The Future of Finance: AI Meets Tokenization

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Federal Reserve Governor Christopher J. Waller recently gave a speech titled: “Innovations and the Future of Finance,” explicitly focusing on two emerging technologies, namely Tokenization and Artificial Intelligence (AI). He said, “the world is rapidly changing, and we need to be open to the potential benefits of innovation.”

After ChatGPT sparked a technological arms-race among the big tech companies, it is not surprising that Waller decided to focus on AI as one of the emerging technologies to bring advanced innovative solutions to the financial industry. It might, though, come as a surprise that the Fed is appreciating the benefits of blockchain technology, the technology that underlines tokenization (and cryptocurrencies like Bitcoin). But the Fed has acknowledged the benefits of blockchain technology, and like other central banks around the world, has been experimenting with Central Bank Digital Currency (CBDC) through several initiatives.

Specifically, the Fed recognizes the benefits of CBDC: “it could provide households and businesses a convenient, electronic form of central bank money, with the safety and liquidity that would entail; give entrepreneurs a platform on which to create new financial products and services; support faster and cheaper payments (including cross-border payments); and expand consumer access to the financial system.”

In his speech, Waller explored the benefits of AI and tokenization to the financial industry and global financial markets. Indeed, both technologies are very powerful and have great potential to make financial products, services, and markets more efficient, inclusive with boundless opportunities.

But when AI meets Tokenization, the integration of the two supercharges their implementations and applicability – and not to mention opportunities – exponentially.

In early August, Microsoft teamed up with Aptos Labs, a blockchain developer, to combine the power of AI and blockchain technology for financial institutions and drive Web3 into the mainstream.

“By fusing Aptos Labs' technology with the Microsoft Azure Open AI Service capabilities, we aim to democratize the use of blockchain enabling users to seamlessly onboard to Web3 and innovators to develop new exciting decentralized applications using AI," said Rashmi Misra, GM, AI and emerging technologies, Microsoft.

Before we explore how this “supercharged” technology works – the combination of AI and tokenization -- and can impact the future of finance, let’s first define and explain each technology, and provide examples on how they have been and could be implemented, separately, in the financial industry and financial markets.

What is Tokenization?

In the financial context, the tokenization of assets refers to the process of issuing a digital token that runs on a blockchain. This token is a digital representation of an asset – tangible or intangible – and its value is based on the value of the asset it represents, like the process of traditional securitization, but with a digital twist.

Tangible assets could be real estate, stocks, or art, which could be tokenized. Similarly, intangible assets could be voting rights, loyalty points or patents. For example, Securitize is a platform that provides both a primary and secondary market for tokenized products. On their platform, you can invest in tokenized assets, such as, KKR tokenized Health Care Strategic Growth Fund II (“HCSG II”) and tokenized S&P index funds, enabling institutional private market strategies more accessible to individual investors.

It should be noted that tokenized assets are securities and abide by securities laws. Securitize is a fully regulated and compliant platform, regulated by both the U.S. Securities and Exchange Commission (SEC) and FINRA. Therefore, the Fed has been comfortable acknowledging and discussing the benefits of tokenization.

What is Artificial Intelligent (AI)?

Artificial intelligence is pretty much just what it sounds like – the practice of getting machines to mimic human intelligence to perform tasks. You’ve probably interacted with AI even if you don’t realize it – voice assistants like Siri and Alexa are founded on AI technology, as are customer service chatbots that pop up to help you navigate websites.

Machine learning is a type of artificial intelligence. Through machine learning, practitioners develop artificial intelligence models that can “learn” from data patterns without human direction. The unfathomable volume and complexity of data that is now being generated has increased the potential of machine learning, as well as the need for it.

The benefits of tokenization and real-life use cases

Blockchain, the underlining technology of tokenization, can offer fast or near-real time transfers on a 24/7 basis, which, among other things, allows parties precise control over settlement times and in some cases, can enhance efficiencies and reduce liquidity risks.

Another advantage of tokenized assets is that they are programmable and have smart contract functionality. Smart contracts are self-executing programs, which can be programmed to execute predefined actions once certain conditions are met. When assets are tokenized, smart contracts can be used to construct and execute transactions involving the asset. When the smart contract is activated, the transaction proceeds automatically if the specified conditions are met. Thus, smart contracts enable the execution of transactions without the participation of intermediaries.

Smart contracts may allow for what is called "atomic settlement." Rather than relying on each party to separately carry out its leg of the transaction, smart contracts can effectively combine two or more legs of a transaction into a single unified "atomic" act that is executed by the smart contract. This could be a robust way to achieve delivery-versus-payment (DVP) and payment-versus-payment (PVP) functionality, such that one leg of a transaction settles if and only if the other leg settles as well.

Atomic settlement is useful because it can mitigate settlement and counterparty credit risks – it ensures that the buyer will not pay if the seller does not deliver and vice versa.

This could be quite beneficial for Repurchase (Repo) agreements. Parties to Repo transactions may have more flexibility as to when the transactions settle, which in turn has the potential to enable capital and liquidity efficiency. The atomic settlement functionality may serve as a way to mitigate risk – the repo "seller" can have confidence that it will receive the specified loan amount in exchange for the collateral it conveys while the repo "buyer" knows it will receive the specified collateral. Financial institutions such as JPMorgan and Broadridge have been experimenting with tokenized repo agreements.

There are potential risks associated with tokenization and the use of smart contracts. For example, smart contracts might have bugs and potential cyber vulnerabilities; and instantaneous settlement raises its own set of risks. This is where AI can mitigate those risks and supercharge tokenization.

AI benefits and implementations

AI offers several key benefits in financial services, including improved operations, reduced costs, enhanced fraud detection, automated regulatory compliance, risk mitigation and faster decision making. By leveraging the power of AI, financial institutions can gain a competitive edge, achieve operational efficiencies, and make more informed decisions in a rapidly evolving financial landscape.

Artificial Intelligence has already been applied in financial services in a variety of ways, from risk management to fraud detection to customer service to trading analysis. AI-powered document automation and processing technology has also been explored to assist financial institutions streamline their operations by automating repetitive manual tasks such as data entry, document sorting and verification.

With the recent advancement of generative AI, financial institutions are currently developing a new generation of AI-powered applications, namely GPT-powered applications – utilizing GPT in products such as chatbots and robo advisors.

AI meets Tokenization

When AI integrates with Tokenization, it opens a new set of opportunities and implementations:

Improve smart contract coding

Coding smart contracts can be complex and error-prone. Even small mistakes in the code can have significant consequences. This is where Generative Pre-trained Transformer (GPT) – an AI language model (used in ChatGPT and the like), can prove useful.

GPT can be used to streamline the process of contract creation by assisting in the development and testing of smart contract codes. Developers can write smart contract code in natural language, which can help reduce errors and improve the efficiency of the coding process.

For example, it can help developers quickly identify potential issues with their code by analyzing natural language inputs and suggesting improvements or corrections, helping them write more efficient and error-free code, reducing the likelihood of bugs and other issues. This can help improve the accuracy of the code and reduce the risk of costly errors, which will increase users’ trust in tokenized assets.

Enhance the accuracy and efficiency of smart contract execution

AI can be used to automate the process of contract execution. By analyzing and interpreting the data generated by smart contracts, AI can help identify potential issues or errors in the contract code, alerting developers to take corrective action. This can reduce the time and effort required to manually monitor smart contracts, improving the speed and accuracy of contract execution.

In addition, AI can optimize smart contract performance by analyzing contract data and identifying patterns and trends. This can help improve the efficiency of contract execution by identifying areas where the contract can be optimized, such as reducing smart contact execution’s fees or improving execution speed.

Enhance blockchain security and monitoring

AI can help prevent and mitigate security threats in several ways. For example, AI can analyze network traffic and detect unusual activity, such as suspicious transactions or attempted hacks. It can also monitor social media and other sources to identify potential threats, such as discussions of vulnerabilities or attacks. Additionally, AI can identify patterns of behavior that may indicate an insider threat and alert administrators to take action.

Investors and financial institutions can create algorithms to call for specific data or be alerted when specific data values or data actions occur – such as when an asset valuation falls below a certain threshold. In addition, market surveillance engines, such as SMARTs, developed by Nasdaq (and used by the crypto exchange Gemini) can be developed utilizing AI and smart contracts to monitor all data (not merely transactions data) and alert for suspicious transactions. These alerts would be sent in real time and may trigger the execution of a smart contact to halt or block a transaction.

Aid in analyzing and interpreting large amounts of data

Streamline Tokenization process

In the tokenization process AI can help to automate and streamline many of the tasks involved in creating and managing digital tokens. For example, AI algorithms can be used to assess the value of an asset, considering factors such as market trends, historical data, and expert opinions. This can help to ensure that the digital tokens accurately represent the underlying asset and provide a fair and transparent market for investors.

Monitoring tokenized asset risk

AI can be used to monitor and manage the risks associated with tokenized assets. As with any investment, there are always risks involved, and AI can help to identify and mitigate these risks in real-time. This can help build trust in the tokenization process and execution of transactions and encourage more investors to participate in the market.

Improve decision-making

AI can help investors and financial institutions make better-informed decisions by identifying patterns, trends, and correlations that may go unnoticed by human analysts, or simply take too much time to find. AI algorithms can analyze market data, news articles, social media sentiment, and other relevant information to generate real-time insights and predictions on markets and investment opportunities.

Portfolio optimization

By utilizing advanced algorithms, AI can identify optimal investment strategies based on an investor’s risk tolerance, investment goals, and market conditions. It can analyze historical market data, perform risk assessments, and make dynamic adjustments to portfolios, ensuring they remain aligned with the investor’s objectives. This already exists in traditional investments and is continually evolving at an exponential rate.

Market transparency and best price execution (i.e., Reg NMS)

Regulation National Market System (NMS) promotes free market transparency and ensures that investors receive the best price when their order is executed, by removing the ability to have orders executed at a worse price.

Tokenized platforms are global and there could be hundreds of them. AI could assist in aggregating all bid/ask quotes from all tokenization platforms in real time and identifying the best bid/ask to be executed in real time and on which platform – a smart contract can then execute the trade at the best price. This information can be fully transparent and shared with both investors and regulators.

The integration of AI and tokenization has the potential to supercharge financial markets and the global economy. AI’s data analysis capabilities can provide real-time insights and assist in portfolio optimization, while blockchain networks enhance transparency and automation. Together, they enable more efficient and trustworthy financial services, paving the way for a future where decentralized and AI-powered systems play a central role in reducing cost, improving market efficiencies, optimizing decision-making and chiefly democratizing financial markets and enabling economic and social equity for all participants.

The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.

Merav Ozair, PhD

Dr. Merav Ozair is a global leading expert on Web3 technologies, with a background of a data scientist and a quant strategist. She has in-depth knowledge and experience in global financial markets and their market microstructure.

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