Fintech - Trusting Disruption by Deltec

DeFi Explained

Posted on May 10, 2023May 10, 2023 by Jean Chalopin

Decentralised finance (DeFi) is rapidly revolutionising the financial industry by offering innovative financial products and services that are decentralised, transparent, and accessible to everyone. DeFi operates on blockchain technology and allows individuals to take control of their finances without intermediaries.

According to Cointelegraph, the DeFi market has seen tremendous growth, with the total value locked in DeFi protocols surpassing $70 billion in January 2023. As DeFi continues gaining momentum, it is expected to change how the world thinks about and interacts with finance.

What Is DeFi?

Unlike traditional finance, which relies on intermediaries such as banks and financial institutions, it is built on decentralised networks that allow for direct peer-to-peer transactions and offer more transparency, security, and accessibility.

At its core, DeFi leverages blockchain technology to create a new financial infrastructure that is open and accessible to anyone with an internet connection. This infrastructure is based on smart contracts, self-executing agreements that enforce the terms of a contract without the need for intermediaries. This means that its users can access a range of financial products and services, such as lending, borrowing, trading, and insurance, without going through a traditional financial institution.

Financial firms and institutions are taking notice and are looking to incorporate its benefits into their operations. The transparency and security offered can help to reduce the risk of fraud and increase efficiency in financial transactions.

Additionally, its decentralised nature means that it has the potential to offer financial services to individuals who are currently underserved by traditional finance, such as those in developing countries or those with limited access to conventional financial services.

How do DeFi and Blockchain Work Together?

Decentralised finance and blockchain technology are two sides of the same coin, enhancing the other to create a new financial ecosystem. DeFi leverages blockchain technology to provide a decentralised and transparent infrastructure for financial transactions, while blockchain technology offers the security and immutability necessary.

Blockchain technology, the underlying technology, is a decentralised and secure ledger that records transactions across a network of computers. This decentralised nature means there is no central point of control or single point of failure, making blockchain networks highly resistant to hacking and tampering. The transparency and immutability of blockchain technology make it ideal for DeFi, as it allows for all transactions to be recorded publicly and makes it difficult for anyone to alter the records.

DeFi takes advantage of this security and transparency to offer various financial services, such as lending, borrowing, trading, and insurance, without intermediaries. For example, a sample lending platform may allow users to lend and borrow assets using smart contracts, with the platform’s underlying blockchain technology providing the security and transparency necessary for transactions. In this way, DeFi leverages blockchain technology to offer a new, decentralised financial infrastructure accessible to anyone with an internet connection.

DeFi and blockchain technology work together to create a new financial ecosystem that is decentralised, transparent, and secure. The decentralised nature of blockchain technology provides the security and transparency necessary for DeFi to function effectively. At the same time, it leverages blockchain technology to offer financial services without intermediaries. This combination has the potential to change the way the world thinks about and interacts with finance, making financial services more accessible and secure for everyone.

DeFi and Traditional Finance

Traditional finance firms need to care about DeFi because it represents a significant shift in the financial landscape. It offers a new way for people to manage their financial assets and transactions without relying on centralised intermediaries like banks. This decentralised model has proven to be secure, transparent, and accessible to people worldwide, making it an attractive alternative to traditional finance.

By ignoring DeFi, traditional finance firms risk being left behind as more people flock to decentralised alternatives. They need to stay ahead of the curve and understand the growing ecosystem to adapt and evolve their own services to meet the market’s changing demands.

Furthermore, DeFi has the potential to disrupt traditional finance and impact the bottom line of these firms. Traditional finance firms must take DeFi seriously and find ways to integrate it into their business models to remain relevant and competitive.

How Are Start-Ups Using DeFi?

Aave is a DeFi start-up that offers decentralised lending and borrowing services. The platform allows users to deposit their digital assets as collateral and then borrow other assets at a flexible interest rate without needing a central authority.

Aave uses smart contracts to automate the lending and borrowing process and ensure that each loan’s terms are transparent and fair. The platform also offers features like flash loans, which allow users to borrow funds without collateral for a short time, and liquidity pools, which enable users to earn interest on their deposited assets.

Compound is another start-up revolutionising the lending and borrowing world. The platform allows users to deposit and lend various digital assets, including cryptocurrencies, stablecoins, and non-fungible tokens.

Like Aave, Compound uses smart contracts to automate the lending and borrowing process, but it also includes a unique feature called ‘cTokens’, which allows users to earn interest on their deposited assets. cTokens are unique because they represent a user’s stake in a particular asset within the Compound platform, and their value changes in real-time based on market conditions.

Uniswap is a decentralised exchange that allows users to trade cryptocurrencies in a trustless manner. Unlike traditional centralised exchanges, Uniswap doesn’t require users to deposit their funds into a central exchange, which reduces the risk of theft and hacks. Uniswap uses a unique liquidity pool model where users can provide liquidity to the platform in exchange for a share of the trading fees.

Source

The platform’s automated market maker algorithm ensures that users can trade token pairs without needing an order book. This makes it easy for users to trade even less popular tokens that might not be listed on centralised exchanges.

DeFi start-ups are using decentralised finance to disrupt traditional finance and offer new financial services that are secure, transparent, and accessible to people all over the world. By using smart contracts and other blockchain technologies, these start-ups are creating a new financial ecosystem free from centralised intermediaries’ limitations and restrictions.

Moving to a DeFi Model

Fidelity Investments is a traditional finance firm exploring DeFi to offer new financial services to its customers. The company has launched a new division called Fidelity Digital Assets that provide custody and trading services for cryptocurrencies, making it one of the first large traditional finance firms to embrace DeFi.

Fidelity is using DeFi to offer its customers access to new investment opportunities in the cryptocurrency market and reduce the barriers to entry that have traditionally made it difficult for institutional investors to participate in the market.

Goldman Sachs is another traditional finance firm that is exploring DeFi. The company has been actively engaged in DeFi’s value proposition and creating DeFi products. Goldman Sachs is collaborating with other businesses to develop a digital assets framework, per a press release from November 2022.

JP Morgan is another traditional finance firm that is moving into DeFi. The company has been exploring blockchain technology for several years and working on its DeFi initiatives. For example, JP Morgan initiated its first DeFi trade on blockchain in 2022. Project Guardian, a trial programme run by the Monetary Authority of Singapore (MAS) to investigate potential DeFi applications in wholesale finance markets, enabled the trade.

Traditional finance firms are exploring DeFi to offer new financial services to their customers and stay ahead of the curve in an ever-changing economic landscape. By embracing DeFi, these firms can reduce barriers to entry and offer secure, transparent, and accessible financial services to their customers.

Risks and Challenges

One of the main risks associated with DeFi is security. Since it is built on decentralised networks, it is more vulnerable to hacking and other forms of cybercrime. Smart contracts, which are used to automate the process of lending, borrowing, and trading in DeFi, are particularly vulnerable to security threats. For example, if a hacker can exploit a vulnerability in a smart contract, they can steal funds from users or manipulate the platform in other ways.

Another challenge is scalability. As more people use DeFi platforms, the networks can become congested, leading to slow transactions and high gas fees. This can make it difficult for users to participate in DeFi platforms, especially during times of high demand.

Since DeFi is a relatively new technology, there is still a lot of uncertainty about how it will be regulated in the future. Some countries have already taken steps to regulate DeFi, while others have been more cautious. This uncertainty can make it difficult for DeFi platforms to operate and discourage investors from participating in the market.

Lack of liquidity is still associated with DeFi. Although DeFi platforms have snowballed in recent years, they still have relatively small liquidity pools compared to centralised exchanges. This can make it difficult for users to trade their assets and lead to price volatility.

Finally, DeFi can also be challenging for non-technical users. Since it is built on complex technology, it can be difficult for users unfamiliar with blockchain and cryptocurrency to participate in DeFi platforms. This can make it difficult for DeFi to achieve widespread adoption and discourage users from participating in the market.

Despite these risks, by integrating the right technology, such as blockchain, DeFi will still disrupt and revolutionise the industry.

Closing Thoughts

The future of DeFi is exciting and filled with endless possibilities. In the next ten years, we can expect to see it become more accessible and user-friendly, allowing more people to participate in the market. This will likely increase the number of DeFi platforms and the size of the DeFi market.

Additionally, as DeFi grows and matures, we expect to see more innovation in the space, including new financial products and services built on decentralised networks. This will likely include everything from new forms of lending and borrowing to new insurance products and investment opportunities. Overall, the future of DeFi is bright, and we expect continued growth and innovation over the next decade.

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.deltec.io.

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.deltec.io.

Blockchain and AI

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

According to a report by Allied Market Research, the global blockchain technology market was valued at $3 billion in 2020 and is expected to grow to $39.7 billion by 2025. Similarly, the AI market is projected to grow to $190 billion by 2025, according to a report by MarketsandMarkets.

With the increasing demand for both blockchain and AI, combining these technologies can revolutionise many industries and transform the way we do business.

What Is Blockchain?

Blockchain technology is a decentralised, distributed ledger that allows for secure and transparent transactions without intermediaries. It was first introduced in 2008 by an unknown individual or group of individuals under the pseudonym Satoshi Nakamoto to facilitate Bitcoin transactions.

The technology works by recording transactions in blocks linked together to form a chain, hence the name ‘blockchain’. Each block contains a cryptographic hash of the previous block, ensuring the chain’s integrity.

The benefits of blockchain technology include increased security, transparency, and efficiency. By eliminating the need for intermediaries, such as banks, transactions can be completed faster and at a lower cost. The technology’s decentralised nature also makes it more resistant to fraud and hacking. Blockchain is used in various industries, including finance, healthcare, and supply chain management.

What Is AI?

AI, or artificial intelligence, refers to the ability of machines to perform tasks that would typically require human intelligence, such as learning, reasoning, and problem-solving. The history of AI traces back to the 1950s when researchers first began developing algorithms for machine learning. Since then, AI has evolved to include many technologies, including neural networks, natural language processing, and computer vision.

AI has rapidly transformed the finance industry by providing faster, more accurate decision-making capabilities and improving operational efficiency. Some examples of how AI is being used in finance include:

Fraud detection: AI-powered fraud detection systems use machine learning algorithms to identify unusual behaviour patterns and detect fraudulent activities.
Trading and investment: AI-powered trading algorithms use natural language processing (NLP) to analyse news articles, social media, and other data sources to identify patterns and predict market movements.
Customer service: Financial institutions use chatbots and virtual assistants to provide customer service and support.

Financial firms worldwide are increasingly turning to artificial intelligence (AI) technologies to improve their efficiency, automate their processes, and provide better customer service. Three examples of financial firms that have successfully adopted AI are Capital One, Citigroup, and Ping An.

Capital One, a US-based financial institution, has implemented natural language processing (NLP) to enhance customer service. Its virtual assistant, Eno, can understand and respond to customer inquiries in natural language, available via the company’s mobile app, website, and text messages. The system has helped Capital One reduce wait times and enhance customer satisfaction. The company has also used machine learning to detect and prevent fraudulent activity.

Citigroup, a multinational investment bank, has been utilising computer vision to analyse financial data. Its research team has developed an AI-powered platform to analyse financial statements and other data to identify patterns and trends.

The platform can also provide predictive insights, assisting investors in making well-informed decisions. The system has improved Citigroup’s research capabilities and enabled the company to provide superior investment advice to its clients.

Ping An, a Chinese insurance and financial services company, has been using machine learning to improve its risk management. Its risk management platform, OneConnect, can analyse large amounts of data to identify potential risks and provide real-time insights.

The system can also offer tailored risk assessments for different types of businesses. OneConnect has assisted Ping An in reducing its risk and enhancing its operational efficiency.

Financial firms are increasingly adopting AI technologies to remain competitive and enhance customer service. By leveraging NLP, computer vision, and machine learning, financial institutions can streamline operations, improve customer service, and make informed decisions. Firms that fail to embrace these technologies may risk falling behind their competitors.

Why AI and Blockchain Must Work Together

AI and blockchain are two of the financial services industry’s most innovative and disruptive technologies. While they are often seen as separate technologies, AI and blockchain are becoming increasingly interdependent for several reasons.

One of the most significant advantages of blockchain is its ability to provide secure, transparent, and tamper-proof transactions. However, blockchain cannot detect fraud, which is where AI comes in.

By integrating AI and blockchain, financial firms can build more secure and transparent systems that leverage AI’s fraud detection capabilities to enhance the trustworthiness of blockchain. This combination can offer improved security and transparency in transactions, which is crucial in financial services.

Another advantage of integrating AI and blockchain is the improved accuracy and efficiency of financial services. Smart contracts built on blockchain can automate financial transactions and self-execute when predefined conditions are met. By integrating AI, smart contracts can also be made more intelligent and capable of automatically adjusting to changing conditions. This integration can lead to the creation of more efficient and accurate financial systems.

Integrating AI into the blockchain can also help financial firms to detect and mitigate risks more quickly and effectively. AI can analyse vast amounts of data in real-time, making it an ideal tool for risk management. For example, AI can identify anomalies in financial transactions and flag them for review or rejection, making detecting fraud and other risks easier. This benefit can lead to better risk management, an essential component of financial services.

The integration of AI and blockchain can also help financial firms to comply with regulations more effectively. Financial rules are complex and ever evolving, making compliance a significant challenge for financial firms. By combining AI and blockchain, financial firms can improve their ability to comply with regulations and reduce the costs and risks associated with non-compliance. For example, blockchain can provide an immutable record of transactions, while AI can be used to analyse the data and ensure that it complies with regulations.

AI Creates New Business Models

Finally, integrating AI and blockchain opens up new business models and opportunities for financial firms. Decentralised finance (DeFi) applications are leveraging AI and blockchain to create new financial products and services that are more efficient, accessible, and affordable than traditional financial services. The combination of AI and blockchain technology creates new opportunities for financial firms, leading to the development of new financial products and services that were not possible before.

In practice, many examples of financial firms are already successfully leveraging AI and blockchain to enhance their services. For instance, Ripple, a blockchain-based payments solution, has integrated AI to improve its fraud detection and risk management capabilities. JPMorgan Chase is using blockchain to develop a decentralised platform for tokenising gold, and AI is being used to analyse the data generated by the platform. Visa also leverages blockchain and AI to enhance its fraud detection and prevention capabilities.

AI and blockchain can transform financial services, enhancing security, transparency, accuracy, efficiency, risk management, compliance, and new business models. By working together, AI and blockchain can create synergies that make them greater than the sum of their parts. Financial firms embracing AI and blockchain are likely better positioned to succeed in an increasingly competitive and complex financial services landscape.

Closing Thoughts

The future of AI-enabled blockchain in financial services is promising, with significant advancements expected in the next decade. Here are some potential developments:

Financial firms will continue integrating AI and blockchain to improve their operations, increase efficiency, and reduce costs.
By combining AI’s ability to analyse data with blockchain’s secure and transparent ledger, financial firms can develop systems that provide more secure and private transactions.
Decentralised finance (DeFi) applications are already leveraging AI and blockchain to create new financial products and services.
As AI and blockchain become more integrated into financial services, regulatory oversight will increase.
Integrating AI and blockchain will likely create new business models and revenue streams for financial firms.

Overall, the future of AI-enabled blockchain in financial services looks bright, with continued growth and development expected in the next decade. As financial firms increasingly adopt and integrate these technologies, we can expect to see significant advancements in efficiency and security as new business opportunities emerge.

What Is Generative AI?

Posted on April 26, 2023April 27, 2023 by Jean Chalopin

Generative AI is a rapidly developing field of artificial intelligence that has been making waves in recent years. Using advanced algorithms, generative AI can create original and often impressive content, such as images, music, and even text, without direct human input.

This article will delve deeper into generative AI, exploring what it is, how it works, and its potential uses.

Understanding Generative AI

Unlike other types of AI designed to complete specific tasks, such as image recognition or language translation, generative AI is programmed to learn from existing data and generate new content based on that information.

The key to this process is the use of deep neural networks, designed to simulate how the human brain works, allowing the AI system to learn from patterns and generate new content.

One of the most impressive aspects of generative AI is its ability to create content that is often difficult to distinguish from something a human would produce. For example, generative AI can be used to create realistic images of people who don’t exist or to generate music that sounds like it was composed by a human musician. The image below is AI-generated and not of a real person.

This has exciting implications for various industries, from art and entertainment to marketing and advertising.

Against Other Forms of AI

Generative AI is distinct from other forms because it is designed to create something new rather than simply perform a specific task. This contrasts with different types of AI, such as supervised learning or reinforcement learning, which are focused on solving a particular problem.

For example, supervised learning algorithms are commonly used in image recognition software to identify and classify objects within a given image. In contrast, generative AI can be used to create original ideas, such as realistic portraits of people who don’t exist or entirely new landscapes that have never been seen before.

Another example of a different type of AI is natural language processing (NLP), which is used to analyse and understand human language. While NLP can generate text, it is typically focused on tasks such as language translation or sentiment analysis. In contrast, generative AI can be used to create entirely new pieces of text, such as short stories, poetry, or even news articles.

Most of the AI we see today is still based on machine learning, which involves training a model on a large dataset to identify patterns and make predictions. This is done by feeding the machine learning algorithm a set of labelled data, allowing the system to learn from the data and identify patterns that can be used to make predictions on new, unseen data.

While machine learning has already had a significant impact on many industries, from healthcare to finance to transportation, the ability to create entirely new content has the potential to revolutionise these fields completely.

Ultimately, the critical difference between generative AI and other types of AI is the focus on creativity and originality.

The Benefits of Generative AI

Generative AI is a rapidly developing field with numerous potential benefits.

One industry that could improve significantly from generative AI is fashion. With the ability to generate unique designs and patterns, it has the potential to transform the fashion industry. Designers can use it to create new designs, allowing them to produce unique and eye-catching pieces that stand out from the competition. By using it, designers can also save time and resources, allowing them to focus on other aspects of the creative process.

A second industry that stands to gain is gaming. With the ability to generate unique characters, landscapes, and environments, it has the potential to revolutionise the gaming industry. Game designers can use it to create original game elements that are unique and engaging for players. It enables game designers to save time and resources, allowing them to focus on other aspects of the game development process.

Finally, generative AI has the potential to shift the healthcare industry. Using it, researchers can create new drugs and treatments, allowing them to treat diseases and illnesses. It can also be used to analyse medical images and data, allowing doctors and researchers to diagnose and treat patients more accurately. With its ability to create new content and analyse large amounts of data, generative AI can potentially transform how we approach healthcare.

Successful Case Studies

Several companies are already using generative AI to great effect in their applications. Here are a few examples:

Adobe is using generative AI to develop new tools and features for its Creative Cloud suite of products. For example, Adobe’s Sensei platform uses generative AI to analyse images and suggest improvements. The company has also used it to develop new fonts and predict which colours will be popular in the coming year.

OpenAI is a research organisation focused on advancing AI safely and responsibly. The company has developed several generative AI models, including GPT-3, a language model that can generate text that is often difficult to distinguish from something a human would write. GPT-3 has many potential applications, from natural language processing to chatbots. The revolutionary Chat GPT platform is based on these models.

IBM uses generative AI to develop new solutions for various industries, including healthcare and finance. For example, the company has developed a system to analyse medical images and provide more accurate diagnoses. It has also used it to create new financial risk models.

Nvidia is a leading provider of graphics processing units (GPUs) that are used in various applications, including gaming, scientific research, and machine learning. The company is also investing heavily in generative AI and has developed several models that can generate realistic images and even entire virtual environments.

These companies are just a few examples of how generative AI is already being used to create new opportunities and drive innovation in several industries. As the technology develops, it will be interesting to see how it is integrated into even more applications and use cases.

The Risks

While generative AI has enormous potential, several risks are also associated with the technology. One of the most significant risks is its potential to be used for malicious purposes.

For example, it can be used to create realistic-looking fake images, videos, and audio, which can be used for deception or propaganda. In the wrong hands, these tools could be used to manipulate public opinion, create fake news, or even commit fraud.

Another risk of generative AI is its potential to perpetuate biases and inequalities. Its models are only as good as the data they are trained on, and if the data is biassed, then the model will be biassed as well.

For example, a generative AI model trained on predominantly white and male data may be more likely to generate images and text biassed against women and people of colour. This can perpetuate existing inequalities and reinforce harmful stereotypes.

In one study published in 2018, researchers found that several leading facial recognition algorithms were significantly less accurate at identifying the faces of people with darker skin tones, particularly women. This bias was pervasive across multiple algorithms from different companies. The researchers attributed it to the fact that the training datasets used to develop the algorithms were overwhelmingly white and male.

A third risk of generative AI is its potential for cyberattack use. For example, generative AI can generate realistic-looking phishing emails, which can trick people into giving up sensitive information or clicking on links that download malware onto their devices. Additionally, generative AI can generate realistic-looking social media profiles, which can be used for impersonation or other online attacks.

Overall, while it has enormous potential for positive applications, it is vital to be aware of the risks associated with the technology. As the technology continues to develop, it will be necessary for developers and users of generative AI to take steps to mitigate these risks and ensure that the technology is being used responsibly and ethically. This will require ongoing research, development, collaboration, and coordination among stakeholders in various industries.

Closing Thoughts

Generative AI has made tremendous progress in recent years, and there is no doubt that the technology will continue to evolve and improve in the coming decade. One of the most promising areas of development for generative AI is in the realm of creative applications. For example, generative AI is already being used to generate music, art, and even entire literature. As technology advances, we can expect to see more creative works generated by AI and even collaborations between human and machine artists.

The Future of AI-Based Art

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

The worldwide art market, worth billions of dollars, has historically been a traditional and restricted sector. However, as artificial intelligence advances, the art industry is shifting towards a more democratised and accessible market, introducing the phenomenon of AI-based art.

AI-powered technologies can now generate creative works of art, question the validity of existing works, and even estimate their worth. While some are concerned about AI’s ability to disrupt the industry, others view it as a way for artists to reach a larger audience and collectors to access a greater range of investment options.

According to an Art Basel and UBS estimate, the global art industry will be worth $64.1 billion in 2020. However, the market has been on a rising trend in the last decade. The worldwide art market was valued at $39.7 billion in 2011, representing a 61% increase over the previous ten years.

It is important to note that these data are estimations, and the art market is notorious for its lack of openness, making determining the exact value of the market complex.

The Rising Art Market

There are several reasons why the value of the art market has been on a rising trend over the last decade:

Increasing wealth. As wealth has increased globally, more people have been able to invest in art, driving up prices.
Globalisation. The art market has become more global, with a broader range of buyers and sellers participating worldwide. This has increased the demand for high-quality art and has led to a rise in prices.
Investment demand. Art is seen as a safe haven asset, and many investors have been buying art to diversify their portfolios and protect against economic uncertainty.
Technology. The growth of technology has made it easier for buyers and sellers to connect, increasing transparency and efficiency in the market.
Awareness and education. Increased awareness and education about the art market have led to more people becoming involved and interested in collecting, further driving up demand and prices.

These factors have contributed to the overall rise in the art market’s value over the last decade. However, despite the growth in the traditional market, there is a continued focus on AI-based art.

What Is AI-Based Art?

AI-based art is artwork that is made or aided by machines. This can take various forms, from entirely created art pieces generated by algorithms to works that utilise AI technology in their development or presentation. AI-based art may use multiple AI models, such as machine learning and computer vision, to create one-of-a-kind and creative artwork.

Machine learning is utilised to produce art by training algorithms on vast datasets of existing art. These datasets teach the algorithm patterns and styles, which they then employ to create new works. In contrast, computer vision allows editing and improving existing pictures and synthesising new images based on visual inputs.

There are several platforms and websites that offer tools for creating AI-based art:

DeepArt.io: A platform that allows users to upload an image and have it transformed into a unique piece of art using AI algorithms.

Pikazoapp.com: An app that uses AI to remix existing images and turn them into unique works of art.

Let’s Enhance: An AI tool to upscale and enhance images.

RunwayML: An open-source platform that offers a wide range of AI models for creative purposes, including art.

Artbreeder: A platform that allows users to breed unique art pieces by combining existing art and AI algorithms.

Online markets, cryptocurrency-based marketplaces, and direct sales are all methods for selling AI-based art. Artsy and Saatchi Art, for example, offer a forum for artists and collectors to buy and sell art, including AI-based art. Cryptocurrency-based marketplaces such as SuperRare and Rarible enable the purchase and sale of AI-based art using cryptocurrency.

Artists can also sell their AI-based paintings to collectors directly through their websites or personal networks. Furthermore, galleries and shows specialising in digital and new media art may be viable platforms for selling AI-based art.

The ideal method for selling AI-based art will be determined by the artist’s aims, target audience, and genre of work. Portrait of Edmond de Belamy, made by the Paris-based art collective Obvious, is one of the most valuable AI-generated art to date. That painting sold for $432,500 at a Christie’s auction in October 2018.

The Benefits of AI-Based Art

Despite the continuous expansion of the traditional art business, interest in AI-based art is expanding. This can be attributable to a variety of factors.

For starters, AI-based art provides a more accessible and democratised market, enabling a broader spectrum of people to engage as producers or consumers. Because AI-generated artwork has lower production costs than conventional art, they are more affordable to a broader audience.

Furthermore, the application of AI in art expands the definition of art and offers new avenues for creativity and self-expression. Some perceive the emergence of AI-based art as providing new investment opportunities in the form of one-of-a-kind artwork made by robots.

Finally, the use of technology and artificial intelligence in the art business simplifies and improves procedures from conception to sale. These characteristics, taken together, lead to the rising interest in AI-based art, even as the traditional art industry expands.

Industry Use Cases for AI-Based Art

Advertising and marketing, film and video game creation, and fashion and textile design are some of the major industrial use cases for AI-based art.

AI algorithms can create new pictures or improve old ones for use in advertising and marketing materials, easing the creative process and allowing for more remarkable design and visual effects versatility. In 2022, Heinz went viral after asking AI to draw its interpretation of ketchup.

AI algorithms can be used to build virtual sets, characters, and special effects in the film and video game industries, possibly decreasing the time and expense associated with traditional production techniques.

AI algorithms can augment the fashion and textile design sectors by producing new patterns and designs for textiles, as well as aid in the design process by giving suggestions and coming up with new ideas.

AI-based art and imagery are also being applied in fields such as interior design, product design, and architecture, to name a few. The application of AI in these industries is opening new avenues for creative expression and problem-solving. It has the potential to transform how art and design are created and consumed.

The Challenges and Risks

The use of AI in creating art is a relatively new field, and there are several challenges and risks associated with AI-based art. One of the main challenges is the lack of a clear legal framework.

Currently, there is no consensus on whether AI-generated art can be considered a work of authorship and who should own the rights to such works. This lack of clarity can lead to disputes over ownership and copyright and could stifle the growth of the AI-based art market.

A further challenge is evaluating the quality and value of AI-generated art. Unlike traditional artworks, which are typically evaluated based on the skill and talent of the artist, the value of AI-generated art is often tied to the technology used to create it and the algorithms that drive it. This can make it difficult to determine the actual value of an AI-generated artwork and can lead to inconsistencies in pricing and sales.

Another risk associated with AI-based art is the potential for the widespread use of AI to lead to a homogenisation of artistic styles and techniques, resulting in a lack of diversity and originality in the art market. There is also a risk that AI-generated art could be used for unethical purposes, such as creating deep fake images or generating false information.

There is also the issue of privacy and data protection. AI algorithms are trained on vast amounts of data, and it is crucial to ensure that this data is appropriately protected and that the use of AI algorithms does not violate the privacy of individuals. There is also a risk that AI algorithms could perpetuate biases and stereotypes present in the training data, leading to further marginalisation and discrimination.

Despite these challenges and risks, there is the potential for AI-based art to bring new opportunities and excitement to the art market, making it more accessible and allowing for new forms of creative expression. By embracing and carefully managing the challenges and risks associated with AI-based art, it is possible to unlock its full potential and create a new era of artistic innovation.

Closing Thoughts

The impact of AI-based art on the art market and traditional industries is a subject of ongoing debate and discussion. Some believe that AI-based art has the potential to bring new opportunities and excitement to the art market, making it more accessible and allowing for new forms of creative expression. At the same time, there are concerns that the increasing use of AI in art creation could devalue traditional art forms and reduce the importance placed on the artist’s hand and personal touch.

In traditional industries such as advertising, interior design, and product design, the use of AI-based art and images can bring greater efficiency, cost savings, and provide new opportunities. However, there are also concerns that the increasing reliance on AI in these industries could lead to a loss of unique perspectives and human touch and potentially result in a homogenisation of design.

Whether AI-based art will be good or bad for the art market and traditional industries remains to be seen. The impact of AI-based art will likely vary depending on context and use case, but its meteoric rise so far is a marvel in itself.

What Is Liquid Staking?

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

In cryptocurrency, staking has become an increasingly popular way for investors to earn passive income. However, a new concept has emerged that takes staking to the next level: liquid staking. This innovative approach can potentially revolutionise the staking landscape and bring even more people into cryptocurrency.

But what exactly is it? This article will delve into this new concept’s details and explore its benefits and risks. We’ll also look at some of the most promising liquid staking projects in the cryptocurrency sector and discuss their potential for growth and adoption.

Whether you are a seasoned cryptocurrency investor or just starting, understanding the ins and outs of liquid staking is essential for staying ahead of the curve in this rapidly evolving field.

Understanding Liquid Staking

Liquid staking refers to a process that allows investors to stake their assets while still maintaining the ability to use them for other purposes.

The idea has been around for a few years, but it was not until 2020 that the technology to make it a reality emerged. This was primarily due to the development of Ethereum 2.0, which introduced a new staking mechanism that made it possible to stake ETH while still holding a liquid form of the asset, called a stake token.

The technology behind liquid staking involves a complex system of smart contracts and protocols that enable investors to stake their assets and receive rewards in return. These rewards are typically paid out in the form of additional tokens, which can be traded or sold on the open market. The process of staking itself is typically done through a validator node, which is responsible for verifying network transactions and maintaining the blockchain’s integrity.

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As the popularity of liquid staking has grown, so too have the number of projects and platforms that offer this service. Some of the most notable projects include Lido, Rocket Pool, and Ankr, each offering a unique approach with distinct advantages and disadvantages.

Liquid Staking Versus Traditional Staking

Staking is a popular way for cryptocurrency investors to earn passive income by participating in network validation and transaction processing. Traditional staking methods typically involve locking up assets for a set period of time in exchange for staking rewards. However, liquid staking is a newer approach that offers investors more flexibility and control over their assets.

The main difference against traditional staking methods is that it permits investors to maintain control over their assets while still earning staking rewards. With traditional staking, assets are locked up for a set period, which can limit the ability of investors to use or trade those assets.

On the other hand, liquid staking enables investors to stake their assets and receive liquid tokens in return, which can be used for other purposes. This allows investors to continue to trade or use their assets while still earning staking rewards.

Another difference between liquid staking and traditional staking methods is the potential for higher staking rewards. Liquid staking platforms often offer higher rewards than traditional staking, making it more attractive to investors.

Upcoming Projects

There are several platforms and projects that offer liquid staking services, but three of the most well-known are Lido, Rocket Pool, and Ankr.

Lido is a decentralised staking platform that enables users to stake their ETH and receive stETH in return. StETH is a liquid form of ETH that can be traded or used in other ways while still earning staking rewards.

Lido operates through a network of validators that secure the Ethereum network and process transactions. The platform has gained popularity due to its ease of use and high staking rewards, which have consistently been among the highest in the industry.

Rocket Pool is another decentralised staking platform that allows users to stake their ETH and receive rETH in return. Like stETH, rETH is a liquid form of ETH that can be traded or used while still earning staking rewards.

Rocket Pool operates through a network of node operators that provide staking services to users. The platform is designed to be highly scalable and decentralised, focusing on security and transparency.

Ankr is a platform that provides staking services for multiple cryptocurrencies, including ETH, BTC, and DOT. The platform operates through a validator network that secures the blockchain and processes transactions.

Ankr’s liquid staking service enables users to stake their assets and receive liquid staking tokens in return, which can be used for other purposes while still earning staking rewards. Ankr’s platform is designed to be user-friendly and accessible, focusing on ease of use and security.

Overall, these three projects represent some of the most promising and innovative approaches to liquid staking in cryptocurrency. While each platform has its strengths and weaknesses, they all share the goal of making staking more accessible and user-friendly for investors.

The Pros and Cons

As with any new technology, there are both benefits and drawbacks to using liquid staking platforms.

One of the main advantages of liquid staking is that it allows investors to earn staking rewards while still maintaining the ability to use their assets for other purposes. This makes it more flexible than traditional staking, which requires assets to be locked up for a set period. Additionally, liquid staking platforms typically offer higher staking rewards than traditional staking, making it more attractive to investors.

However, there are also risks associated with liquid staking. One of the main concerns is the potential for smart contract vulnerabilities or other security issues that could lead to loss of funds. Another concern is the potential for market volatility, which could lead to significant price fluctuations in the underlying asset.

When it comes to specific platforms, there are also pros and cons to consider. For example, Lido has been praised for its ease of use and high staking rewards, but some users have raised concerns about the centralisation of the platform and the potential for censorship.

Rocket Pool, on the other hand, has been praised for its scalability and decentralisation, but it is still in the early stages of development. It may not be as user-friendly as some other platforms.

The pros and cons of liquid staking will vary depending on the specific platform and the investor’s needs. While risks are involved, many investors have found that the benefits of liquid staking outweigh the potential drawbacks.

Closing Thoughts

Liquid staking is a modern concept in cryptocurrency that allows investors to earn staking rewards while maintaining control over their assets.

Despite the risks noted in this article, the popularity of liquid staking is expected to continue to grow in the coming years. As more investors become interested in earning passive income from cryptocurrency investments, the demand for liquid staking platforms will likely increase.

Additionally, as the technology continues to evolve, we are likely to see even more innovative approaches emerge, with a focus on improving security, scalability, and user experience.

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.

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 Financial Planning Process

Posted on March 3, 2023March 3, 2023 by admin

Financial planning or wealth planning is the process of creating an achievable roadmap for your life’s goals. The financial planning process involves several intensive steps, such as:

Defining your financial goals
Assessing your current financial situation
Developing a realisable plan of action
Implementing that plan
Monitoring and adjusting

Whether saving for retirement, paying off debt, or planning for a major purchase or life event, tailored financial planning means the difference between success and failure. The financial planning process itself must be well-structured, thorough, and comprehensive.

After painting a complete picture of your finances, the capable advisor defines the primary realisable goals relevant to you and how to achieve them. Of course, the ultimate goal remains long-term financial stability and success through the well-ordered mind.

This article explores the five essential steps that are the foundation for a well-ordered financial planning process. In the greater scope of wealth management, financial planning continues to be a complex speciality requiring both experience and compassion.

Source: finexplained

Defining Your Financial Goals

As the critical first step of the financial planning process, it helps you clarify what you want, what’s achievable, and what you want to achieve in the long term. Further, it provides a path forward. There are five considerations to keep in mind.

Short- vs long-term. We must identify whether your goals are short-term (less than one year), medium-term (one to five years), or long-term (more than five years). This aids goal prioritisation and the relevance level of available strategies.

Measurable. After time durations are determined, we examine the specific numbers to which we must hold ourselves accountable. This lets us see where we could have met specific objectives.

Realistic. Ambitious goals come with high risk, while realisable goals enable us to moderate that risk, especially over longer durations. Considering the current income stream, we can identify any weak points and define strategies for remedying them.

Prioritised. An essential question inside any financial planning process: what can we do without, and what is imperative? Pre-paying school fees for possible tax benefits is a high-priority item, while an additional car is not.

Value-aligned. Your values and priorities dictate how you spend your disposable wealth. Otherwise, why hire a financial planner? Your passions and beliefs should enter many of your financial and life goals.

Assessing Your Current Financial Situation

The second step of the financial planning process, it provides an essential baseline for evaluating your forward progress and the necessary plan of action.

Income. We must evaluate the varied sources of income and their levels of consistency. For example, salary payments versus sporadic rental income. Then we factor in taxes, deductions, and all matters relevant to your legal jurisdictions.

Assets. We then need to review the total value of your assets, including savings accounts, retirement accounts, investment portfolios, private funds, real estate, and other sources. Not only does accurately understanding your net worth open up new doors, but it guides the timeline for realising more significant goals.

Liabilities. In short, we must ensure that all unnecessary liabilities are handled with the utmost care and urgency. While more time may be needed for property or loan balances, removing minor matters immediately improves your financial momentum and well-being.

Cash flow. In the final but essential portion, we need to determine the current cash flow picture and how it can be adjusted to meet your financial goals. This is one area where experience and financial acumen becomes critical.

Developing a Realisable Plan of Action

The third step of the financial planning process, developing a realisable action plan, entails producing a concrete strategy for achieving your financial goals.

Set targets. After setting your financial goals through to bequests and the next generations of your family, set your smaller, achievable targets. The overall goal is to know how one achievement feeds into the next.

Identify obstacles. We’re all familiar with the timeless maxim: life happens. So what are the expected and possibly unexpected obstacles you might face in your journey? Your advisor must account for these and structure finances accordingly.

Choose the right strategies. Yes, easier said than done, but this is the substance of any worthwhile financial plan. What are the vital commitments? What are the appropriate structures? How many generations are in the family? Dozens of questions comprise this point.

Monitor your progress. Some ideas feel good in the mind or work until the market or the Fed takes a turn for the worse. Your advisor must always be reachable in the event changes are needed.

Implementing Your Financial Plan

The final step of the initial financial planning process, implementing your plan, must be done carefully and guided by experience. Just as timing investments significantly impacts returns, time also impacts long-term financial plans.

Automate your savings. As an essential “Rich Dad Poor Dad” technique, define your monthly portfolio contribution before spending your regular income. This not only brings mental well-being and confidence, but ensures that your financial plan keeps to your desired goals.

Stay disciplined. By defining your significant purchases for the next five years with a financial advisor, you can avoid unnecessary expenditures or liabilities while limiting debt exposure. In addition, a worthy financial planner gently reminds you of your long-term ambitions whenever appropriate.

Remain ready to re-evaluate. This can be negative or positive. If the real estate or cryptocurrency markets take an upswing, then the immediate cash boon should be included if favourable. If events turn unfavourable, then it’s best to prioritise and move forward.

Closing Thoughts

Define, assess, develop, implement, and then monitor. These five steps comprise a great financial planning process. We say: don’t settle for anything less. This is the baseboard, the bare minimum you should expect.

Financial planning differs from private banking or traditional wealth management because it focuses more on the individual and the long term. It is far more idiosyncratic, considering hopes, fears, desires, and flaws. As personal dreams make the best north star, compassion and an experienced ear make the best financial plan.

Disclaimer: The author of this text, Paul Winder, has a career that spans over 30 years in the financial services sector with emphasis on creating products and services in the international tax treaty and estate planning arena. Paul is Head of Fiduciary Products & Markets at Deltec Bank & Trust and CEO of Deltec Fund Services, 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.

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.