Jean Chalopin - Trusting Disruption by Deltec

Blockchain and Supply Chain Management

Posted on March 22, 2023March 22, 2023 by Jean Chalopin

One industry for which blockchain tech has been particularly beneficial is the management of global supply chains. With more connected devices, this will become even more prevalent. This article will introduce the basics of supply chain management, and then explain how blockchain technologies aid in its optimisation.

Supply Chain Management

The goal of all supply chain management is to streamline a company’s supply-side operations, from the planning to its after-sales services, to reduce costs and enhance overall customer satisfaction.

Supply chain management, or SCM, is the control of the complete production flow, beginning with raw materials and ending with the final product or service at the destination. SCM also handles material movement, information storage and movements, and finances associated with the goods and services.

While supply chain and logistics can be confused, logistics is only one part of the complete supply chain. Supply chain management traditionally involves the steps of planning, sourcing, production, delivery, and post-sale service for the central control of the supply chain.

That said, the SCM process begins with selecting suppliers that source raw materials that will eventually be used to meet the needs of customers. Next, a decision of, if the manufacturer will deliver themselves or outsource these tasks. And once delivered, the seller must decide if and what after-sales services will be provided, such as return and repair processing, which may or may not be needed to ensure customer satisfaction.

Modern SCM systems have software management helping to decide everything from goods creation, inventory management, warehousing, order fulfilment, product and service delivery, information tracking, and after-sales services.

Amazon, for example, uses numerous automated and robotic technologies to store goods in the warehouse as well as pick and pack orders for shipment. They are now beginning to use drones to deliver packages weighing less than five pounds in selected test regions.

Supply Chain Evolution

The digital supply network is beginning to combine new technologies, like artificial intelligence (AI), blockchain, and robotics, into the supply chain, adding additional information from several sources to deliver valuable data about goods and services along the supply chain.

The supply chain starts with a strictly physical and functional system but then links to a vast network of data, assets, and activities. By using AI algorithms, businesses are now extracting insights from massive datasets to manage their inventory proactively, automate warehouses, optimise critical sourcing connections, reduce delivery times, and develop customer experiences that will increase satisfaction.

Additionally, AI-controlled robots can help automate manual tasks such as picking and packing orders, delivering raw materials and manufactured goods, moving items during distribution, and scanning boxed items.

Amazon claims that by using its robots, it can hold 40% more inventory, which allows it to fulfil its on-time Prime shipping commitments.

Blockchain’s Impact on Supply Chain Management

Blockchain-based supply chains differ from traditional supply chains, and they can automatically update the transaction data when a change occurs. This attribute enhances traceability along all parts of the supply chain network.

Blockchain-based supply chain networks excel with private-permissioned blockchains carrying limited actors rather than public, open blockchains that are better suited for financial applications.

There are four key actors in blockchain-based supply networks:

1. Standard organisations. These develop blockchain rules and the technical standards, such as Fairtrade, to create environmentally friendly supply chains.

2. Certifiers. These certify individuals for their involvement in supply chain networks.

3. Registrars. These provide network actors with their distinct identities.

4. Actors. These are producers, sellers, and buyers that participate on the blockchain that are certified by a registered auditor or certifier in order to maintain the system’s credibility.

*Key actors in a blockchain-based supply chain courtesy of* *Cointelegraph*

Ownership of a product and its transfer by a blockchain actor is a fascinating feature of the structure and flow of a blockchain-based supply chain. But we must ask if blockchain-based supply chain management makes the system more transparent?

As the related parties are required to fulfil the conditions of smart contracts and then validate them before transfers or exchanges are complete, ledgers are updated with all the transaction information after the participants have completed their duties and processes. This system means that there is a persistent layer of transparency in any one blockchain-based supply chain.

Further, the chain can specify the nature, quality, quantity, location, product dimensions, and ownership of the goods transparently. This results in a customer having a view of the continuous chain of custody, potentially from raw materials to final sale.

Blockchain-Based Traceability

When referring to supply chains, traceability is the capacity to pinpoint previous and current inventory locations and a record of product custody. Traceability involves tracking products while they move through a convoluted process, from raw material sourcing to merchants and customers, often passing through several geographic zones.

Traceability is a significant benefit of blockchain-driven supply chain innovation as a blockchain consists of a decentralised open-source ledger recording data. This ledger is replicable among users, and transactions happen in real-time.

The result is a blockchain-built supply chain that is smarter and more secure because it means that products can be tracked through a robust audit trail. Concerned parties can access the origin, price, date, quantity, destination, certification, and additional data using a blockchain.

By connecting supply chain networks through a decentralised system, blockchain has the potential to enable frictionless movement between suppliers and manufacturers.

*Benefits of blockchain-based traceability, courtesy of* *Cointelegraph*

Producers and distributors can record information such as the product origin, quality, purity, and nutritional value securely using the collaborative blockchain network. Additionally, having access to the product history gives buyers further assurance that the items purchased are from reputable producers, making the supply chain more sustainable.

Finally, if any health concerns or non-compliance with safety standards issues are discovered, the needed action can be taken against the manufacturer, aided by the information stored on the blockchain’s ledger.

Tradeability

Blockchain technology in SCM has a unique advantage over traditional supply chains, which is tradeability. Blockchain platforms can ensure tradeability by using tokenized assets. Blockchain tokenization converts a tangible asset, digital asset, product, or even a service, into a token on the blockchain. A token is a thing that digitally represents ownership of that single product that it tracks, and the token can be exchanged in that market.

Blockchain participants can transfer ownership of these tokens without needing to exchange the physical assets because they are tradeable. Additionally, automated smart contract payments can help identify ownership of licence software, services, and products accurately and immutably on the blockchain.

Ownership consensus is provided via blockchain participants. There is no disagreement over transactions on the chain by design. Every entity on the chain uses the same ledger version. There are no disagreements possible, the ledger is the rule of law.

Companies prefer the tokenisation of assets over direct payments in fiat currency because smart contracts enable peer-to-peer payments, which are generally faster and more cost-effective than traditional currency transfers. Also, token payments prevent fraudsters from using chargeback situations and stealing from companies.

Closing Thoughts

The demand for blockchain-based supply chains is related to the need for information demanded by the supply chain’s participants, as is the case for the production of goods using ethical standards. Blockchain tech in supply chain management can address concerns that traditional supply chains cannot manage, or require the preparation of burdensome paperwork or certifications.

Additionally, a decentralised, immutable record of organisations and transactions combined with the digitisation of physical assets makes it possible to track products all along the supply chain from source to manufacturing, and then to delivery to the final consumer.

Like all things blockchain and crypto, blockchain-based supply chains have yet to reach mainstream adoption. Because blockchain technology remains in its infancy stage, it is governed by different laws for each nation, affecting the supply networks.

Despite these barriers, we expect blockchain-based solutions to replace conventional supply chain networks. Large companies have shareholders that demand sustainability and ethical sourcing information, as well as cost savings. The benefits of blockchains will push businesses toward their use for supply chains, and they will likely become the more common management solution.

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.

Artificial Intelligence and Biomedicine

Posted on March 17, 2023 by Jean Chalopin

Two unlikely interweaving sciences, artificial intelligence and biomedicine, have changed our health and lives. These two sciences have now intertwined further, aiding scientists, medical professionals, and, ultimately, all of us to improve our ongoing health so we can live better lives. This article will introduce some of the ways these two sciences are working together to solve medical mysteries and problems that have plagued us for generations.

Combining With Artificial Intelligence

The field of biomedical sciences is quite broad, dealing with several disciplines of scientific and medical research, including genetics, epidemiology, virology, and biochemistry. It also incorporates scientific disciplines whose fundamental aspects are the biology of health and diseases.

In addition, biomedical sciences also aim at relevant sciences that include but are not limited to cell biology and biochemistry, molecular and microbiology, immunology, anatomy, bioinformatics, statistics, and mathematics. Because of this wide breadth of areas that biomedical sciences touches, the research, academic, and economic significance it spans are broader than that of hospital laboratory science alone.

Artificial intelligence, applied to biomedical science, uses software and algorithms with complex structures designed to mirror human intelligence to analyse medical data. Specifically, artificial intelligence provides the capability of computer-trained algorithms to estimate results without the need for direct human interactions.

Some critical applications of AI to biomedical science are clinical text mining, retrieval of patient-centric information, biomedical text evaluation, assisting with diagnosis, clinical event forecasting, precision medicine, data-driven prognosis, and human computation.

Medical Decision Making

The Massachusetts Institute of Technology has developed an AI model that can automate the critical step of medical decision-making. This process is generally a task for experts to identify essential features found in massive datasets by hand.

The MIT project automatically identified the voicing patterns of patients with vocal cord nodules (see graphic below). These features were used to predict which patients had or did not have the nodule disorder.

Vocal nodules may not seem like a critical medical condition to identify. However, the field of predictive analytics has increasing promise, allowing clinicians to diagnose and treat patients. For example, AI models can be trained to find patterns in patient data. AI has been utilised in sepsis care, in the design of safer chemotherapy regimens, to predict a patient’s risk of dying in the ICU or having breast cancer, among many others.

Optoacoustic Imaging

At the University of Zurich, academics use artificial intelligence to create biomedical imaging using machine learning methods that improve optoacoustic imaging. This technique can study brain activity, visualise blood vessels, characterise skin lesions, and diagnose cancer.

The quality of the images rendered depends on the number of sensors used by the apparatus and their distribution. This novel technique developed by Swiss scientists allows for a noteworthy reduction in the number of sensors needed without reducing the image quality. This allows for a reduction in the costs of the device and increases the imaging speed allowing for improved diagnosis.

To accomplish this, researchers started with a self-developed top-of-the-end optoacoustic scanner with 512 sensors, which produced the highest-quality images. Next, they discarded most of the sensors, leaving between 32 and 128 sensors.

This had a detrimental effect on the resulting image quality. Due to insufficient data, different distortions appeared on the images. However, a previously trained neural network was able to correct for these distortions and could produce images closer in quality to the measurements obtained with the 512-sensor device. The scientists stated that other data sources could be used and enhanced similarly.

Using AI to Detect Cancerous Tumours

Scientists at the University of Central Florida’s Computer Vision Center designed and trained a computer how to detect tiny particles of lung cancer seen on CT scans. These were so small that radiologists were unable to identify them accurately. The AI system could identify 95% of the microtumors, while the radiologists could only identify 65% with their eyes.

This AI approach for tumour identification is similar to algorithms used in facial recognition software. It will scan thousands of faces, looking for a matching pattern. The University group was provided with more than 1000 CT scans supplied by the National Institutes of Health with the Mayo Clinic collaboration.

The software designed to identify cancer tumours used machine learning to ignore benign tissues, nerves, and other masses encountered in the CT scans while analysing the lung tissue.

AI-Driven Plastic Surgery

With an always-increasing supply of electronic data being collected in the healthcare space, scientists realise new uses for the subfield of AI. Machine learning can improve medical care and patient outcomes. The analysis made by machine learning algorithms has contributed to advancements in plastic surgery.

Machine learning algorithms have been applied to historical data to evolve algorithms for increased knowledge acquisition. IBM’s Watson Health cognitive computing system has been working on healthcare applications related to plastic surgery. The IBM researchers designated five areas where machine learning could improve surgical efficiency and clinical outcomes:

Aesthetic surgery
Burn surgery
Craniofacial surgery
Hand and Peripheral Surgeries
Microsurgery

The IBM researchers also expect a practical application of machine learning to improve surgical training. The IBM team is concentrating on measures that ensure surgeries are safe and their results have clinical relevance–while always remembering that computer-generated algorithms cannot yet replace the trained human eye.

The researchers also stated that the tools could not only aid in decision making, but they may also find patterns that could be more evident in minor data set analysis or anecdotal experience.

Dementia Diagnoses

Machine learning has identified one of the common causes of dementia and stroke in the most widely used brain scan (CT) with more accuracy than current methods. This is small vessel disease (SVD), a common cause of stroke and dementia. Experts at the University of Edinburgh and Imperial College London have developed advanced AI software to detect and measure small vessel disease severity.

Testing showed that the software had an 85% accuracy in predicting the severity of SVD. As a result, the scientists assert that their technology can help physicians carry out the most beneficial treatment plans for patients, swiftly aiding emergency settings and predicting a patient’s likelihood of developing dementia.

Closing Thoughts

AI has helped humans in many facets of life, and now it is becoming an aid to doctors, helping them identify ailments sooner and determine the best pathways to tackle diseases. AI performs best with larger data sets, and as the volume of data increases, the effectiveness of AI models will continue to improve.

The current generation of machine models uses specific images and data to solve defined problems. More abstract use of big data will be possible in the future, meaning that more extensive data sets of disorganised data will be combined, and high-quality computers (potentially quantum computers) will be allowed to make new inferences from those data sets.

For example, when multiple tests like blood pressure, pulse-ox, EKG, bloodwork, and other tests, including CT and MRI scans, are all combined, the models may see things that doctors did not piece together. This is when machine learning will take medicine to the next level, providing even more helpful information to doctors to help us live longer and healthier lives.

Utilising Quantum Entanglement

Posted on March 14, 2023 by Jean Chalopin

Quantum entanglement is a phenomenon whereby two or more quantum systems become connected so that the state of one system can affect the state of the other(s), even when separated by large distances. Classical physics does not explain this connection, or “correlation,” between the systems.

It has been a subject of intense study and debate since it was first proposed by Albert Einstein, Boris Podolsky, and Nathan Rosen in 1935. Quantum entanglement is one of the fundamental properties behind quantum computing, and its potential impact on finance, business, and society is excellent.

We will discuss the history of this scientific field, ongoing research, how entanglement relates to the exciting field of quantum computing, and how these technologies may solve some essential and potentially fundamental questions as we advance.

What Is Quantum Entanglement?

The concept of quantum entanglement was born in the 20th century during the atomic age. One of the first and most famous examples of quantum entanglement is the Einstein-Podolsky-Rosen (EPR) paradox.

In this thought experiment, two particles are created at the same point in space and separated considerably. The spin state of one particle is measured, and it is found to be ‘up’. According to classical physics, the other particle’s spin state should also be ‘up’ or ‘down’ with a 50-50 chance.

*This graphic, courtesy of* ***NASA/JPL-Caltech***, is intended to explain “entangled particles.” Alice and Bob represent photon detectors, which were developed by the Jet Propulsion Laboratory (under NASA) and the National Institute of Standards and Technology.

However, in quantum mechanics, the state of the second particle is instantaneously affected by the measurement of the first particle, meaning that its spin state is also ‘up’. This concept is known as ‘spooky action at a distance’ and was considered by Albert Einstein to be a flaw in quantum mechanics.

This graphic, courtesy of NASA/JPL-Caltech, is intended to explain ‘entangled particles’. Alice and Bob represent photon detectors developed by the Jet Propulsion Laboratory (under NASA) and the National Institute of Standards and Technology.

Back in 1964

The phenomenon of quantum entanglement was first experimentally demonstrated by physicist John Bell in 1964. Bell proposed an inequality, now known as Bell’s inequality, which stated that certain measurements of entangled particles would always produce specific results if classical physics were correct.

However, experiments have repeatedly shown that the results of these measurements violate Bell’s inequality, proving the existence of quantum entanglement.

One of the most critical implications of quantum entanglement is the concept of quantum teleportation. Quantum teleportation is the process of transferring the state of a quantum system from one location to another without physically moving the system. For example, in 1993, physicist Charles Bennett and his team successfully teleported a photon’s state over a few metres.

Since then, scientists have been able to teleport the state of atoms, ions, and even larger objects over increasingly larger distances, both in and outside the laboratory. Quantum entanglement has also been used to create highly secure forms of encryption.

For example, in a quantum key distribution (QKD) process, two parties can communicate securely by sharing a secret key encoded in entangled particles. Furthermore, because any attempt to measure the state of these particles will alter it, any third party attempting to intercept the communication can be detected. We’ll discuss the implications of QKD a bit more in a subsequent section.

The Potential of Quantum Entanglement

Several research areas are looking at the potential uses of quantum entanglement for practical applications. One possible application of quantum entanglement is in the field of quantum sensing.

Quantum sensors use entanglement properties to measure physical phenomena with unprecedented accuracy. For example, quantum sensors can measure temperature, pressure, and acceleration more precisely than classical sensors. Quantum sensors can also detect faint signals, such as gravitational waves, that are otherwise difficult to detect.

Quantum entanglement is also being researched as a possible technology for quantum communication. A quantum communication network would use entangled particles to transmit information, making the communication highly secure and resistant to eavesdropping.

In medicine, quantum entanglement is being researched to develop quantum-based diagnostic tools.

For example, in a study published in the journal Nature Communications, researchers from China proposed a new method to detect cancer cells using entangled photons; this technique is highly sensitive, non-invasive, and could be used for early cancer diagnosis.

Quantum Entanglement and Quantum Computing

Quantum entanglement and quantum computing are closely related. Quantum computing relies on the properties of quantum mechanics, including superposition and entanglement, to perform certain types of calculations much faster than their classical computer counterparts. In a quantum computer, information is stored in qubits, which exist in superposition and entanglement. Using its qubits, a quantum computer can solve specific complex mathematical problems, such as factoring large numbers, that would take a classical computer an impractical amount of time.

Quantum communication (introduced above) is also being incorporated into quantum computing. Quantum communication allows qubits to be shared between different locations, which is necessary for the distributed nature of quantum computing. This enables quantum computing to be performed on a large scale, with many qubits distributed across different locations, allowing for more powerful quantum algorithms.

Possible Uses of Quantum Entanglement

Quantum entanglement has the potential to be used in several ways, in finance, business, healthcare, and more.

Quantum cryptography. Quantum key distribution (QKD) allows two parties to communicate securely by sharing a secret key encoded in entangled particles. QKD could be used to protect sensitive financial transactions such as online banking or stock trading. On the other hand, quantum computers could also break many current encryption algorithms to protect sensitive information. This code-breaking could have significant implications for online transactions, medical records, and communications security.
Quantum computing. Quantum computers could be used to solve complex optimization problems in finance, such as portfolio optimisation or risk management.
Quantum machine learning. Quantum machine learning (QML) is a field that combines the power of quantum computing with machine learning algorithms. QML could be used to analyze large sets of financial data, such as stock market trends, and make more accurate predictions, which could have applications in fields beyond finance, such as healthcare and transportation.
Quantum internet. The idea of a quantum internet is based on using quantum entanglement to transmit information in a highly secure way. This could be used to create a new kind of internet that would be highly resistant to hacking, which could be important for financial institutions that must protect sensitive information.
Quantum random number generation. Quantum entanglement can generate truly random numbers, which could be used to generate secure encryption keys for financial transactions and encode sensitive information.
Drug discovery. Quantum computers could be used to simulate the behaviour of molecules. This ability could accelerate the drug discovery process and make it more efficient, produce better health outcomes for many patients, and prevent the growing problem of antibiotic resistance.
Optimization problems. Quantum computers could solve specific optimization problems faster than classical computers. Such optimization could have applications in logistics, finance, and energy management.
Quantum simulation. Quantum computers could simulate the behaviour of quantum systems with high accuracy. This simulation could study the properties of materials, predict the behaviour of complex systems, and understand the properties of fundamental particles such as quarks and gluons.
Quantum chemistry. Quantum computers can be used to simulate the behaviour of chemical compounds and predict their properties, which could help speed up the discovery of new materials, catalysts, and drugs.

It’s important to note that these are only potential applications, but all show great promise, and most are currently in the research and development stages. It will likely take some time before they become practical technologies. However, many overlap, and if one were to be solved, a host of other applications would soon follow.

Closing Thoughts

Quantum entanglement is a mysterious and fascinating phenomenon. It has already been used to create highly secure forms of encryption and to teleport the state of quantum systems over large distances. The study of quantum entanglement continues to be an active area of research, with scientists working to uncover its true nature, properties, and potential uses.

Despite its many potential applications, the true nature of quantum entanglement still needs to be fully understood. Some theories propose that entanglement is a fundamental aspect of the universe, while others suggest that it results from more complex interactions between particles.

Whatever the truth behind quantum entanglement, scientists will continue to research how it and the quantum computers built on its principles can solve some of the most challenging problems and questions we are currently asking. Suppose it does live up to the hype. In that case, quantum entanglement could prompt a new technological age and fundamentally alter our understanding of physics.

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

Blockchain, AI, and IoT

Posted on March 8, 2023March 8, 2023 by Jean Chalopin

Artificial intelligence (AI), the Internet of Things (IoT), and blockchain are the most promising and rapidly evolving technologies of our time.

Combined, these three technologies solve many problems across many different industries, including supply chain management, finance, healthcare, and manufacturing. We will explore how this combination can change many aspects of our lives.

Supply Chain Management

One potential use case for combining AI, IoT, and blockchain is tracking and managing goods moving through a supply chain. By using IoT sensors to gather data on the location and condition of goods and blockchain to create a transparent and immutable record of that data, it is possible to create a real-time, end-to-end view of the supply chain.

This system can help to improve efficiency, reduce the risk of fraud, and increase transparency for all parties involved.

Another potential use case for combining these technologies is optimising logistics and transportation. By using AI to analyse data from IoT sensors and make predictions about demand, shipping routes, and other factors, logistics companies can make more informed decisions about how to move goods more efficiently. Being immutable, blockchain can also create a tamper-proof record of shipping data, which can help improve transparency and reduce the risk of fraud.

Additionally, these technologies can be combined in smart contracts, which can automate and streamline supply chain transactions by using AI to identify and execute contract terms and blockchain to ensure that the contract terms are executed transparently and securely.

Financial Services

In financial services, the first potential use case for the combination of AI, IoT, and blockchain is in the field of fraud detection and prevention.

By using IoT sensors to gather data on financial transactions, and blockchain to create an immutable and transparent record of that data, it is then possible to use AI algorithms to identify patterns and anomalies that indicate fraudulent activity. This combination helps financial institutions detect and prevent fraud more quickly and effectively, reducing costs for the company and the client.

Another potential use case for the combination of these technologies is risk management. By using AI to analyse data from IoT sensors and other sources, financial institutions can gain a more comprehensive view of the risks they are exposed to and make more informed decisions about managing those risks.

Finally, like with the supply chain, these technologies can be combined in intelligent contracts. Financial institutions can automate and simplify the contract execution process, reducing the need for manual intervention and increasing efficiency. The cost-benefit of such a solution could be significant by preventing human error, creating a trustless environment, and providing nearly minute-by-minute updates.

Healthcare

Combining AI, IoT, and blockchain technologies can also significantly impact the healthcare industry.

One potential use case for combining AI, IoT, and blockchain in healthcare is the management of electronic medical records (EMRs). Using IoT sensors to collect and transmit data to the blockchain makes it possible to create a secure and tamper-proof patient data record. AI algorithms can then be used to analyse this data and identify patterns that can help improve patient care on the individual level and speed up the discovery of new treatments for all.

Another potential use case is in the field of personalised medicine. Personalised healthcare is a new concept that could turn the medical world on its head. For example, the way cancer drugs are currently tested, a group of patients with a particular type of cancer is given a drug, and its effectiveness for the overall group is determined. A patient’s cancer cell DNA would be tested with personalised medicine, and a cocktail of drugs effective at treating cancer that fit that genetic profile could be prescribed.

Using IoT-enabled devices to collect data on a patient’s health, combined with blockchain to create a secure and transparent record of that data, AI can analyse the data and make personalised treatment recommendations. This can help doctors provide more individualised care to patients, leading to better health outcomes.

Additionally, blockchain tech can create secure and transparent medical supply chains, allowing for the tracking and traceability of medical products and devices from manufacturer to patient. While all supply chains are essential, ensuring that patients receive safe and effective treatments that have been shipped adhering to required standards and reducing the risk of counterfeit drugs and medical devices will save lives.

Manufacturing

Combining AI, IoT, and blockchain technologies can significantly impact the manufacturing industry. By leveraging these technologies, manufacturers can create more efficient and cost-effective operations and improve the overall quality of their products. In addition, these technologies can provide significant benefits by improving the manufacturing process’s efficiency, transparency, and security.

One potential use case for combining these technologies in manufacturing is in the field of predictive maintenance. By using IoT sensors to collect data on the performance of manufacturing equipment, AI algorithms can then analyse massive amounts of data and predict when equipment is likely to fail.

This system can help manufacturers schedule maintenance timely and cost-effectively, reducing downtime and increasing overall efficiency. Such information is already being applied to advanced systems such as aeroplanes, blurring the lines between manufacturing and services.

Additionally, blockchain tech can create secure and transparent traceability systems for products, from raw materials sourcing, production, and logistics to product traceability and warranty management. This can help to ensure that products are safe and of high quality and can help to protect a company’s reputation and brand.

With the increasing significance of environmental, social, and governance (ESG) issues, manufacturers and the consumers of their goods care more about the sustainable practices of companies. A clear and transparent trail that can be followed on an immutable blockchain will give confidence to those who value ESG issues.

Ongoing Concerns

As organisations look to implement AI, IoT, and blockchain technologies, it is crucial that they also consider the potential risks and challenges associated with these technologies. One of the essential considerations is data privacy and security.

Collecting and storing large amounts of data through IoT sensors and blockchain technology can present significant privacy and security risks. Personal information, including health and financial data and other compassionate information, can be vulnerable to breaches, hacking, and cyber-attacks. Organisations must take the necessary steps to protect this data, such as implementing robust security protocols, encrypting data, and regularly monitoring potential threats.

A study by PwC highlights that the growing use of IoT in healthcare has raised privacy concerns among patients and healthcare providers and regulatory challenges for organisations that handle patient data. Furthermore, another study by Deloitte states that blockchain technology can be used to implement robust security protocols and data encryption, as well as data sharing and access controls, which can help to mitigate these risks. The correct balance of these technologies will be needed.

Another vital consideration is regulatory compliance. The use of these technologies is subject to a range of laws and regulations, including data protection and privacy laws, financial regulations, and healthcare laws. Organisations must comply with all relevant regulations and have the processes and procedures to meet regulatory requirements.

A report by the World Economic Forum highlights that regulations and standards are needed to ensure the safe and responsible use of these technologies while also enabling innovation and growth.

To address these concerns, organisations should work with data privacy and security experts and legal and regulatory compliance experts to develop a comprehensive strategy for technology implementation. This strategy should include a thorough analysis of the potential risks and benefits of the technologies and a plan for mitigating those risks. Additionally, organisations should be prepared to invest in the necessary infrastructure and resources to ensure the security and privacy of their data.

Closing Thoughts

Combining AI, IoT, and blockchain tech significantly benefits various industries. For example, in financial services, they can be used to improve fraud detection and prevention, risk management, and brilliant contract execution. In healthcare, they can be combined to manage electronic medical records, improve personalised medicine, and secure medical supply chains. Finally, in manufacturing, they can be used for predictive maintenance, supply chain management, and product traceability.

Each use case demonstrates how combining these technologies can improve transparency, security, and efficiency in different industries. By leveraging the power of AI, IoT, and blockchain, organisations can gain a more comprehensive view of their operations and make more informed decisions, leading to better outcomes for their customers and an improved bottom line.

These systems are now being considered even more significantly, with proposed smart cities taking advantage of them for optimised infrastructure. Furthermore, it is easy to imagine using the data created and analysed by these technologies to be further combined for other uses, some of which may still be unseen.

It is important to note that while these technologies have the potential to bring significant benefits, there are also challenges to be addressed. For example, ensuring data privacy and security and addressing regulatory concerns are significant challenges that need to be addressed. Nevertheless, with the right approach and partners, organisations can successfully implement these technologies and reap the benefits they can offer.

Combining these three new technologies represents a significant opportunity for organisations across various industries. As their use in transparency, security, and efficiency expands beyond business sectors, they will begin to help society and the earth.

The Impact of AI on Insurance in 2023

Posted on February 28, 2023February 28, 2023 by Jean Chalopin

The impact of artificial intelligence (AI) in the insurance industry has been significant in the past five years. According to Accenture, the adoption of AI in the insurance sector is expected to double in the next three years. It could lead to cost savings of up to $1.2 billion annually.

Another study by PwC found that 63% of insurance companies have already implemented or are planning to implement AI in their business operations. As a result, AI has allowed insurance companies to improve customer experience, reduce costs, and streamline underwriting processes, leading to a more efficient and profitable industry.

What Is AI in Insurance?

Artificial intelligence (AI) in insurance refers to using advanced technology, such as machine learning, natural language processing, and computer vision, to automate and optimise various functions in the insurance industry.

This includes underwriting, claims processing, fraud detection, and customer service. By leveraging AI, insurance companies can analyse vast amounts of data, make predictions, and provide personalised services to customers in real-time. The integration of AI has the potential to transform the insurance industry by improving efficiency, reducing costs, and enhancing the customer experience.

Why Does Insurance Need AI?

There are several reasons why the insurance industry needs AI in 2023 and beyond:

Increased efficiency and cost savings: AI can automate manual processes and help insurers analyse large amounts of data quickly and accurately, leading to faster decision-making and cost savings.
Improved customer experience: AI can provide personalised recommendations and real-time support to customers, helping insurance companies meet their evolving needs and preferences.
Enhanced risk assessment: AI can analyse historical data and other relevant information to identify and assess risk factors, helping insurers make more informed decisions.
Fraud detection: AI can help insurers detect fraudulent activities by analysing patterns and anomalies in data, reducing the risk of financial losses.
Better decision-making: AI can provide actionable insights to insurance companies, helping them make more informed decisions and improve their operations.

Given AI’s numerous benefits, it is likely that the insurance industry will continue to adopt and integrate this technology in the coming years. This article will examine some of these areas in more detail.

The Challenge for Legacy Insurers

Legacy insurers must invest in AI to keep up with fintech start-ups because these start-ups often have a technology-first approach and can offer innovative, personalised services to customers in real time. AI can help legacy insurers automate manual processes, provide better customer experiences, and make more informed decisions, allowing them to compete with fintech start-ups and remain relevant in the market.

An example of a legacy insurer investing in AI is AXA, one of the world’s leading insurance companies. AXA has integrated AI into its operations, using machine learning to automate manual processes, improve risk assessment, and provide personalised recommendations to customers.

Another example is Allianz, which has invested in AI to enhance its underwriting processes and improve its efficiency. These companies recognise the importance of AI in staying competitive and relevant in the market and are taking steps to integrate this technology into their operations.

The Rise of Gen Z

Insurance companies need to invest in AI to keep up with the demands of Gen Z tech-savvy buyers who demand fast, convenient, and personalised experiences. AI can help insurance companies automate manual processes, provide real-time support, and deliver personalised recommendations, meeting the demands of this demographic.

Insurance companies use AI to analyse social media data and understand customer preferences and behaviours to meet Gen Z’s demands. For example, an insurance company might use AI to analyse customer interactions on social media platforms, such as Facebook or Instagram, to determine which products and services are most relevant to them.

One insurance company that is already using social media to its advantage for Gen Z is Lemonade. The company has built a chatbot that uses natural language processing to handle customer inquiries and uses AI algorithms to process claims quickly.

Using AI to understand customer behaviour and preferences, Lemonade can provide a personalised experience that appeals to Gen Z buyers. This demonstrates how insurance companies can use AI to keep up with the demands of this demographic and remain competitive in a rapidly evolving market.

AI for Efficiency and Cost Savings

Insurance companies use AI to improve efficiency and reduce costs by automating manual processes and making more informed decisions. AI algorithms can quickly analyse large amounts of data, identify patterns, and make predictions, allowing insurance companies to make more informed decisions and reduce the time and resources required to complete tasks.

For example, some insurance companies use AI to automate the underwriting process, reducing the time and resources required to assess risk and provide quotes. Others use AI to automate claims processing, reducing the time required to process claims and improving the overall customer experience.

MetLife is one insurance company already utilising AI to increase efficiency and save costs. AI has been incorporated into the company’s operations, with algorithms used to automate procedures, enhance risk assessment, and deliver tailored suggestions to consumers. Using AI, MetLife can improve customer service, save operating expenses, and increase operational efficiency.

Improving Customer Service

Insurance companies use AI to improve the customer experience by providing more personalised services and real-time support. For example, AI algorithms can analyse customer data and preferences to provide tailored recommendations, and chatbots powered by natural language processing can provide instant customer support. These technologies allow insurance companies to provide a faster, more convenient, and more personalised experience, meeting the demands of modern customers.

One insurance company already using AI to improve customer experience is Oscar Health. The company uses AI to personalise the customer experience, from identifying and addressing potential health issues to providing care recommendations. Oscar Health uses machine learning algorithms to analyse customer data, such as claims and health records, to identify potential health issues and provide personalised recommendations to customers.

By using AI to provide a more personalised experience, Oscar Health can meet its customers’ demands and provide a level of service that sets it apart from other insurance providers. This demonstrates how insurance companies can use AI to improve the customer experience and remain competitive in a rapidly evolving market.

Enhancing Risk Assessments

Insurance companies use AI to enhance risk assessment by providing more accurate and reliable data analysis. AI algorithms can quickly analyse large amounts of data, identify patterns, and make predictions, allowing insurance companies to make more informed decisions and better assess risk. This helps insurance companies reduce fraud risk and underwrite policies more effectively, improving the overall customer experience.

One insurance company already using AI to enhance risk assessment is Allstate. The company uses AI algorithms to analyse customer data, such as driving patterns and vehicle usage, to assess risk and provide personalised insurance coverage. Allstate’s AI system can quickly process large amounts of data and identify patterns that might indicate increased risk, allowing the company to make more informed decisions and better assess risk.

Using AI to enhance risk assessment, Allstate can provide better customer service and remain competitive in a rapidly evolving market. This demonstrates how insurance companies can use AI to drive operational efficiency, reduce costs, and improve customer experience.

Using AI to Detect Fraud

Insurance companies use AI to detect fraud by analysing large amounts of data to identify suspicious patterns and anomalies. AI algorithms can quickly process data, identify red flags, and trigger investigations, helping insurance companies prevent fraud more effectively.

One insurance company that is using AI to detect fraud is Anthem. The company uses AI algorithms to analyse customer data, such as claims and payment history, to identify suspicious patterns and trigger investigations. Anthem’s AI system can quickly process large amounts of data and identify red flags, such as unusual billing patterns or repeated claims from the same provider, allowing the company to detect fraud more effectively.

Using AI to detect fraud, Anthem can reduce the risk of financial losses and improve the overall customer experience. This demonstrates how insurance companies can use AI to enhance security, reduce costs, and remain competitive.

Creating Actionable Insights

Insurance companies use AI to create actionable insights by analysing large amounts of data to identify patterns, make predictions, and inform business decisions. AI algorithms can quickly process data, identify trends, and provide real-time insights, allowing insurance companies to make data-driven decisions and improve the overall customer experience.

Some examples of insurance companies using AI to create actionable insights include Allstate, Metromile, and Lemonade.

Allstate uses AI to assess risk and provide personalised insurance coverage by analysing customer data, such as driving patterns and vehicle usage. Metromile uses AI to analyse telematics data from connected vehicles to provide real-time insights and inform pricing and underwriting decisions. Finally, lemonade uses AI to automate the insurance process, making it faster and more efficient to create actionable insights that drive business decisions and improve the overall customer experience.

Closing Thoughts

AI is having a significant impact on the insurance industry, transforming the way that insurance companies operate and interact with customers. AI is helping insurance companies to improve efficiency, reduce costs, enhance risk assessment, detect fraud, and create actionable insights.

In the next ten years, the use of AI will likely continue to grow, leading to more advanced and sophisticated applications of AI in areas such as underwriting, claims to process, and customer service.

As AI becomes more prevalent in the insurance industry, we will likely see a shift towards more data-driven, personalised, and automated insurance services that deliver improved customer outcomes and increased efficiency for insurers. With the continued growth of AI in insurance, the industry will continue to evolve and adapt to meet the changing needs of customers and remain competitive in a rapidly changing market.

What Is a Global Citizen?

Posted on February 24, 2023February 24, 2023 by Jean Chalopin

The term ‘Global Citizen’ has earned increasing attention in recent years as people worldwide become more aware of our interconnectedness and our actions’ impact on one another.

At its core, being a Global Citizen means recognising that we are all part of a larger global community and that we are responsible for taking actions that promote the well-being of people and the planet, both locally and globally. This includes valuing diversity, respecting human rights, promoting peace and justice, and working to address global challenges such as poverty, inequality, climate change, and other issues that affect people across borders.

Global Citizenship is not limited to nationality, religion, or culture. It’s a mindset and a way of living that emphasises our common humanity and our shared responsibility for the world we live in. It involves actively engaging with others, learning about different perspectives and cultures, and taking action to address social, economic, and environmental issues in our own communities and beyond.

A Global Citizen has the following key traits:

Awareness: Having a global perspective and understanding of how our actions impact others in different parts of the world.
Empathy: Showing empathy for those who are different from us and being willing to learn from their experiences.
Action: Taking action to address issues that affect people and the planet, such as volunteering, donating to charities, and supporting policies that promote social and environmental justice.
Collaboration: Working with others across different backgrounds, cultures, and sectors to find solutions to complex global challenges.

Being a Global Citizen is about recognising that our actions have consequences that extend past our immediate surroundings. It’s to take responsibility for promoting a more just, equitable, and sustainable world for all people, regardless of where they were born, where they live, and how they identify.

This article delves into these traits shared by Global Citizens worldwide, what they care about today, and examples of what they’ve done with their success.

A Global Citizen Is Aware

They maintain constant awareness of the interconnected world–that there are expanded ripple effects to our actions. They understand that there are social, economic and environmental issues that affect people in different parts of the world and that it’s essential to be informed about these issues.

For example, a Global Citizen makes a point to learn different cultures and languages and seeks opportunities in line with their passions. They recognise not only the potential to make a difference but to benefit themselves and add to their life while doing so.

Although renowned late Beatle singer George Harrison passed away two decades ago, he lived ahead of his time and set the definition for a Global Citizen. In 1971, he organised the first benefit concert, The Concert for Bangladesh, which strove to raise awareness and help save the lives of 10 million East Pakistani refugees suffering from disease and starvation.

Five decades on, this concert remains one of Mr. Harrison’s hallmark achievements and added to the permanent legacy of world-changing music.

A Global Citizen Is Empathetic

They waste no time in showing empathy for others, especially those who are different from themselves. They recognise that there are many factors that influence a person’s experiences, such as their culture, gender, sexuality, religion, economic status, and race. A Global Citizen seeks to understand these factors and remain open to learning from others.

For example, a Global Citizen might participate in cultural exchange programs, learn a new language, or volunteer to support people in marginalised communities. They are aware that their own experiences are limited and that they can benefit from understanding the life stories of others.

Wawira Njiru earned the headline’ novice cook to international icon’ for her dedication to transforming Kenyan schoolchildren’s lives through access to food. She launched the group Food for Education which targets explicitly the cycle of poverty and showcases how it can end with a simple, good meal. She has now served over nine million meals.

A Global Citizen Understands Action

They don’t hesitate when the time comes to stop planning, and start doing. A Global Citizen is someone who takes action to address global challenges, both locally and globally. They recognise that there are many issues facing our world, such as poverty, inequality, climate change, and other environmental issues, and they’re willing to take action to address these challenges.

For example, a Global Citizen might volunteer for a local charity, donate to a global relief organisation, or advocate for policies that promote social and environmental justice. They recognise that their own actions can make a difference and that collective action is necessary to create positive change.

Hamdi Ulukaya, founder and CEO of Chobani, champions fully-paid parental leave, local food banks, and trustworthy food programs for local schools. Further, he founded the Tent Partnership for Refugees, which seeks to provide hiring, training, and mentorship for refugees. Most are aware of the world’s crises, but it comes down to extending a helping hand.

A Global Citizen Collaborates

They don’t live on an island, alone, king of a kingdom-of-one. A Global Citizen is someone who works with others to find solutions to complex global challenges. They recognise that no one person, organisation, or nation can solve these challenges alone, and that collaboration across different backgrounds, cultures, and sectors is necessary.

For example, a Global Citizen might participate in community forums, join a global network of activists, or collaborate with organisations to address issues such as poverty, climate change, or social injustice. They recognise that different perspectives and experiences can enrich the solutions we develop, and that working together is necessary to create meaningful change.

Richard Curtis has worked with numerous organisations and projects, such as Comic Relief, Red Nose Day, Projet Everyone, and Make Poverty History. His collaborations have led to the fundraising of over a billion US dollars for the benefit of children and vulnerable people worldwide.

Closing Thoughts

Awareness, empathy, action, and collaboration–in that order. Global Citizens understand they’re on Earth to contribute, to do something, and help make something great. They utilise their unique talent or profession, be that music, business management, or cooking, and make it the bedrock of an enterprise far greater than themselves.

Ironically and because of it, they become much more; memorable individuals we read about in magazines or hear of in podcasts. And it doesn’t take $10,000–not even $1,000. Wawira started with a single Kenyan meal, and later became one of the world’s most remarkable success stories under 30 years old.

By combining a singular passion with a greater focus, entrepreneurs transition from the realm of ‘good’ to ‘great’. It’s this reason that prompted Deltec to launch the Deltec Initiatives Foundation, empowering young Bahamians, and Deltec Cares, a global disaster relief effort.

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.deltec.io.

The co-author of this text, Conor Scott, CFA, has been active in the wealth management industry since 2011. Mr. Scott is a Writer for Deltec International Group, www.deltec.io.

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.

Robotics and AI

Posted on February 20, 2023February 20, 2023 by Jean Chalopin

While it is obvious that artificial intelligence (AI) and robotics are different disciplines, robots can perform without AI. However, robotics reaches the next level when AI enters this mix.

We will explain how these disciplines differ and explore spaces where AI is utilized to create envelope-pushing robotic technology.

Robotics in Brief

Robotics is a subset of engineering and computer science where machines are created to perform tasks without human intervention after programming.

This definition is broad, covering everything from a robot that aids in silicon chip manufacturing to the humanoid robots of science fiction, and are already being designed like the Asimo robot from Honda. In global finance, we’ve had robo-advisors working with us for some years already.

Robots have traditionally been used for tasks that humans are incapable of doing efficiently (moving an assembly line’s heavy parts), are repetitive, or are a combination. For example, robots can accomplish the same task thousands of times a day, whereas a human would be slower, get bored, make more mistakes, or be physically unable to complete it.

Robotics and AI

Sometimes these terms are incorrectly used interchangeably, but AI and robotics are very different. In AI, systems mimic the human mind to learn through training to solve problems and make decisions autonomously without needing specific programming (if A, then B).

As we have stated, robots are machines programmed to conduct particular tasks. Generally, most robotics tasks do not require AI, as they are repetitive and predictable and not needing decision-making.

Robotics and AI can, however, coexist. Robotic projects that use AI are in the minority, but such systems are becoming more common and will enhance robotics as AI systems grow in sophistication.

AI-Driven Robots

Amazon is testing the newest example of a household robot called Astro. It is a self-driving Echo Show. The robot uses AI to navigate a space autonomously, acting as an observer (using microphones and a periscopic camera) when the owner is not present.

This type of robot is not novel; robotic vacuums have been in our homes, navigating around furniture, for almost a decade. But even these devices are becoming “smarter” with improved AI.

The company behind the robot vacuum Roomba, iRobot, announced a new model that uses AI to spot and avoid pet poop.

Robotics and AI in Manufacturing

Robotic AI manufacturing, also known as Industry 4.0, is growing in scope and will become transformational. This fourth industrial revolution may be as simple as a robot navigating its way around a warehouse to systems like that of Vicarious, who designs turnkey robotic solutions to solve tasks too complex for programmed-only automation.

Vicarious is not alone in this service. For example, the Site Monitoring Robot from Scaled Robotics can patrol a construction site, scanning and analyzing the data for potential quality issues. In addition, the Shadow Dexterous Hand is agile enough to pick soft fruit from trees without crushing it while learning from human examples, potentially making it a game changer in the pharmaceutical industry.

Robotics and AI in Business

For any business needing to send things within a four-mile radius, Starship Technologies has delivery robots equipped with sensors, mapping systems, and AI. Their wheeled robot can determine the best routes to take on the fly while avoiding the dangers of its navigating world.

In the food service space, robots are becoming even more impressive. Flippy, the robotic chef from Miso Robotics, uses 3D and thermal vision, learning from the kitchen it’s in, and acquiring new skills over time, skills well beyond the name it earned by learning to flip burgers.

Flippy, the robot chef from Miso Robotics

Robotics and AI in Healthcare

Front-line medical professionals are tired and overworked. Unfortunately, in healthcare, fatigue can lead to fatal consequences.

Robots don’t tire, which makes them a perfect substitute. In addition, Waldo Surgeon robots perform operations with steady “hands” and incredible accuracy.

Robots can be helpful in medicine far beyond a trained surgeon’s duties. More basic lower-skilled work performed by robots will allow medical professionals to free up time and focus on care.

The Moxi robot from Diligent Robotics can do many tasks, from running patient samples to distributing PPE, giving doctors and nurses more of this valuable time. Cobionix developed a needleless vaccination administering robot that does not require human supervision.

Robotics and AI in Agriculture

The use of robotics in agriculture will reduce the effect of persistent labor shortages and worker fatigue in the sector. But there is an additional advantage that robots can bring to agriculture, sustainability.

Iron Ox uses robotics with AI to ensure that every plant gets the optimal level of water, sunshine, and nutrients so they will grow to their fullest potential. When each plant is analyzed using AI, less water and fertilizer are required producing less waste.

The AI will learn from its recorded data improving that farm’s yields with every new harvest.

The Agrobot E Series has 24 robotic arms that it can use to harvest strawberries, and it uses its AI to determine the ripeness of the fruit while doing so.

Robotics and AI in Aerospace

NASA has been working to improve its Mars rovers’ AI while working on a robot to repair satellites.

Other companies are also working on autonomous rovers. Ispace’s rover uses onboard tools, and maybe the device hired to lay the ‘Moon Valley’ colony’s future foundation.

Additional companies and agencies are trying to enhance space exploration with AI-controlled robots. For example, the CIMON from Airbus is like Siri in space. It’s designed to aid astronauts in their day-to-day duties, reducing stress with speech recognition and operating as a system for problem detection.

When to Avoid AI?

The fundamental argument against using AI in robots is that, for most tasks, AI is unnecessary. The tasks that are currently being done by robots are repetitive and predictable; adding AI to them would complicate the process, likely making it less efficient and more costly.

There is a caveat to this. To date, most robotic systems have been designed with AI limits in mind when they were implemented. They were created to do a single programmed task because they could not do anything more complex.

However, with the advances in AI, the lines between AI and robotics are blurring. Outside of business- or healthcare-driven uses, we’ve noticed how AI facilitates the relatively new, lucrative field of algorithmic trading becoming increasingly available to retail investors.

Closing Thoughts

AI and robotics are different but related fields. AI systems mimic the human mind, while robots help complete tasks more efficiently. Robots can include an AI element, but they can exist independently too.

Robots designed to perform simple and repetitive tasks would not benefit from AI. However, many AI-free robotic systems were created, accounting for the limitations of AI at their time of implementation. As the technology improves, these legacy systems may benefit from an AI upgrade, and new systems will be more likely to build an AI component into their design. This change will result in the marrying of the two disciplines.

We have seen how AI and robotics can aid in several different sectors, keeping us safer, wealthier, and healthier while making some jobs easier or performed more efficiently entirely by robots. However, we also consider a possible change in employment structure. People will be outsourced to robots, and they must be accounted for with training and other options for employment.

With the combination of AI and robotics, significant changes are on our horizon. This combination represents the very forefront of innovation.

The Uses of Chatbots Like ChatGPT

Posted on February 17, 2023February 17, 2023 by Jean Chalopin

When we used to hear the word “chatbots,” pain often comes to mind. Frustration with the novel was the norm. With chatbots that were mostly able to receive a question and reply, “I am sorry, I can’t answer that. However, I will contact someone that can help you with it. You should receive a reply in 24 hours.”

Yet, chatbots have come a long way, and the next-generation bots, like the new Chat GPT and those under development by Google, are excellent. They will become a vital part of the customer experience and take the burden of repetitive tasks, simple tasks, and questions away from agents while improving satisfaction results by quickly providing the info clients need.

Chatbots in Brief

Chatbots had evolved since their inception when programmers wanted to surpass the Turing Test and create artificial intelligence. For example, in 1966, the ELZA program fooled users into thinking they were talking to a human.

A chatbot is a computer program often using scripts that can interact with humans in a real-time conversation. The chatbot can respond with canned answers, handle different levels of requests (called second and third-tier issues), and can direct users to live agents for specific tasks.

Chatbots are being used in a wide variety of tasks in several industries. Mainly in customer service applications, routing calls, and gathering information. But other business areas are starting to use them to qualify leads and focus large sales pipelines.

The first chatbots of over 50 years ago were intended to show the possibilities of AI. In 1988 Rollo Carpenter’s Jaberwacky was designed more for entertainment but could learn new responses instead of relying on canned dialog only. As they progressed, chatbots surpassed “pattern matching” and were learning in real-time with evolutionary algorithms. Facebook’s Messenger chatbot of 2016 had new capabilities and corporate use cases.

The general format of a Chatbot system takes inputs looking for yes-no answers or keywords to produce a response. But chatbots are evolving to do more comprehensive processes, including natural language processing, neural networks, and other machine learning skills. These chatbots result in increased functionality, enhanced user experiences, and a more human-like conversation that improves customer engagement and satisfaction.

Benefits of Chatbots

Improved customer service. Clients want rapid and easy resolutions. HubSpot found that 90% of customers want an immediate response to customer service issues.

This is seen with the increase in live chat, email, phone, and social media interactions. Chatbots can provide service to users 24/7, handling onboarding, support, and other services. Even robo-advisors can use chatbots as a first line of contact.

More advanced systems can pull from FAQs and other data sources that contain unstructured data like old conversations and documents. Chat GPT uses a massive supply of information up to its 2021 cutoff point.

Improved sales. Chatbots can qualify leads and guide buyers to information and products that fit their needs, producing a personalized experience that builds conversions. For example, they can suggest promotions and discount codes to boost purchase likelihood. They can also be a checkout page aid to reduce cart abandonment.

Money savings. The goal of chatbot deployment for service and sales support is often to reduce casts. Chatbots can service simple and repetitive tasks allowing human agents to focus on complex issues.

For example, if a small HR team is slowed with holiday and benefits questions, a chatbot can answer 90% of these, lessening the HR team’s load. An Oracle survey found that chatbots could produce savings of more than half of a business’s upfront costs. While the upfront costs of chatbot implementation are high, the long-term cost savings in staff equipment, wages, and training will outweigh the initial spending.

Chatbot Implementation Mistakes

While chatbots cannot do everything yet, and it will be a long time before they can do many tasks, they have a skill set that can be used. They can help humans, allowing them to work on more human-required tasks.

No human option. This is a mistake many companies make. Chatbots cannot solve all problems, and the client should have a way to escalate their interaction to a human who can solve it.

Lacking customer research. A bot needs to know what to look for and what to address. If an implementation starts with the most common and time-consuming questions and decides if a chatbot can solve these, it will prove its value many times over.

Neglecting tool integration. A well-built chatbot will be part of the contact center platform, aiding agents and supervisors. Able to pull info from multiple sources and escalate to a live agent with useful contextual information allowing the agent to quickly take over from where the chatbot ended.

Use Cases of Chatbots

How can businesses use chatbots? Here are a few examples of great implementations improving customer service and outcomes.

Retail Banking

Banks or online brokers will generally field simple questions from depositors and borrowers. However, many may come at times of vulnerability. The rising cost of living means a closer focus on finances. Clients may have pending transactions, payments, fraud, or other issues; technology could allow them to monitor these in real time.

If there is only a call center to address these issues, they will have added pressure. But these can be addressed across multiple channels. A banking chatbot with sentiment analysis can handle text-based digital channels (web chatbot, social media, SMS messaging).

Launched on the website, mobile app, and social media, this virtual assistant can handle first and second-tier queries (credit card payments, checking account balances). The implementation of sentiment analysis can detect upset customers, quickly getting them to a natural person.

Chatbots can also aid with the creation of balance alerts, alter other settings, and set up payment reminders, ensuring that both the present issue is solved and the likelihood of a future issue is reduced.

Property Management

As a commercial and residential real estate business grows, more calls are coming in from customers covering a wide range of issues (rent, maintenance, renovations, and potential customers). As a result, they are taking up the contact center’s resources. For example, a chatbot could answer routine renters’ questions, guide them to self-service solutions, or submit a service ticket.

Chatbots can also collect info that will allow the direction of their query to relevant categorical information or help from the related agent. This reduces high call volume and becomes a source to produce tickets 24/7, not just when the office is open, providing notifications to the clients when their submissions are updated. Chatbots can also be set for rent reminders via text and provide online payment options to improve on-time payments—a win-win for the user and the company’s bottom line.

Logistics

Logistics customers want to know where their items are and in real-time. Accurate tracking info is more widely available, but with logistics, there are many variables to contend with on the global level. In addition, high volumes of location requests can overpower a company; even if they are simple requests, they stretch a company’s resources.

A chatbot can deflect many calls from the call center to automated phone response or web services that have a text chat service, providing callers with a way to track their packages and lowering the strain on the service staff, allowing them to focus on complicated issues.

Direct-to-Consumer Retail

Online retailers have a lot of spinning plates. Supply chain, warehousing, couriers, drop shippers, and other order fulfillment, and running an E-com site. When one piece fails, there are unhappy customers. If a manufacturer has assembly issues for a hot new product, the company may experience high call volume and service requests, resulting in many refunds and returns.

An AI-powered chatbot like ChatGPT can be a lifesaver, guiding customers to troubleshooting and instructional media such as video tutorials or the webpage’s knowledge base. It can also take customer feedback and use this information to improve service outcomes, further optimizing flow.

It can also be helpful in the returns process, streamlining the system, resolving returns without the need for a human team member. In addition, by deflecting most inbound calls to self-service, the call center’s volume is decreased, reducing wait times and producing cost savings. The chatbot could also generate viable leads helping consumers find the right products for their needs while upselling products and services through personalized recommendations.

Closing Thoughts

All of the use cases for chatbots provided above are currently being employed and are solutions that use chatbots that are less sophisticated than ChatGPT. However, chatbots can provide higher levels of service that can instantaneously scale with a business while doing so at an attractive ROI.

There are thousands of chatbot implementations possible for today’s businesses, allowing customers to get the real-time service they need with more personalization and specificity than before; this will only continue to improve and expand, allowing more to be provided to consumers.

As chatbots improve their capabilities, their use will likely broaden in scope and volume. Many things humans did in the past, or do now, will be replaced by the faculties of ever-advancing chatbots. These humans will need to be trained to do other work or higher-level service tasks so that we don’t have a glut of out-of-work service personnel.

On the other hand, this training will result in more satisfying work for employees, which in the long run can improve their lives. Balance is needed to gain further acceptance of chatbots by employees and the populace as a whole.

What Are Wrapped Tokens?

Posted on February 15, 2023February 15, 2023 by Jean Chalopin

If you’ve been investing in cryptos, you may have likely heard the term “wrapped” Bitcoin or wrapped tokens. This article will explore the types of wrapped tokens in the crypto space, why they exist, and what benefit they have to you as a crypto trader or long-term investor.

Blockchains Are Separated

Different blockchains like Ethereum and Bitcoin use different protocols and have different functionalities. Moreover, due to the fundamental differences in their algorithms, they cannot talk to each other. While this independence preserves the blockchain’s sovereignty and increases security, it makes the existence of an interoperable distributed ecosystem with easy data exchange challenging.

The ideals of decentralized finance, or DeFi, is a smooth, efficient, and speedy movement of the value, and this is why wrapped tokens can find a place as a practical application. Newer blockchains, such as Polkadot, were developed to solve the interoperability issue that plagues separate blockchains. However, the need for communication between blockchains became apparent, and this communication was possible through the development of wrapped tokens.

Wrapped Cryptocurrency Basics

Wrapped cryptocurrencies and crypto tokens are cryptocurrencies and assets pegged to the value of another cryptocurrency or asset, such as a precious metal, stock, or real estate, and then minted on a DeFi platform. They have become popular as retail crypto brokers made this asset class more accessible and more advanced in tandem.

The original asset gets “wrapped” into a digital vault, with a newly minted token created, which can be used to transact on another blockchain. These wrapped tokens allow non-native assets to be used on any blockchain, building bridges between different networks and creating interoperability in the crypto space.

Wrapped tokens can be created from any asset, art, commodities, collectibles, equities, real estate, and even fiat currencies. However, because wrapped tokens get “pegged” to another asset, it’s required for them to be managed by a custodial entity that wraps and unwraps the asset. We will be discussing why this is a limitation in the crypto world.

The First Wrapped Cryptos

Bitcoin was the first crypto to be wrapped, and the space is dominated by wBTC, which took bitcoin and put it on the Ethereum blockchain using smart contracts. This allowed investors to earn a passive fixed income. There are now many wrapped tokens, most of which use Ethereum’s ERC-20 format or the Binance Smart chain BEP-20 format.

Interestingly, though ERC-20 tokens are issued on Ethereum, the native ETH token is not compliant with ERC-20 standards because ETH was developed before ERC-20. Therefore, Ether must be wrapped to comply with other ERC-20 token standards. A tokenized wrapped Ether has therefore been created on the Ethereum platform.

Cardano, Solana, and Polkadot have begun experimenting with wrapped tokens, facilitating their access to DeFi applications. More recently, projects included the bETH, a wrapped ETH token, which can be traded freely or used as collateral on protocols of the Ethereum network.

Wrapped Token Types

Stablecoins were, in fact, the first wrapped “tokens.” As a result, they have a significant difference from the more established wrapped “coins.” A stablecoin such as the USDT, the Tether, is, for example, backed by a value of approximately one dollar.

However, Tether does not keep the exact amount of USD fiat currency for each USDT minted; its reserves include other assets besides cash, including cash equivalents, T-bills, and more.

There are two general wrapped token types:

Cash Settled

It is impossible to settle these for their underlying asset, only for their cash value.

Redeemable

These wrapped tokens can be exchanged for their underlying asset.

Non-native blockchains will host these two types of wrapped tokens.

Inner-Workings of Wrapped Tokens

Merchants such as Airswap, AAVE, Ox, Maker, and CoinList will mint the number of original tokens sent on platforms such as Ethereum and act as custodians of that value.

A similar process is used when the wrapped token must be converted back into its original coins, such as Bitcoin, Ether, or the asset. The holder of the wrapped token will request the custodian to release the token from the reserves. For every wrapped BTC, there is a Bitcoin that a custodian is holding.

The process employed for minting and managing wrapped tokens remains a limitation in crypto, as a trusted custodian who holds the funds is required. Unfortunately, this requirement needs to be revised for a decentralized distributed network that is supposed to be trustless.

A custodian is required because traders cannot independently use their wrapped tokens for cross-chain transactions. The technology is, however, evolving, and the potential for decentralized options that solve this problem are appearing.

Wrapped Bitcoin (wBTC)

“Wrapped Bitcoin” was first launched in January 2019 and was the first wrapped Bitcoin. The protocol was designed to bring the potential and liquidity of Bitcoin to the Ethereum network and, in doing so, an ERC-20’s flexibility.

The native BTC was unsuitable for decentralized finance (DeFi) transactions; the wrapped version could be used in place of the original asset to transact within the growing DeFi ecosystem and other Dapps within Ethereum’s network.

The wrapped Bitcoin is a significant addition to the cryptocurrency space. While a wBTC’s value is equivalent to the original Bitcoin, the added functionality accrued with the change to wBTC increases its value allowing it to be used in DeFi applications.

A holder of BTC can lend their Bitcoin via smart contracts by simply connecting their crypto wallet to a decentralized lending platform and earning a fixed interest rate each year. Concurrently, borrowers can use their crypto (BTC) as collateral which could automatically go to the lender in case of a default.

Using this type of financing, holders of the currency can still see returns on their holdings even in bear markets if the value of their asset drops.

Wrapped BTC, Unwrapped

There are three primary actors in wBTC’s creation and management.

The DAO

wBTC’s Decentralized Autonomous Organization comprises 17 members, all from the DeFi space, who hold a multi-signature contract allowing them to add to or remove from the list of wBTC merchants and custodians.

Merchants

These administrators trigger the minting of wBTC by sending a defined amount of BTC to the custodian and requesting the mining of an equivalent amount of the wrapped tokens, defined by the investor’s and trader’s demands.

Custodians

These trusted agents act as vaults who provide reliability and security for wBTC, ensuring that all the wBTC will be backed and verified via an on-chain proof of reserves. Custodians mint the wBTC and send that equivalent amount of wBTC (a one-to-one pegged value of BTC) back to the requesting merchant.

In essence, the merchant transfers the real BTC to the custodian’s address on the Bitcoin blockchain, which is then locked. Once the real BTC is received, the custodian’s address mints the equivalent amount of wBTC on the Ethereum network.

The reverse will happen, and the wBTC will be converted back into real BTC through the burning (destroying) of the ERC-20 BTC token, at which point the locked BTC will be released. The minting and burning of wBTC tokens are tracked and verified on the Ethereum blockchain.

Why Is There wBTC?

wBTC was created because of the growth of DeFi applications which are now valued in the billions of dollars. These tokens are sent to lending platforms, options, derivatives, and other financial applications.

The demand for BTC use as an underlying asset in DeFi was such that it needed to be converted to ERC-20 compatible tokens to participate in Ethereum ecosystem Dapps.

Are They Safe?

From a technical viewpoint, a wrapped Bitcoin token is safe. The original BTC will be in the custody of safe platforms like Ethereum or the Binance Smart Chain. When it is converted to an ERC-20 or BEP-20 token, it will maintain the security of the interconnected network.

A flaw with the wrapped BTC tokens is the need for trust in a custodian that holds the underlying asset. If that custodian unlocks and releases the Bitcoin to someone else, the ERC-20-compatible wrapped Bitcoin holders would be holding a worthless asset.

How the original Bitcoin is held determines the security level provided.

Centralized Custodial Bridge

For example, this organization promises to mint the ERC-20 tokens. The centralized entity must be trusted to hold BTC and not abscond. Users must ensure that these organizations are backed with guarantees and insurance in case something terrible happens.

Decentralized smart contract bridge

These would be the best choice in the crypto world. There would be no need to trust a third party, only the immutable time-stamped intelligent contract coding.

The security of wrapped BTC bridges, crossing different chains, has resulted in several arguments in the DeFi community because of the need to rely on custodians to keep the real BTC locked and their financial incentive not to.

Closing Thoughts

Arcane Research reports that the amount of Bitcoin currently locked on the Ethereum blockchain has grown to $3.5 billion as of Dec 2022 (similar to the 3.6 billion Coinbase estimate above). In addition, it is estimated that over 1% (215,800) of Bitcoin’s current supply of 19.2 million coins is now being used in DeFi, all through wrapped tokens of various types.

Wrapped tokens increase the liquidity and capital efficiency of centralized and decentralized exchanges due to their capability to move assets across multiple blockchain platforms that otherwise would have remained isolated.

Additional advantages wrapped tokens provide speedy transaction times and lowered fees possible with newer blockchains, exceeding the capabilities of older blockchains like Bitcoin and Ethereum’s first generation.

Wrapped tokens also offer fractionalized ownership which is not usually possible for some underlying assets such as art, collectibles, or a classic car. We might see wrapped tokens appearing with discount trading platforms or as part of greater liquid portfolios.

These asset-packing solutions make bitcoin and, more importantly, other assets more useful. We will see many items wrapped into fungible and nonfungible tokens that can be used in the DeFi and metaverse moving forward. The ERC-20 and BEP-20 token formats make the world of DeFi possible.

Stablecoins vs. CBDCs

Posted on February 11, 2023February 11, 2023 by Jean Chalopin

Stablecoins are crypto assets whose values are pegged to fiat currencies, such as the US dollar. Stablecoin operators generally maintain a reserve of fiat currency which equals the token’s circulating supply.

With the rapid rise in stablecoin circulation over the past few years, central banks have increased their efforts to develop their stable digital currencies. These centralized fiat copies are called Central Bank Digital Currencies (CBDCs), or cryptos backed by a country’s central bank.

Rather than being pegged to a fiat currency, CBDCs are a digital form of the country’s legal tender. This article will explain some of the critical differences between Stablecoins and CBDCs, and why CBDCs add very little to the global economy.

The Past Decade

Cryptos have come a long way since the inception of Bitcoin in January 2009. While still a speculative asset, cryptos are evolving into an asset class that is a legitimate investment opportunity as respected investing apps continue to onboard crypto trading.

The technological foundation of cryptocurrencies, the blockchain, has been shown to have utility in several public and private applications. Blockchain is now being applied in fields from the supply chain to medicine, gaming, ticketing, art, and finance.

What has also changed is crypto, and blockchain’s favorability with governments. Different crypto projects have garnered different levels of openness to regulation, and different governments have different perceptions about the advantages or threats of cryptos.

With these recent advances, two unique kinds of digital currencies have resulted. Stablecoins and CBDCs have emerged as potential options that could be widely used for commerce and trade in the future. Being related to fiat, these digital currencies may, at first glance, be similar, but there are significant differences between them.

What Are Stablecoins?

Stablecoins represent a type of tokenized asset whose value is pegged to a real-world asset, generally a fiat currency like the USD, but there are stablecoins pegged to gold and other assets too. They’re vital for removing almost all transaction fees and enabling the liquid trading of those using advanced crypto brokers.

Stablecoin operators usually maintain a reserve of the fiat currency and other assets (including cryptocurrencies) equal to the token’s circulating supply.

If the project mints more stablecoins, an equal amount of the pegged fiat currency should be added to the project’s reserves, and if “burned” (the process of unminting the coin and removing it from circulation), the reserve is reduced by an equal amount. This method is how many stablecoins maintain their value with the pegged currency.

What Are CBDCs?

On the other end of the spectrum, Central Bank Digital Currencies (CBDCs) are digital assets (not specifically a cryptocurrency) backed by a country’s or region’s central bank. Rather than being pegged to the fiat currency, these digital assets would be a digital form of the legal tender of the region or country such as China, which is probably the furthest ahead in its CBDC rollout program.

Similarities Between CBDCs and Stablecoins

The most significant similarity between stablecoins and CBDCs is that they are both digital currencies that can be used for payment. In addition, the speed by which a digital currency can be transmitted and a transaction completed makes these useful for domestic and international trade.

Depending on the CBDC, if they are blockchain-based, they can be stored pseudonymously in a crypto wallet like any other crypto. The transactions are all stored on a publicly distributed ledger. However, CBDC programs are generally authorized as (private) blockchain-based.

A second similarity between the two concerns their volatility. Most cryptocurrencies are volatile; changing 5 to 10% or more in a month is not uncommon, even for Bitcoin and Ethereum, which have the highest market caps. However, while stablecoins and CBDCs are digital assets, most of them are much more stable.

The final similarity between the two reflects their regulation. Both stablecoins and CBDCs are regulated. Third-party auditing firms regulate stablecoins, and central banks regulate CBDCs. The chance of a rug pull occurring for both digital currencies is minimal.

Differences Between Stablecoins and CBDCs

The first significant difference between CDBCs and stablecoins is their governing authority. Stablecoins are usually governed by private companies such as Circle or Binance. Still, there are also stablecoins, such as DAI, that are governed by DAOs (decentralized autonomous organizations), or a group of governance token holders that have a vote in the management of the coin.

CBDCs, on the other hand, are created, controlled, and regulated by the central bank of a country or region that releases the CDBC. Any country can develop a CBDC of its fiat currency and manage its monetary policy just like physical fiat.

The second difference is that stablecoins are (generally) backed by an equivalent amount of fiat currency. You can exchange your stablecoins for an actual dollar stored in the stablecoin’s reserves. CBDCs don’t have any assets backing them; they only have the promise of the country and its central bank. Governments used to use a gold standard that backed the currency with a supply of gold, but this was given up with the change to fiat.

Stablecoins fall under the crypto blanket. This designation means that there is a potential for national governments to ban them, and they can be taxed as digital assets. Alternatively, CBDCs would be considered the same as a country’s currency and, therefore, would be neither taxed nor banned.

Stablecoin’s Issues

Stablecoins have become the standard for international transactions and investments in decentralized finance (DeFi). Stablecoins have chosen to peg with only the most traded currencies, such as the USD, Euro, and Yen, and generally have strict auditing to preserve their international trust. However, there are two primary issues with stablecoins.

First, there is a need for stablecoins to trust the organization that is managing the coin and the organization that is auditing the coin’s reserves. For example, Terra (now known as Terra classic UST) is a famous algorithm stablecoin that fell from grace because the system, which included a management token Luna, that it relied on to keep its peg with the USD faltered when a significant amount of the coin was traded out at one time. Its holders lost $60 billion.

Similar algo-based stablecoins could have similar issues yet to be tested or discovered. There must also be trust in the reserves held by the stablecoin to ensure that the peg is sufficiently being met and that the auditors are doing their job; any break in this system could cause the coin to falter.

Second, a stablecoin is only as good as the fiat currency to which it is pegged. If the country falters in its currency management, the coin’s value will fall, which is out of the control of the stablecoin management and its investors.

CBDC’s Issues

CBDCs will only be as strong as the fiat currency of the minting country. If a country’s currency is banned from commerce by a nation, government, or organization that does not accept that currency, then they would not accept the CBDC form of it either. A fiat, in cash or CBDC form, is not backed by anything and is at the mercy of its central bank’s control.

CBDCs are not cryptocurrencies. They don’t even have to be on a blockchain or other distributed ledger. They are controlled by a central authority and can be minted and burned at that authority’s whim. True cryptos are decentralized and controlled by rules that cannot be easily changed. The benefits of blockchain are what make cryptocurrencies unique. They are trustless and immutable.

CBDCs, if on private blockchains, cannot benefit from these. They don’t incorporate all crypto aspects.

Closing Thoughts

Central bank digital currencies have the potential to “partially” transform economies, making transactions safe, bringing greater transparency and inclusivity to those that have been unbanked.

However, this is where the benefits of CBDCs stop. CBDCs are not a replacement for cryptocurrencies and stablecoins, which are the basis for DeFi applications allowing them to have a set of uses that CBDCs cannot fathom.

Nearly all countries have turned over their financial controls to central banks. This is the reason that actual cryptocurrencies are of potential benefit. Cryptos are not under the control of any central authority. However, it is unlikely that a country would take back its monetary policy control from its central bank.

Every CDBC will likely be on its blockchain; that is the only way to guarantee control. This system would then require bridges between different countries’ blockchains, such as what Polkadot, Visa, and PayPal are trying to do. The best solution may be a single global cryptocurrency that is not controlled by any central authority and is fully decentralized.