 

Closing Thoughts

While companies have started building metaverses, any virtual environment that lacks the above ingredients cannot be considered a fully developed metaverse. Web 3.0 is required to achieve the greatest potential inherent to the word metaverse.   

The metaverse is built with openness and decentralization as its core principles. Self-autonomy and property rights must endure the influences of centralized powers and do require decentralization to flourish. With collective ownership, the metaverse avoids the pitfalls of unilateral ownership. With collective ownership, innovation flourishes.   

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment.  Mr. Chalopin is Chairman of Deltec International Group, www.deltecbank.com.

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business.  Mr. Trehan is a Senior VP at Deltec International Group, www.deltecbank.com.

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

IoT Devices Enhance Proactive Risk Management

Posted on by Jean Chalopin

IoT (Internet of Things) is a buzzword that has been around for a few years and is growing in popularity as we slowly connect everything to the net. An enormous amount of data is being collected already, and this is going to the next level through IoT sensors. 

While there are many problems with IoT sensor security that still need to be solved, the data that is being supplied by these devices, if useful and used correctly, has the power to disrupt traditional risk management. This article will discuss some proactive uses of IoT for risk management and why IoT will be invaluable in the finance and insurance fields.

IoT’s Growth

The growth of IoT as a technology is unbelievable. IoT use cases are being seen in nearly every business sector, from connected technologies to cloud computing and digital data.  Pharma is using IoT for material tracking and machine monitoring. Oil producers are using IoT for safe extraction and delivery. The travel industry is connecting aircraft to regulate seat temperatures and other IoT devices to make travel seamless. 

Cannabis producers are using IoT devices for monitoring their plants from seed to store to stay compliant with their local regulations. Any industry can find benefits from IoT devices. And for finance and insurance, this spread of devices can be used for our own needs.  

IoT for Risk Management

The embedding of IoT sensors into physical objects can complement risk mitigation and risk management services. The finance and insurance industries can either piggyback, extracting data from devices that are already installed, or can require the use of our own device’s native sensors. Our goal is to predict and identify risks with reliable accuracy.  

During the COVID-19 pandemic, the use of IoT sensors surged in popularity. The shutdowns of the pandemic forced many businesses to rely on IoT sensors to be their eyes and ears.  

These new sensors had the ability to watch over vacant buildings. If a building’s system fails, the IoT sensor would identify the failure and notify someone to deal with the problem. The ubiquity of these sensors means that there is a continuous supply of tracking data, like with the data inherent to finance and insurance.

At this year’s Risk Management Society (RIMS) conference, several industry leaders from Waymo, Chubb and Prologis Inc. spoke about how IoT is being used for their risk mitigation practices.  

The team members from Chubb, including their chief risk officer, spoke about how IoT is helping Chubb take risk mitigation and management to the next level, allowing them to predict and even prevent potential damage before it happens. A Chubb team member stated that IoT is having a particularly noteworthy impact on their commercial insurance industry. This change is evolving the way that they are now pricing, underwriting, and servicing commercial insurance. 

IoT in Insurance

The adoption of IoT in the commercial insurance segment has accelerated significantly since the beginning of the pandemic, and they expect it to expand further. Chubb’s senior vice president and IoT lead, Hemant Sharma, said that Chubb sees IoT as a valuable opportunity to offer their clients bespoke risk prevention services that will ultimately reduce or, in some cases, avoid losses. 

Prologis Inc’s senior vice president of global risk management, Jeffery Bray, spoke about how critical IoT was to their business. Prologis has a billion-dollar portfolio of warehouses, and they are using IoT to find better ways to manage and predict risk. IoT tech provides the perfect fit as Prologis’s main risk is driven by property exposure. 

The IoT sensors help Prologis get ahead of their operating risks, collect more data in real-time and be more predictive. According to Bray, Prologis is now working on valuing leading indicators as opposed to reacting to lagging counterparts. This switch involves the ideation and development of “autonomous” buildings, those which effectively use IoT devices. 

One new area advancing IoT: drones. After a natural disaster, drones can be utilized to gather in-field data quickly for any resulting claims. Drones gather data for building inspections, providing underwriters with more information and people with faster payouts. 

Potential Uses for IoT in Risk Management

For future uses of IoT, there are two crucial questions to ask:

1.     Will this new technology help drive differentiation in the marketplace?

2.     Will it stand the scrutiny required of a solid and profitable business case?

The risk management space has many candidates that can potentially fulfill these requirements.

Oil and Gas

The oil and gas industry has consistently invested in its sensor and early warning infrastructure to ensure safety. Some of the most common risks in the energy industry are injuries, fires, hazardous gas leaks, and vehicle accidents. 

A collaboration between the energy industry and insurers can be formed through IoT data to look for the early signs of potential accidents. This can prevent costly accidents, environmental spills, and insurance claims.  

Despite preventive measures, risk is always present with oil and gas, and the costs of adverse events are often devastating. Research from 1974 to 2015 shows the total accumulated value of the 100 largest oil and gas disasters exceeds $33 billion. Another report shows that only Russian refinery damage from 2011 to 2015 exceeds $1.5 billion. 

Infrastructure

The variety of sensors for commercial infrastructure OEMs has seen a substantial increase.  These sensors can monitor safety breaches, ranging from water leakage, smoke, overloading of weight-bearing structures, and the presence of mold and mildew, among others. There will be an ongoing integration of infrastructure management systems with IoT data to aid loss prevention programs and provide preventative actions. 

A 2018 study compared a classical (non-telematics, IoT-based) risk model against a telematics-based version and a hybrid (telematics and traditional factors) version, measuring their predictiveness levels. The result: the classic model ranked least predictive. 

Grocery and Other Retail

With the millions of routine visits to these stores and the potential hazardous locations within grocery and convenience store aisles, seafood facilities, salad bars, and liquid storage areas, opportunities for proactive risk management are abundant. 

IoT devices can be used in accident-prone areas to monitor human traffic patterns, debris, and cleaning. Beyond the logging of activity for compliance reasons, IoT can help prepare injury reports and the necessary remedial actions for reducing claims-based losses. 

Smart Homes

We now see the addition of new connected devices entering our homes.  Ring doorbells, smart thermostats, baby monitors, IoT-enabled refrigerators, other appliances, pipe leakage sensors, lighting, and entertainment controls are becoming more commonplace.  If utilized correctly, the resulting increase in data can allow for new innovative insurance products and engagement with the insured and mortgage borrowers. 

Wearables

Connected health wearables such as watches, patches, shoes, socks, and a new supply of industrial safety wearables are entering the market.

These different items of clothing monitor biometric data, as well as odd joint angles (improper lifting technique, carpal tunnel syndrome), bad posture, and more. They help prevent injuries and costly medical insurance claims.

Proactive Risk Management in IoT Programs 

IoT technologies continue to evolve, and the real test is whether the technology can benefit the finance or insurance carrier and the borrower or insured respectively. Until the industry can get a high engagement index with the user, be they personnel or commercial, the chance of the user opting out remains high. Thus, the technology’s potential is limited.

Progressive Insurance and other pioneers in the IoT space have moved in the right direction, initially focusing on the automotive sector. Their Snapshot program rewards the insured with monetary benefits when they can drive safely and avoid high-risk driving behaviors such as late-night driving or excessive acceleration and breaking. 

The result is a “high stickiness” describing their insured population, who will keep lower rates for passing the six-month “Snapshot” test. It also allows Progressive to identify more risky drivers that will not receive the lower rates while still notifying those drivers with “beeps” that their actions are hazardous. Additionally, Snapshot has withstood the scrutiny of actuaries, reshaping how insurers assess, limit, and price the risk of their product offerings. 

Image courtesy of Progressive Insurance

So, what can we do to fulfill the two questions of market differentiation and profit?

  • Develop an ecosystem with technology partners. This means to explore the IoT marketplace thoroughly by studying product roadmaps, vendors, and system integrators. 
  • Continuously experiment. This means to include businesses and markets adjacent to your usual targets through expanded coverage or product rehauls. 
  • Integrate IoT into operations early. In other words, developers must marry underlying systems to IoT-capable devices starting from the ideation stage. 
  • Plan for the long-term. As IoT evolves, business leaders should increasingly take on an “investor mindset,” seeking out opportunities to improve income or reduce costs? 

Closing Thoughts

The internet of things (IoT) is flourishing globally as the number of connected devices continues to expand, projected to grow beyond $50 billion in 2025, with more than two devices for every human (19.1 billion). This massive expansion, coupled with ongoing device computing power improvements, is giving rise to new possibilities for the finance and insurance industries..

Possible incentives include better pricing on mortgages and loans, rebates on policies, and discounts for companies that use them. IoTs also come with added conveniences, such as reduced employee absence, less downtime, and faster repairs. The key is to remain proactive and consistently seek out methods by which IoT reshapes the global risk management industry. 

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment.  Mr. Chalopin is Chairman of Deltec International Group, www.deltecbank.com.

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business.  Mr. Trehan is a Senior VP at Deltec International Group, www.deltecbank.com.

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

The Metaverse and the Programmable World

Posted on by Jean Chalopin

We have previously discussed both the Metaverse and Web3. Enterprises are reimagining the internet, and we as individuals should prepare for the future that is quickly coming to us all. 

Over almost 20 years, companies have developed a wide range of digital capabilities. However, all these solutions were designed for the internet we have at present, generally called Web 2.0, or a digital landscape where the drivers of value are separate entities. The result is more activity offline than online.

The next internet generation will not be restricted by offline thinking or limitations but focus upon connection. Web 3.0 and the metaverse are changing the foundation of our virtual world. 

Rather than seeing the internet as a collection of separate websites and applications, only connected by a browser on a desktop or laptop computer, tablet, or phone, the new metaverse is a persistent 3D environment for work and home. 

Building the Future’s Metaverse

The future’s internet, or metaverse, will only be created through hard work. This involves building new platforms, creating novel products and services, developing strong partnerships, and actioning the required technology. 

The identification of these new business models and use cases is going to require intense effort and much risk-taking. However, there are tremendous opportunities. Many of risk takers who led the shift from Web 1.0 to 2.0 are trying to lead the way to Web 3.0. 

The tech titans had narrow business lines. Amazon only sold books, Netflix mailed out DVDs, and Google felt like just a browser. They evolved. What’s more, the metaverse is open to disruption from all entrants, new or old. 

The metaverse will likely be a combination of an immersive digital-only world and augmented reality. Internet “browsing” in this context would blend the digital with the physical.  

Creating a Digital Domain

Web 3.0 is a reinvention of how data moves, and the metaverse is a new way to experience and interact with that data.

The changes that Web 3.0 is making will result in data with value, authenticity, and provenance. The main goal of all Web 3.0 projects is to create a blanket of trust covering the web, giving data owners assurance that their data is theirs. 

The metaverse focuses on solutions that offer life-like experiences. Major companies are reimagining operations to develop use cases for these new technologies. 

German Automaker BMW has taken Nvidia’s metaverse development platform Omniverse to construct digital copies of 31 of their actual factories. These models are 3D recreations that include everything from people at their workstations to machinery with which they interact. 

They are utilized as virtual test centers allowing engineers to train real-life robots to navigate in the environment. They also allow designers from around the globe to experiment with line layouts. 

The real value of Web 3.0 and the metaverse will depend on their final iterations and mutual interactions. A simple and intuitive experience is required to gain widespread adoption–if we are going to reimagine how Web 3.0 data is moved through the internet. 

Bringing the Physical to the Digital World

Simultaneously, there are other companies and projects that are moving the physical world closer to the digital world, creating a “programmable real world.” Their goal is based on fundamental software elements, such as customization, control, and automation, and applying them to the non-digital environment around us. 

For the past decade, digital technologies have flourished across the physical world, and with the Covid-19 pandemic, this has only accelerated. Cameras are now everywhere, through smartphones, cameras, vacuums, and cars. 

The advances in natural language processing, computer vision, and data analysis are building the capabilities of these technologies, making them a persistent layer of our environment. As the rollout of a global 5G network continues, there will be an even wider network of low latency connected devices that are helping (monitoring) us. 

Business leaders must bring the real and digital worlds closer together, such as through augmented reality glasses, smart (interactive) materials, and nanotech-based devices. 

Making the Real World Programmable

To build a new generation of products and services that are incorporated into the digital world, we will need to work with three elements:

·       The connected

·       The experiential

·       The material

With new technologies in manufacturing, such as 3D printing and self-assembling machines, we are changing how and where physical goods are created.

In the past, internet of things (IoT) devices have had limited abilities, constrained by limited computing power. Emerging tech, including 5G, is redefining those limitations through increased processing power and simultaneous, multiple connections.

Experiential refers to utilizing IoT devices to provide a holistic, immersive experience by creating digital representations of real-world counterparts. These representations provide organizations with real-time insights into environments and operations. The digital twin market, which was valued at $3.21 billion in 2020, is expected to grow to $184.5 billion by 2030. 

The second piece to the experiential component is augmented reality. Even at this early stage, the value of combining AR glasses with digital twins is evident: they can overlay any environment with a digital experience.

Material refers to the on-demand and customizable products that are now possible with 3D printing technology. This leads to the possibility of using programmable matter, able to change their physical properties on demand.

Moving Toward the Programmable World

A leader moving toward the programmable world must embrace its three components: connected, experiential, and material. Different businesses will prioritize one element over others but should have competence in all three.

As 5G grows, the programmable world will benefit from industry-wide alliances shaping new technology standards and enabling devices to increase their connectivity and response times. 

The experiential component will begin with the continued expansion of digital twins, already providing beneficial use cases and competitive edges. In time, they can be harnessed to design products and experiences providing novel business models. Interactive avatars, terminals, and screens represent only a beginning. 

The material component will pair technologies such as IoT or 3D printing with ambitious startups and their ideas. 

Closing Thoughts

The digital and physical worlds are coming together. When this marriage will be complete depends on the progress of the metaverse and Web 3.0. Together, they will reinvent how data is created and transferred across the new digital experience. 

Having the ability to customize a digital experience through interactive, physical manipulation, now referred to as “browsing,” could create massive new revenue streams for leading tech titans and disruptors alike. The opportunities are vast, but they require vision grounded by solid use cases. 

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment.  Mr. Chalopin is Chairman of Deltec International Group, www.deltecbank.com.

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business.  Mr. Trehan is a Senior VP at Deltec International Group, www.deltecbank.com.

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

Does the Metaverse Need Blockchain?

Posted on by Jean Chalopin

With the advancement of blockchain technology, many ideas that were once only hypotheses are materializing. The “metaverse,” a virtual environment, is one of them. What impact will this world have on the global online marketplace and the conventional internet?

Neal Stephenson introduced the concept of the metaverse, a virtual world with all the possibilities of a real one, in his science fiction book “Snow Crash” back in 1992. The concept was only a pipe dream in the early 1990s, but has found new ground with the development of blockchain technology.

A completely functional economy inside the virtual world where you may buy and sell any virtual asset has been created thanks to cryptocurrencies and NFTs. A select few individuals have already been successful in making large sums of money by selling digital artwork, virtual properties, and other items. Unsurprisingly, many adamantly believe in blockchain and the metaverse. 

Understanding Blockchain and the Metaverse

A virtual, online environment created using 3D technology is the “metaverse.” Modern technology developments like blockchain, augmented and mixed reality, non-fungible tokens (NFTs), and many more have a direct relationship to this concept. 

Today, several blockchain-based platforms employ cryptocurrencies and NFTs, establishing an ecosystem for decentralized digital assets creation, ownership, and monetization. The idea of the metaverse is incomplete without blockchain because of the problems inherent to centralized data storage. 

Because blockchain is a decentralized digital source that can operate worldwide, it fundamentally differs from the capabilities of the old internet, which naturally takes the shape of websites and apps. Any digital place may be accessed through the blockchain-based metaverse without the influence of a centralized authority.

Source: BBC News

Blockchain Unlocks the Metaverse’s Potential

The fundamental operating principles of the metaverse’s ecosystem have already been devised, even if there is still no singular notion of the metaverse. The concept itself is only partially implemented in initiatives like the Metaverse Facebook Horizon and Google Blocks.

Hardware and software are the two significant blocks of any metaverse. Users may comfortably engage with virtual or augmented reality thanks to the hardware component, which incorporates all common controllers. In the case of software, we’re referring to a digital setting where the user has access to the material.

The majority of those in the sector now concur that software should be built on blockchain technology, which stands for a secure decentralized database where independent nodes may communicate in a single, constantly updated network. Looking at blockchain technology’s key features makes it rather clear that it can satisfy the needs of the metaverse.

Source: https://intellipaat.com/blog/tutorial/blockchain-tutorial/how-does-blockchain-work/

Security. The exabyte-scale data storage of the metaverse presents concerns about secure transmission, synchronization, and storage. The decentralization of data processing and storage nodes makes blockchain technology extremely pertinent.

Trust. Blockchain requires the existence of tokens, which are safe storage units capable of conveying things like encrypted personal data, virtual content, and authorization keys. Because sensitive data won’t be accessible to outside parties, the metaverse blockchain fosters greater user confidence in the ecosystem.

Decentralization. For the metaverse to work properly, all participants must view the same virtual reality. Blockchain-based decentralized ecosystems enable thousands of independent nodes to coordinate.

Smart contracts. Through these, relationships between ecosystem players inside the metaverse may be efficiently regulated in terms of economic, legal, social, and other factors. Additionally, smart contacts let you create and implement the fundamental guidelines for the metaverse’s governance.

Source: https://www.bitpanda.com/academy/en/lessons/what-are-smart-contracts-and-how-do-they-work/

Finance. Cryptocurrency may function as a reliable substitute for fiat currency because it is an essential component of a blockchain. It is also a valuable tool for settlement between parties in the metaverse.

Centralized ecosystems pose significant hazards to the development and operation of the virtual world. These include viruses, hacking, and even centralized decision-making that affects how the metaverse works. However, the dangers are reduced with blockchain technology, making it feasible to create a reliable virtual environment.

Blockchain Use Cases in the Metaverse

There are several use cases for blockchain in the metaverse. 

Virtual Currency

One of the most apparent applications of blockchain technology in the metaverse is settlements. The time when consumers purchase in 3D is quickly approaching. We can be confident that cryptocurrencies will soon find uses in a decentralized environments since offline commerce is progressively giving way to internet businesses.

MANA, which is used to purchase virtual property in the game “Decentraland,” is one of the metaverse instances of how virtual currencies are utilized. Within this metaverse, agreements worth millions of dollars are already being signed, and this is only the beginning. 

Users will soon be able to purchase virtual replicas of everything in the real world. This technology won’t be restricted to only video games. The rapidly growing Defi market might serve as a beta-testing environment for metaverse lending, borrowing, investing, and trading. As a result, the potential of cryptocurrencies is potentially limitless.

NFTs

Numerous analysts predict that non-fungible tokens will play a significant part in the metaverse. NFTs also have considerable potential for integration into any metaverse crypto initiatives involving the purchase of avatars, gaming assets, and other such items. Non-fungible tokens will soon be utilized as evidence of real estate ownership if this field keeps growing.

Source: https://mint.intuit.com/blog/investments/what-is-an-nft/

NFTs will ultimately be used as prizes in metaverse NFT games, instead of fungible tokens. Since practically every digital asset may be copied an infinite number of times, NFTs can assign value to specific digital assets. Only a certificate of ownership integrated into a digital object can verify the right of the legal owner.

Identity Authentication

Identity authentication in the metaverse is carried out similarly to how a social security number is assigned. The blockchain stores all information about a particular user, including their age, activities, appearance, and other traits. As a result, the metaverse becomes as transparent as possible and remains free from criminal activity.

Identity authentication also eliminates the chance that someone would use a fake name in a virtual environment to commit crimes.

Closing Thoughts

Blockchain technology is essential to the metaverse because it allows users to safeguard their digital assets in a virtual reality. Actual blockchain initiatives like “Axie Infinity” and “The Sandbox” emphasize this concept. They both concern the metaverse. By using a metaverse-based cryptocurrency, users may build and trade NFTs, as well as profit from the virtual economy.

Without blockchain technology, experts believe that the concept of a fully functional virtual environment cannot be realized. This is because, as we’ve mentioned, consumers must be allowed to safely own and sell their digital property by transferring assets between the platforms without a centralized authority’s consent.

Blockchain guarantees the economic efficiency and transparency of this metaverse. It is crucial to employ trustworthy algorithms while building a virtual reality replacing tangible assets with digital ones. In other words, the future is digital. 

Disclaimer: The information provided in this article is solely the authors opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment.  Mr. Chalopin is Chairman of Deltec International Group, www.deltecbank.com.

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business.  Mr. Trehan is a Senior VP at Deltec International Group, www.deltecbank.com.

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

Comprehensive Frameworks Are Coming to the Digital Asset Space

Posted on by Jean Chalopin

The White House’s press release issued last month points to a global, assertive, 360-degree take on the rapidly growing digital asset space and digital asset service providers. It covers:

●      Consumer protection

●      Affordability

●      Financial stability

●      Responsible innovation

●      Illicit finance

Further, it introduces a multi-regulator approach and places the spotlight squarely upon ensuring the stability of digital assets. For example, digital asset service providers, located anywhere, may now enter the crosshairs of US enforcers if they infringe upon a consumer located within American borders. 

While this feels like an escalation, it truly represents a leap in the right direction for the digital asset space’s advancement, stablecoins and central bank digital currencies included. The White House’s release follows the European Union’s introduction of the proposed Markets in Crypto Assets regulation (“MiCA”) in June 2022, which promotes the regulated use of stablecoins and enforces registration for digital asset service providers. 

Earlier in the same month, Japan passed a landmark bill designed to regulate stablecoins after the collapse of TerraUSD in May. An algorithmic stablecoin, Terra had relied on natural market forces, high lending rates, and partial reserves in Bitcoin to defend against a rout–without maintaining a one-for-one peg to a fiat currency such as the US dollar. 

It can be argued that this collapse kick-started and finally woke regulators up to the great potential of digital assets, stablecoin or otherwise, and to their lurking risks as well. This is what the White House means with a reference to “responsible” innovation. 

However, we cannot forget how The Bahamas passed its own landmark piece of legislation in December 2020, well ahead of the market turbulence and crypto winter. The Digital Assets and Registered Exchanges (DARE) Bill covers all facets of the digital asset sector: cryptocurrencies, stablecoins, digital asset service providers, coin exchanges, and, even, initial coin offerings. 

This article reviews the White House’s press release as part of a greater, global movement supporting the future of digital assets, and how the DARE Act already hit the mark. 

The Digital Asset Space and the White House

Inside the press release are nine key quotes or excerpts all actors in the digital asset space must pay attention to:

  1. Regulators, such as the Securities Exchange Commission (SEC) and Commodity Futures Trading Commission (CFTC), should “aggressively pursue investigations and enforcement actions” against unlawful practices in the digital asset space.
  1. The Consumer Financial Protection Bureau (CFPB) and the Federal Trade Commission (FTC) must “redouble efforts to monitor consumer complaints and to enforce against unfair, deceptive, or abusive practices.” 
  1. The Financial Literacy Education Commission shall lead public efforts to help consumers understand the risks involved with the digital asset space. 
  1. Relevant agencies will encourage the “adoption of instant payment systems, like FedNow.”
  1. Agency recommendations shall be reviewed, on whether to establish a framework to regulate non-bank payment providers. 
  1. The Treasury and similar financial regulators should provide US firms developing “new financial technologies with regulatory guidance, best-practices sharing, and technical assistance.” 
  1. The Department of Commerce shall establish a “standing forum” to convene all relevant public and private actors in the digital asset space to foster and coordinate ideas and growth. It shall also help US digital asset firms sell, or “find a foothold” for, their products or services in global markets. 
  1. The President shall evaluate whether to call for an amendment to the Bank Secrecy Act (BSA) to explicitly include cryptocurrencies, digital asset service providers, and non-fungible tokens (NFTs). Further, he shall consider whether to amend federal statutes and permit the Department of Justice to “prosecute digital asset crimes in any jurisdiction where a victim of those crimes is found.” 
  1. The Treasury is due to complete an “illicit finance risk assessment” on DeFi by February 2023, and a further assessment on NFTs by July 2023. 

When the EU Council released their MiCA, there had been an implicit hope for the rest of the world to follow their guidance on the treatment of stablecoins. While the US clearly acknowledged the need for a stablecoin framework geared foremost towards protecting consumers first, it addressed the equally clear lack of guidance pertaining to unbacked cryptocurrencies, NFTs, and DeFi. 

In other words–it sought to launch a global campaign addressing the entire digital asset space. 

Understanding America’s Framework

Points 1-3 above focus on the potential for illicit activity in the unregulated digital asset space, acknowledging costly gaffes of the past such as the fall of TerraUSD. Here, the White House states, in no uncertain terms, that it will investigate and prosecute those who seek to prey off of consumer ignorance while digital assets continue to grow and enter mainstream portfolios. 

Points 4-5 recognize and echo the founding ethos of cryptocurrencies using blockchain technology: financial accessibility. For the banked, the cost of owning, transferring, and otherwise utilizing money remains far too high as CNBC reports that the US banking system is “one of the most encumbered and heavily surveilled in virtually any Western country.” For the unbanked, traditional banks continue to present barriers to entry as the White House itself reports that “roughly 7 million Americans have no bank account.” 

Source: CNBC Television

Points 6-7 serve to create public-private partnerships vital to the developing digital asset space. Points 8-9 seek to provide ammunition to the necessary regulators and enforcers while providing a global reach. If a victim of illicit activity is found in Pennsylvania, for example, then there are now grounds for the Department of Justice to pursue the criminal–wherever they may reside. 

Central Bank Digital Currencies (CBDCs) 

The framework also reintroduces CBDCs into the spotlight. In this case, the prospect of a “digital dollar.” 

The White House press release reads: “It could enable a payment system that is more efficient, provides a foundation for further technological innovation, facilitates faster cross-border transactions, and is environmentally sustainable. It could promote financial inclusion and equity by enabling access for a broad set of consumers.”

This clearly echoes the reason why stablecoins exist–to provide instant, cross-border transactions, securely, and with little to no cost. 

It is worth noting that The Bahamas which is a well- regulated and progressive jurisdiction, had already launched its own CBDC–the Sand Dollar in October 2020. This was a demonstration of an early understanding of the points raised and framework foreshadowed by the White House. The Sand Dollar serves to: 

●      Provide secure transactions at much faster settlement speeds. 

●      Achieve greater financial inclusion and cost-effectiveness across all of The Bahamas. 

●      Strengthen national efforts against money laundering, counterfeiting, and other illicit activities. 

The DARE Act Did It Already

The DARE Act, which was passed by The Bahamas’ Parliament in December 2020, demonstrated how a progressive international financial center embraced innovation without compromising on effective regulation to provide the necessary guidance for the digital asset industry ahead of G7 nations.

This pioneering move and collaborative approach was an impetus for world-leading digital asset exchange, FTX, moving its headquarters from Hong Kong to The Bahamas in September 2021. In April 2022, FTX and Anthony Scaramucci’s SALT premiered their first “Crypto Bahamas” conference, bringing in many of the globe’s top players in the digital asset space, including former President Bill Clinton and former British Prime Minister Tony Blair. 

Specifically, the DARE Act provides comprehensive guidance on: 

  1. Data protection.
  2. Digital asset trading.
  3. The registration and operation of digital asset service providers, including financial, compliance, and anti-money laundering (AML) requirements.
  4. The registration and operation of digital asset exchanges, and their requirements.
  5. Initial token offerings and the correct procedures for coming to market. 
  6. Relevant rules, oversight powers, and sanctions designed to ensure a well-functioning digital asset marketplace. 

This is a non-exhaustive list, but covers the fundamental building blocks necessary for encouraging digital asset innovation in The Bahamas. The framework successfully competes on the world stage, as already demonstrated with the success of Crypto Bahamas. At a time when uncertainty surrounds the regulatory direction for the digital asset space, The Bahamas remains unafraid to show its expertise–backed by many decades–with financial services, compliance, and international law.

Disclaimer: The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment. Mr. Chalopin is Chairman of Deltec International Group, www.deltecbank.com.

The co-author of this text, Emmanuel O. Komolafe, is a member of The Bahamas’ Digital Advisory Panel and one of the nation’s leading governance, risk and compliance experts. Mr. Komolafe is the Chief Risk Officer of Deltec Bank & Trust Limited, www.deltecbank.com.

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees. This information should not be interpreted as an endorsement of cryptocurrency or any specific provider, service, or offering. It is not a recommendation to trade.

Machine Learning and Predictive Analytics 

Posted on by Jean Chalopin

Machine learning (ML) is widely used as a predictive technology in fields such as transportation, finance, healthcare, advertising, travel, and several manufacturing industries across the globe. Machine learning and predictive analytics aid companies in making better decisions by anticipating what will happen. 

ML and predictive analytics predict future outcomes through the analysis of current and past data. The two terms machine learning and predictive analytics are sometimes used interchangeably, and although related, they are two different disciplines. 

Machine learning can be applied to various applications, while predictive analytics focuses on forecasting specific variables and scenarios. Combining predictive analytics with machine learning is a powerful way for financial companies to gain value from the massive amount of data generated and collected through business operations. 

We will go through these two concepts and how they can be used to improve processes and be a foundation for a company’s underlying abilities.  

Machine Learning and Predictive Analytics, in Brief

Machine learning is a subsection of artificial intelligence (AI) that creates computer algorithms designed to improve their accuracy as they process or “learn” from large data sets. Machine learning’s ability to learn using previous data and its adaptability with a wide array of applications makes it highly beneficial. Fraud and malware detection, spam filtering, and image analysis are a few of the many applications of machine learning by industry.

Predictive analytics uses tools and techniques to build predictive models for forecasting outcomes. Its methods include machine learning algorithms as well as statistical modeling, descriptive analytics, data mining, and advanced mathematics. Predictive analytics is an approach rather than a defined technology.  

Predictive Analytics

Predictive Analytics is a type of advanced analysis building upon two earlier analytics types that were done through human coding, descriptive and diagnostic analytics. Companies use descriptive analytics to see, for example, how many items were sold yesterday or this week, while diagnostic analytics subdivides that data to determine why fewer items were sold this week than the week before.  

Predictive analytics utilizes measurable variables in order to predict the behaviors of people or things, like buying habits of an individual customer, when a machine requires maintenance or a forecast of a store’s or company’s sales. Classical statistical techniques like linear and logistic regressions, and machine learning techniques such as neural networks, support vector machines, and decision trees are applied to predictive modeling. 

The need for expert knowledge of these advanced techniques means that predictive analytics has been the domain of data scientists, analysts, and statisticians. This requirement is beginning to change as business intelligence vendors offer advanced AI capabilities and analytics in their platforms, resulting in the democratization of analysis by business users. 

Strong business leadership is needed for the deployment of predictive analytics because the first step of a successful deployment is defining the business’ objectives and the project’s goal. The next priority is the identification of the correct data and analytical techniques needed to build a robust predictive model. Having high-quality data is necessary during the training, especially if the data sets are smaller. 

Machine Learning

Artificial intelligence is the replication of human intelligence by computers. AI includes a broad range of diverse technologies beyond machine learning, including robotics, natural language processing, and computer vision. These wide-ranging technologies are all meant to replicate human actions.

Machine Learning is a software-based AI that becomes better at predicting without being programmed to do so. The program learns by detecting patterns in data sets. Machine learning algorithms are created to be versatile, allowing developers to make changes with parameter tuning.  

Machine learning is the foundation for neural networks and deep learning, which are used to do such tasks as financial forecasting and the driving done by autonomous vehicles ML can increase the rate at which data is processed and analyzed.

By applying machine learning to predictive analytics applications, algorithms train using extensive data sets and perform complex analyses on several variables with only minor manual changes. 

Machine learning and AI provide benefits that make them enterprise staples, and there is no longer debate over their value. In the past, their operationalization required a complicated transition, but the technology is now successfully implemented across multiple industries.

Predictive Analytics Versus Machine Learning

To recap, predictive analytics applies advanced mathematical techniques to discover patterns in current and historical data to predict future events, while machine learning is a tool that automates predictive modeling through training algorithms searching for patterns and behaviors in data while not receiving explicit instructions.

There are several key differences:

  • Machine learning can be trained through supervised or unsupervised methods, and it is the foundation of several advanced technologies such as deep learning, computer vision, and autonomous vehicles.  
  • Predictive analytics is built on the fields of descriptive and diagnostic analytics, and it is a stepping stone to prescriptive analytics. This type of analytics provides guidance on contextual-specific next steps. 
  • Machine learning algorithms are designed to both evolve and improve their predicting abilities with their continued processing of more data, without being programmed by humans to do so.

Just as the value of machine learning and artificial intelligence in business has become widespread, their differences have lessened. As ML gains more widespread understanding and employment in business applications, it becomes a more integral feature in predictive analytics.

Use Cases

The successful application of machine learning and predictive analytics by enterprises is widespread. Here are a few examples:

  • Marketing and retail organizations are using various prediction models to refine their strategies. Predictive analytics is being used to spot website user trends, hyper-personalize advertising, and target emails. 
  • Manufacturers, including airplane makers, are using prediction models to monitor machinery and equipment and identify when failures will happen.
  • Healthcare organizations use prediction models to identify outbreaks and extrapolate outcomes beyond drug trials, new drug approvals, and the course of disease based on past data.

Challenges

While predictive analytics and ML techniques are becoming embedded in more “novice usable” software resulting in so-called “one-click” forecasting, enterprises will face the usual challenges associated with getting value out of their data. This starts with the data itself.  

All types of data, including corporate data, are error-prone, inconsistent, and incomplete.  Finding the correct data and preparing it for processing and forecasting is time-consuming.  Expertise in deploying and interpreting predictive models is still scarce. 

To assume that the one-click solution will be accurate is dangerous and must be tested.  Moreover, software for predictive analytics is expensive, and so is the processing required to create effective models. 

Finally, machine learning technologies continue to evolve rapidly, resulting in continuous scrutiny on how and when to upgrade to newer approaches. 

Financial Applications

The global financial markets have experienced the profound impact of machine learning and predictive analytics on various aspects of digital pricing. From international financial organizations down to retail traders, digital pricing techniques are used to generate maximum profit and returns. 

Moreover, when applied, predictive analytics and machine learning can improve trading strategies for all asset classes, including cryptocurrency and digital pricing markets. Similar pricing techniques are being applied for a sustainable future when conducting global business.  

Finally, using ML and predictive analytics, organizations conduct faster and cheaper transfers, or exchange currencies more rapidly..  

Closing Thoughts

The complementary nature of applying machine learning with predictive analytics makes the combination a powerful tool for forecasting in finance and several other fields. When trained with clean data and then applied in the correct fashion, the accuracy and speed of their abilities exceeds that of several humans combined. 

The key to long-term success is to create the proper environment with defined goals and success metrics, using clean data from the beginning and then evaluating the application over time. As ML and predictive analytics applications broaden their reach, their acceptance will soon become commonplace.

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment.  Mr. Chalopin is Chairman of Deltec International Group, www.deltecbank.com.

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business.  Mr. Trehan is a Senior VP at Deltec International Group, www.deltecbank.com.

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

AI and Blockchain: Disrupting Together

Posted on by Jean Chalopin

Businesses that effectively embrace the digital transition will prosper as the world changes. The world as we know it is about to change because of two developing technologies: blockchain and artificial intelligence (AI).

However, it’s not always clear whether these two technologies complement or compete. Are they compatible or antagonistic with one another?

How can you ensure that your company embraces blockchain and AI seamlessly?

Blockchain

A blockchain is a continuously growing digital ledger that records every transaction that has ever taken place. The continuous ledger that is made up of a “chain” of “blocks” of data are connected across a network of computers and is referred to as a “blockchain.”

The network of computers that manages the blockchain disperses data across the network, unlike a conventional database, which is controlled by a single entity. As a result, a highly secure, transparent, and immutable distributed network system is produced.

Source: https://www.euromoney.com/learning/blockchain-explained/what-is-blockchain

This is so that any changes or additions to the ledger don’t require simultaneous changes to all of the computers on the blockchain network, which each maintains an identical copy of the ledger. As a result, the blockchain system creates a very safe and impenetrable record of transactions.

AI

Artificial intelligence is the term used to describe computers that, through intricate algorithms built into the software, may display understanding resembling humans. AI enables organizations to develop and flourish by automating repetitive operations, improving decision-making, and streamlining procedures.

AI is computer code created to imitate and reproduce human intellect. It is a machine with human-like learning, comprehension, and response capabilities.

Source: https://sciencenotes.org/what-is-ai-or-artificial-intelligence-an-ai-answers/

Machine learning (ML), a subset of AI, can fuel AI. A computer program that can “learn” and enhance its performance over time is referred to as ML. 

For instance, if an AI system can be taught to recognize pictures, it may utilize the information to figure out what a particular image is. Everything from facial recognition to comprehending customer purchase behavior may be done using machine learning.

How AI and Blockchain Work Together

Blockchain and AI can combine to address sharing economy or supply chain management issues. AI-generated data and insights may be utilized to enhance the precision and accuracy of blockchain technology and establish new levels of system trust.

While computer systems and apps utilize AI algorithms to automate processes and activities, blockchain technology serves three core purposes when dealing with these systems and applications:

  • Blockchain technology is used to distribute/share the results of AI algorithms among several decentralized players, such as companies linked by a shared interbank network or a blockchain-based supply chain.
  • Internet of Things (IoT) devices leverage blockchain technology in addition to artificial intelligence. IoT devices frequently employ AI-driven algorithms. Some of these gadgets are also employed as oracles, which provide blockchain networks with data from the real world.
  • Some blockchain-based cryptocurrency and decentralized finance (Defi) systems utilize AI algorithms for various tasks, including cross-platform asset management and trading option suggestions.

By incorporating data from sensors and cameras, which can gather more exact data, such as the date and location of an item’s creation, AI may help blockchain become more accurate and precise. The accuracy of the blockchain record can increase thanks to the data from these sensors. In addition, demonstrating an item’s provenance and assisting in eradicating fraud can enhance user confidence in the system.

Use Cases for AI and Blockchain

Substantial potential for industry-specific integration of AI and blockchain has emerged due to the expanding use of these two technologies. While some of the opportunities are now fully tapped into, others are more likely to do so in the future. The following sectors demonstrate the greatest need for or the possibility of applying blockchain-based AI solutions.

Decentralized Finance (DeFi)

Some DeFi apps already make use of artificial intelligence by:

• Maximizing proposals for exchange trades

• Automatically putting up the best portfolios of cryptocurrency assets from several platforms

• Predicting asset rate changes to assist in the decision-making of crypto investors

Most DeFi platforms now rely primarily on individuals to make the majority of trading and investing decisions, while they offer some limited automation capability for these processes. Essentially, they provide the user with a trading interface, letting them weigh their alternatives and decide what to do.

A few Defi systems have already made technological advances in AI. For instance, the yield farming app YAI.Finance on the Oraichain platform employs AI algorithms to assist users in evaluating the risks and rewards of various investment situations and provides suggestions for the best course of action.

Source: https://zephyrnet.com/oraichain-review-the-ai-powered-oracle-system/

Banking

The following are some use cases for blockchain and AI in the banking sector:

Banks use AI algorithms to detect and identify questionable transactions as part of their anti-money laundering efforts. Blockchain technologies may be used to exchange and access the necessary information because many anti-money laundering duties involve collaboration between various banks, financial institutions, and governmental organizations.

For the onboarding of new clients, banks often implement stringent KYC (“Know Your Customer”) checks. In this procedure, AI features like picture recognition are routinely applied. Additionally, it is frequently required for banks and governmental organizations to share data, especially in suspicious circumstances. Blockchain technology may enable multiple banks and government agencies to cross-verify the relevant identification data in this situation.

One of the most popular uses of AI in banking is evaluating new clients’ credit risk. Sharing the findings of these evaluations between banks and credit score reporting bureaus can be facilitated by blockchain-based systems. In addition, to improve credit risk assessments of consumers, they can also be utilized to obtain extra information from multiple banks and reporting agencies.

Source: https://www.semanticscholar.org/paper/DyCREM%3A-Dynamic-Credit-Risk-Management-Using-Li-Shi/c5ac1b1fb66a6abc64ee4db6d6deeb809adad8e1

Insurance

Another significant consumer of both blockchain and AI technology is the insurance sector. 

Fraud detection in claims processing. To streamline their claims processing operations—the main activity of any insurance business—many insurance companies are turning to blockchain-based solutions. Blockchain networks link various reinsurers, brokers, healthcare organizations, auto repair shops, and other stakeholders.

To identify fraudulent claims, these claims processing networks usually utilize AI algorithms. For instance, critical data is provided to blockchain-based claims systems through external oracles to verify claims. The sensors and cameras deployed on roads and AI capabilities to assess traffic and road incidents are typical examples of these oracles.

Optimal insurance plan and rate development. Insurance carriers utilize AI algorithms to predict customers’ future claims events and behaviors, much as banks use AI to evaluate customers’ credit risk. The best insurance prices and policies are created based on these projections.

The accuracy of these AI forecast algorithms may significantly increase by using blockchain technology to inject external data into them from other insurers, governments, credit reporting agencies, and healthcare providers.

Source: https://www.startus-insights.com/innovators-guide/8-blockchain-startups-disrupting-the-insurance-industry/

Government

Government use cases for blockchain-based AI are numerous, given how often governments employ AI to fight crime, estimate economic models, develop urban settings, and offer various services to the populace. 

Inter-departmental networks powered by blockchain for identity management and delivering a range of public services. These platforms heavily rely on AI technology, such as face recognition software, to identify identity theft and provide the general public with simplified services.

Intergovernmental blockchain-based networks employ artificial intelligence (AI) to look for and find financial crimes, unauthorized human movement, and tax evasion.

AI-based blockchain land registry records are used to identify fraud in land claims. However, due to challenges in obtaining and measuring land and other country-specific factors, land register records in many nations include inaccurate and occasionally false information.

Such a register might be managed by a blockchain and AI-based system, reducing fraud. The Swedish government is now testing a solution like this for its land register database.

Retail

Supply networks for big players in the retail sector are getting more intricate. Hence, the blockchain emerges as an appealing option to hold data for transparency and optimization as these supply networks get more complicated.

Supply chains for supermarkets frequently employ AI to predict the ideal amounts of inventory and orders for the next delivery period. All the network participants involved, including farmers, distributors, resellers, and transportation firms, may plan and optimize their activity levels and operations as these projections are communicated across a blockchain-based supply chain system.

Knowing the supermarket’s anticipated order level for the upcoming buying season will be advantageous to each of these network partners for the supermarket.

Supermarkets already utilize AI imaging technology to spot rotten produce on their shelves. Retailers might reduce expenses associated with purchasing subpar food by integrating AI technology to detect inferior product quality at the source of purchase and transmitting this information over the blockchain.

Source: https://blog.accubits.com/ensuring-food-safety-blockchain/

The openness and accountability of original produce makers and distributors concerning delivering quality food will improve by storing this information on a blockchain-based supply chain system.

Closing Thoughts

Blockchain-based AI solutions open up a wide range of new prospects. The Defi, finance, insurance, retail, and government sectors are where AI will succeed most.

Blockchain and AI are frequently combined through sharing AI algorithm outputs on blockchain systems, using AI-enabled oracles to supply blockchain with data, and using asset optimization and prediction algorithms based on AI on various Defi platforms.

As the combined usage of blockchain and AI expands, the Defi sector should likewise experience rapid growth in intelligent automation. Platforms that offer AI-enabled automated trading and investment advice will win users away from Defi platforms that do not, as these services become the industry standard.

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment. Mr. Chalopin is Chairman of Deltec International Group, www.deltecbank.com.

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business. Mr. Trehan is a Senior VP at Deltec International Group, www.deltecbank.com.

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

How AI Transforms Medical Research

Posted on by Jean Chalopin

Using artificial intelligence (AI), businesses have been moving toward digital transformation long before the Covid-19 pandemic in their collective quest to optimize production, product quality, safety, services, and customer experiences. Some actively desired a sustainable planet for all. 

The advantages of the next digital era feel limitless. Still, businesses are hesitant to adopt these technologies because they require significant behavioural and structural changes, such as new business models, operating procedures, worker skill sets, and mindsets. These technologies include not only AI, but machine learning, and deep learning “at the edge” (where rapid automation occurs). 

The pandemic acted as a wake-up call to drastically accelerate the timescale for digital transformation since it put our way of life in danger. 

The need is urgent and lifesaving, and the time is now. This is supported by a recent IBM poll that shows the Covid-19 epidemic caused the majority of global organizations (six out of 10) to accelerate their digital transformation strategies.

Source: https://www.globaldata.com/covid-19-accelerated-digital-transformation-timeline-pharmaceutical-industry/

Due to the pandemic, we can see how creative problem-solving and once-in-a-lifetime risk-taking leads to incredible breakthroughs and significant improvements. 

Medical research is one vital area that is reaping the benefits of accelerated AI adoption. 

AI and Predicting Outbreaks

Epidemiologists are already benefiting from the improvement of AI algorithms, which evaluate ever-increasing amounts of data made accessible to the public and track the onset and spread of infectious illnesses. To forecast the spread of the flu and other diseases in various regions, researchers are analysing geographical data and internet search inquiries on common symptoms.

Time is an advantage. Before calling a doctor, people are already aware that they are unwell. Before obtaining professional assistance, many people attempt to self-diagnose online. 

Epidemiologists may use machine learning models to anticipate the spread of the flu in a particular location with a high degree of probability if they see a surge in searches for phrases like “sore throat” or “difficulty swallowing” originating from IP addresses in a specific ZIP code.

Source: https://time.com/5780683/coronavirus-ai/

Governmental health organizations assess crowd densities by location and analyse that information to forecast the likelihood of future outbreaks using public data and demographic mapping. For instance, to train machine learning models in indicating how many people would visit specific sites on a given day, health authorities in Europe, Israel, China, and other places utilize anonymized mobile phone traffic density data. Venues might limit attendance, reduce visiting hours, or even close if the total rises to pandemic levels.

Optimizing Treatment

AI is already being used to diagnose diseases earlier and with more accuracy, such as cancer. The American Cancer Society claims many mammograms provide misleading findings, telling one in two healthy women they have cancer. Mammogram reviews and translations are now 30 times faster and 99% accurate thanks to AI, eliminating the need for pointless biopsies.

People with chronic or lifelong diseases may perform better thanks to AI. One inspiring example: Machine learning models analyse cochlear implant sensor data to provide deaf patients feedback on how they sound so they can interact with the hearing world more effectively. 

Computer Vision

In contrast to the human eye, AI-based computer vision can quickly sift through thousands of images to find patterns. In medical diagnostics, where overworked radiologists struggle to pick up every detail of one image after seeing hundreds of others, this technology is a great help. AI assists human specialists in situations like this by prioritizing visuals that are most likely to show a problem.

Source: https://www.altexsoft.com/blog/computer-vision-healthcare/

X-rays, CT scans, MRIs, ultrasound pictures, and other medical images provide a rich environment for creating AI-based tools that support clinicians with identifying various problems.

Drug Discovery

Small-molecule drug development can benefit from AI in four different ways: access to new biology, enhanced or unique chemistry, higher success rates, and speedier and less expensive discovery procedures. The solves numerous problems and limitations in conventional research and development. Each application gives drug research teams new information, and it might completely change tried-and-tested methods in certain situations.

Source: https://zitniklab.hms.harvard.edu/drugml/

AI is used by BioXcel Therapeutics to find and create novel drugs in the areas of neurology and immuno-oncology. The business’s drug re-innovation initiative also uses AI to uncover fresh uses for current medications or to locate new patients.

Transforming the Patient Experience

Time is money in the healthcare sector. Hospitals, clinics, and doctors treat more patients each day by effectively delivering a smooth patient experience.

In 2020, more than 33 million patients were admitted into U.S. hospitals, each with unique medical needs, insurance coverage, and circumstances that affected the quality of care. According to studies, hospitals with satisfied patients make more money, while those with dissatisfied patients may suffer financial losses.

New advancements are streamlining the patient experience in AI healthcare technologies, enabling medical personnel to handle millions, if not billions, of data points more effectively.

Employers who want to give their staff the tools to maintain good mental health can use Spring Health’s mental health benefits solution. 

Each person’s whole dataset is collected as part of the clinically approved technology’s operation, and it is compared to hundreds of thousands of other data points. Using a machine learning approach, the software then matches users with the appropriate specialist for in-person care or telemedicine sessions.

For treating chronic illnesses like diabetes and high blood pressure, One Drop offers a discreet solution. With interactive coaching from real-world experts, predictive glucose readings powered by AI and data science, learning resources, and daily records taken from One Drop’s Bluetooth-enabled glucose reader, the One Drop Premium app empowers people to take control of their conditions.

AI Does Not Replace Humanity

Faster, more accurate diagnoses and lower claim processing error rates are just two of the potential benefits of AI that CEOs at healthcare organizations already see. But they must also realize that no amount of advanced technology will ever fully replace the human experience.

Business executives must also consider the possibility of bias in AI algorithms based on past beliefs and data sets, and put safeguards in place to address this problem. For instance, there has historically been discrimination in how specific populations’ medical illnesses are identified and treated.

AI is there to augment human decision-making in healthcare–not replace it. 

Closing Thoughts

Traditionally, it’s tricky to understand whether AI is living up to its potential or whether everything we read is merely hype. For several years, due to the roadblocks outlined at the beginning of this article, progress has been slow and needed some hype. However, the pandemic is genuinely accelerating the integration of AI in healthcare and medical research. It almost sounds cliché now, but Covid-19 has initiated a “new normal” in healthcare. 

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment.  Mr. Chalopin is Chairman of Deltec International Group, www.deltecbank.com.

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business.  Mr. Trehan is a Senior VP at Deltec International Group, www.deltecbank.com.

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

Understanding Blockchain Oracles

Posted on by Jean Chalopin

As you continue delving deeper into the world of blockchain technologies, you will hear about “blockchain oracles.” These oracles have no relation to the cloud application company Oracle, but they provide a way by which the decentralized Web3 ecosystem can access existing data, legacy systems, new data, and advanced computations. 

When blockchain oracles and decentralized networks are combined, they can create hybrid smart contracts, allowing for on-chain and off-chain infrastructure to support decentralized applications or DApps, that can react to real-world events and interact with traditional systems. Oracles connect a blockchain to an input or an output.

Image courtesy of chain.link

Take, for example, a sports bet between Ann and Bill. Ann bets Bill $50 that the Cardinals will beat the Steelers. Ann and Bill put $50 (total $100) in a smart contract escrow. At the game’s finale, how does the smart contract know to whom it should release the funds?  This answer requires an oracle that will obtain accurate data about the game’s outcome from an off-chain source and provide it to the blockchain securely and reliably.  

Blockchain Oracles Defined

Blockchain oracles are entities connecting blockchains to external systems, allowing smart contracts to execute based upon the inputs and outputs the oracles transfer to and from the real world.

The Oracle Problem

In general, blockchains are closed systems. The “oracle problem” blockchains have is a fundamental limitation on smart contracts. Smart contracts cannot interact inherently with data systems outside their native blockchain. Any resources that are not on the native blockchain (on-chain) are designated as “off-chain.” 

Blockchains obtain their most valuable properties by being purposely isolated from external systems.  Their properties include robust consensus of user transaction validity, the prevention of double spending, and the mitigation of network downtime. If a chain is to securely interoperate with any off-chain data or systems, an additional piece of infrastructure known as an oracle is required to bridge the two environments. 

The oracle problem must be solved because most smart contracts, especially those related to DeFi, require the knowledge of real-world data and events that happen off-chain. Oracles, therefore, expand the types of digital arrangements that blockchains can support by offering a gateway to the many off-chain real-world resources while upholding the blockchain’s valuable attribute of security. 

Many industries can benefit from combining oracles and smart contracts. This includes asset prices in finance, weather information for the insurance industry, randomness for gaming needs, IoT sensors for shipping, ID verification for governments and others, and much more.

Because the data supplied to a blockchain from an oracle will determine the outcome of any related smart contracts, it is vital that the oracle mechanism is correct and the data that the oracle is pulling from is accurate.

Decentralized Oracles

Suppose a blockchain’s oracle mechanism uses a centralized entity to deliver the data to the smart contract. In that case, this introduces a single point of failure, which defeats the entire purpose of utilizing a decentralized blockchain. Additionally, if the oracle goes offline, then the smart contract will not have access to the required data for its execution, or it may execute incorrectly due to stale data. 

Worse still, if the single oracle were to be corrupted, the data that was being delivered to the smart contract could be incorrect and may result in improper execution and bad outcomes, the epitome of garbage in, garbage out. Also, because a blockchain’s transactions are executed automatically and are immutable, a smart contract supplied with faulty data could not be reversed, and the escrow funds could be lost permanently. This single point of failure is why centralized oracles cannot be utilized for smart contract applications.  

Overcoming the oracle problem and a single point of failure requires using decentralized oracles, which prevent data manipulation, data inaccuracy, and downtime. Decentralized Oracle Networks (DONs) extend the decentralized nature beyond the blockchain from end to end. 

What’s more, some DONs, like Chainlink’s Price Feeds, have a three-layer decentralized system:

1.    From the data source

2.   The node operator

3.   Oracle network levels

This system further ensures the elimination of any single point of failure. It has been successfully used to secure tens of billions of dollars across smart contract ecosystems, using the multi-layered decentralized approach, resulting in smart contracts that can safely rely on the data inputs provided to execute.   

Courtesy of pricefeeds

Blockchain Oracle Types

Because there is an extensive range of off-chain data sources, blockchain oracles come in several varieties. Hybrid smart contracts not only need various types of off-chain data and computations, but they also require different delivery mechanisms and security levels. Each oracle will conduct a combination of the following four tasks required of the off-chain data:

·       Fetching

·       Validating

·       Computing 

·       Delivering

Input Oracles

The most common and widely recognized oracles utilized today are known as “input oracles,” which fetch data offline from the real world, then deliver it to the on-chain network for use by smart contracts.  Input oracles are used to provide off-chain financial market data to on-chain DeFi smart contracts to execute correctly.  

Output Oracles

Working in the opposite direction to input oracles are output oracles. These oracles allow smart contracts to send commands to various off-chain systems, which will cause the execution of certain actions. 

Such actions include informing a banking network to make or release a payment, informing a storage provider that items can be released, or that supplied items or data can be stored. At a slightly more complex level, an IoT system could be informed to unlock a rented vehicle that was paid for using an on-chain car rental service. 

Cross-Chain Oracles

The third type of oracle is a cross-chain oracle, which can read and write information carrying it between different blockchains. Cross-chain oracles are the key to enabling interoperability, allowing for the movement of data and assets between separate blockchains. 

Moving such data from one blockchain can trigger an action on the other, or assets can be bridged across the chains so that they can be used outside of the native blockchain where they were issued.  These oracles may be the bridge to wider crypto acceptance.

Compute-Enabled Oracles

The newest type of oracles, becoming more widely utilized for smart contract applications, are computer-enabled oracles. These oracles secure off-chain computations to provide decentralized services that are unworkable on-chain due to technical, legal, or financial constraints. 

Services such as off-chain computation on Aglorand and Keepers on Chainlink can automate running smart contracts when predefined events occur, such as Zero-Knowledge Proofs (ZKPs) where one party can prove to another that they have the knowledge about a piece of information without having to reveal that information, or by running a verifiable randomness function which provides a provably fair tamper-proof source of randomness beyond the control of a smart contract.  

Hybrid smart contracts can be constructed with advanced capabilities by using multiple oracles.

Hybrid smart contract construction courtesy of link education

Oracle Reputations

Because there are so many choices, finding an oracle service with a strong reputation is critical when deciding which one to use. Blockchain oracle reputation systems allow users and developers to monitor and filter through oracles, based on the parameters they believe are essential. An oracle’s reputation is aided by the fact that oracles sign and deliver data to a blockchain’s immutable public ledger, allowing for the historical performance to be reviewed and provided to blockchain users through interactive dashboards like reputation.link and market.link.

Such reputation frameworks provide each oracle network and node (oracle) operators accuracy and reliability. Developers can then make an informed decision as to which oracle they want for their smart contracts. 

Oracle Use Cases

Smart contract developers can build more advanced Dapps using oracles, giving them a more comprehensive range of use cases for on-chain applications. There are potentially an infinite number of use cases with each new oracle added, but the following are the most common:

Decentralized Finance (DeFi)

A large swath of the decentralized finance (DeFi) ecosystem requires the use of oracles to access financial data (markets and assets). Decentralized money markets, for example, use price oracles to determine a user’s borrowing capacity and check if the user’s positions are undercollateralized and require liquidation. Likewise, synthetic asset platforms will use price oracles to peg the value of their tokens to real-world assets, while automated market makerswill use price oracles to concentrate liquidity at the current market price improving capital efficiency.  

Gaming and Dynamic NFTs

Non-financial use cases can be enabled through oracles such as on-chain gaming, which can use verifiable randomness to create unpredictable and more engaging gameplay for users, like the appearance of prizes or a randomized bracket during a tournament. 

Oracles can also be applied to smart contracts such as dynamic NFTs (Non-Fungible Tokens) that can change their appearance, value, or distribution, depending on external events like the time of day, the weather, or by completing a task in gaming. What’s more, computer oracles can be used to generate verifiable randomness that is then used by a project to assign random traits to an NFT or for selecting the lucky winner when a high-demand NFT is dropped.  

Insurance

Insurance smart contracts use inputs from oracles to verify the occurrence of an insurable event during the claims processing period by accessing physical sensors, APIs, satellite images, and legal data. Insurance smart contracts also use output oracles to make payouts for claims using other blockchains or for linking to a traditional payment service.  

Enterprises

The use of cross-chain oracles provides enterprises with a secure bridge between chains, a blockchain middleware that allows them to connect backend systems to any blockchain network. This structure allows enterprise systems to read and write on any blockchain and perform complex logic operations deciding how best to deploy assets and data for recipients on the same oracle network. 

The result: enterprises work quickly, joining blockchains in high demand and swiftly creating support for any smart contract services desired by users. 

Sustainability

Oracles play a critical role with sustainability by supplying smart contracts with environmental data from IoT and similar sensors, satellite images, and machine learning computations. This data enables smart contracts to dispense rewards to those that conduct reforestation initiatives or participate in conscious consumption. By extension, oracles also support the carbon credit process that is intended to offset a company’s climate change impact. 

Closing Thoughts

Oracles extend the capabilities of blockchain networks and the smart contracts running on them, providing access to several off-chain data stores and resources. These can be harnessed to create advanced, hybrid smart contracts whose use cases can now go far beyond simple tokenization and transfer of value. 

Much like how the original Internet brought forth monumental change, democratizing data and how it is exchanged, hybrid contracts powered by oracles are redefining how the world economy should function. 

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment. Mr. Chalopin is Chairman of Deltec International Group, www.deltecbank.com.

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business. Mr. Trehan is a Senior VP at Deltec International Group, www.deltecbank.com.

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

Manufacturing and Recycling Electric Vehicle Batteries: The Environmental Impact

Posted on by Jean Chalopin

The recent passing of the Inflation Reduction Act was touted as a significant step forward for reducing fossil fuel use and pushing the US to switch to electric vehicle (EV) technology. 

Sadly, the devil is in the details, and the details of this bill are not very good. The tax credit in the bill will be the current $7,500 credit extended to 2032, and an additional $4,000 added to that credit for a total of $11,500. The problem is that according to the Alliance of Automotive Innovation, around 70% of the electric hydrogen and hybrid vehicles currently being sold in the US would not be eligible for the credit because the bill states: to qualify for the credit, final assembly must take place in North America, and will hinge on the vehicle’s size, total cost, and potential buyers’ income.  

Before 2024, 40 percent or more of the critical minerals and half of the battery components must come from the US or a free trade partner to access the total credit. However, these vital materials are sourced globally. Most cobalt comes from the Democratic Republic of the Congo, and lithium is sourced from South America and Australia, with the processing of these materials taking place in China.

Assuming the California fossil fuel ban from 2030 happens, and other states follow suit, we must ask, are EVs more environmentally friendly to produce, and what will happen to all of these batteries once their time is up? The front end has an environmental impact from lithium mining, cobalt, and other essential metals like nickel. 

Let’s look at the contents of an electric vehicle battery, where they end up when their life is over, and ask if they are the best environmental choice. 

Can We Recycle Electric Vehicle Batteries?

Fortunately, EV batteries are highly recyclable. Cleantech company Li-cycle can extract and use over 95% of a lithium-ion battery’s components via a method called hydrometallurgy. Hydrometallurgy involves grinding the battery components up and running them through an acid solution.  

From there, several solvents and a series of electroplating rounds can pull the individual elements out of the solution. A simpler method of smelting is available, but requires more energy, and its results are less than impressive.

The resulting pollution caused by either method of battery recycling is negligible. However, the current problem is that there are insufficient recycling facilities operating at the required scale to process the increasing number of electric car batteries that are already coming to their end of life. 

As of 2022, a study from the Journal of the Indian Institute of Science found that in the US and EU, we are recycling less than 1% of the lithium-Ion we consume, which is down from a 2019 study that found global recycling of lithium-Ion batteries at about 5%. For comparison, we recycle about 99% of our lead-acid batteries used in vehicles and the power grid. 

The value of lithium, cobalt, and nickel is growing, and this study from the Clausthal University of Technology shows that recycling is economically viable. They state that process routes for cobalt, copper, and nickel achieve high yields, but lithium processing is more difficult and results in a lower yield albeit at a higher economic value.

Lithium Mining’s Environmental Impact

Lithium is a vital component of our modern batteries and plays an important role in battery chemistry, but it comprises only about 11% of a battery’s total mass. Most of the world’s lithium supply comes from Australia, Chile, and China, with the current global production of 500,000 metric tons of lithium carbonate equivalent (LCE) in 2021. 

McKinsey has estimated that this will grow with a sharp trajectory to between three and four million by 2030. In 2015, automotive needs ate up approximately 31% of the lithium supply, and this is expected to be the main user of the global supply going forward.

Data courtesy of Danwatch

Lithium is extracted in two ways: from salt flats and hard rock mining. When the hard spodumene ore (a translucent, grayish-white aluminosilicate mineral and essential source of lithium) is mined, it is broken apart, separated, and acid washed, with the lithium sulfate eventually separated out from the rest of the mix. 

The hard rock process is economically cheap compared to salt flat processing, but the product is low grade. This standard mining method comes with the customary environmental risks of pollutants forming in tailing ponds. And because hard rock mining is labor intensive, this method produces about triple the emissions per ton of lithium than that shown with salt flats. 

The world’s largest lithium producer, Australia, has about 46% of the global lithium production and relies heavily on the hard rock mining method.  

Salt flats are formed when water is pumped underground, and on its return to the surface, brings with it dissolved minerals. The brine is spread across several pools to evaporate, and left behind are minerals to be separated and processed. 

Salt flat mining is common in the triangle that overlaps Argentina, Bolivia, and Chile. The nearby Andes Mountain range has created large subsurface lithium deposits due to geothermal activity, which leaches minerals from volcanic rock. Dry higher elevations promote faster evaporation of the brine pools. 

The primary cost of salt flat extraction is its water use, and obtaining exact numbers is difficult. However, estimates for water use range from 250 gallons per extracted pound up to 240,000 gallons.  The Chilean government has provided data suggesting that the water for brine production at the Atacama flats exceeds the aquifer’s ability to resupply by 30%, and its lithium mining uses about 65% of the region’s water. 

These mining operations are taking place in a high desert where the water supply is limited; indigenous communities are in a water crisis predicament, and local agriculture is being strained. Bolivian indigenous groups living near abandoned mines also have to deal with the materials left behind, disrupting local ecosystems. 

Many of these indigenous groups have been subjected to similar abuses by international mining firms in the past. The result is that the communities now are in staunch opposition to new mining projects or have claimed significant ownership of the projects. 

The Other Materials Used in Batteries

Batteries contain several other materials, such as cobalt, nickel, and graphite. Half of the world’s supply of cobalt is mined out of Congo. There has been heavy Chinese investment into the Congo, resulting in many industrial mining operations that feed Chinese battery production demands.

However, local workers are often excluded from such enterprises and relegated to digging unsafe artisanal mines with minimal, if any, recourse in the case of injuries. These locals end up selling their cobalt to the same traders who work with the industrial-scale mines, and it is eventually ferried to China.

Production of nickel is not as tenuous but is not without cost. Nickel is mined throughout the globe, and around 30% of the total supply comes from Indonesia. Most nickel is used in stainless steel, but 6% is used to make batteries.  

Are Electric Vehicles Good for the Environment?

When taken collectively, it may appear that there is a high cost to making electric vehicles a reality. When assessing the lifecycle impacts of electronic versus traditional fossil fuel burning vehicles, EVs are undoubtedly front-loaded with emissions due to the environmental cost of making batteries. Yet, the difference is made up over the vehicle’s lifetime. 

It’s estimated that in the US internal combustion engines produce between 60 to 68% more emissions than EVs. Considering the outsized role that fuel makes in this calculation, creating a grid using more clean energy is almost as important as putting more EVs on the road. In Europe, depending on how the EV is charged, average emissions savings range between 28% and 72%.  

Closing Thoughts

At the end of the day, a transition to electric vehicles is still necessary to make a real change to global emissions. Nonetheless, those living near mining operations still have a significant number of environmental, water, and health challenges to contend with, even before they are confronted with the challenges of climate change. 

Governments should be doing a better job holding the mining industry to higher standards of proper site management. We must also build out the electric infrastructure that includes multiple sources of green energy and its effective distribution. 

On the other end of the EV lifecycle, we need to make the recycling of lithium batteries easier and preferable to lead acid batteries. It is up to us to push democratic governments towards a greener future and hold them to account for the hazardous flaws in the current infrastructure. 

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment. Mr. Chalopin is Chairman of Deltec International Group, www.deltecbank.com.

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business. Mr. Trehan is a Senior VP at Deltec International Group, www.deltecbank.com.

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

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