Blockchain + AI in finance: how opposites attract

Blockchain and artificial intelligence (AI) may seem like something out of a sci-fi movie, but they are actually transforming the financial services industry before our eyes. But what exactly are blockchain and AI?

When we think of blockchain, many of us think of cryptocurrencies like Bitcoin and Ethereum, but that’s like saying the internet is a search engine. Reduced to its core, the blockchain is a digital record of transactions. Blockchain technology decentralizes data storage so that data is not owned or managed by a single governing body.

When we think of artificial intelligence, we might imagine the Terminator movies and intelligent robots with human behaviors. In practice, AI uses computer systems to perform tasks that usually require human intelligence, such as making predictions. Machine learning is a subset of AI that uses computational and statistical techniques that allow computer systems to use data to “learn” how to perform a task.

Change finance

AI and blockchain are changing the landscape of the financial industry. AI can process data exponentially faster than humans. It enables financial institutions to mine large amounts of data to extract more insights, automate repetitive tasks, and accelerate innovation.

Blockchain is also disrupting the financial industry with more transparency and access to financial markets through decentralized finance (DeFi) and smart contracts. The more Decentralized Autonomous Organizations (DAOs) are launched, the more we will be forced to rethink existing centralized financial systems.

Combine forces

What do blockchain and AI have in common? Not much, actually. They are both at the forefront of current trends in innovation and technology and have many use cases, but otherwise blockchain and AI act in entirely different ways. Blockchain acts as secure storage and is decentralized, tamper-proof and transparent. AI, on the other hand, processes huge amounts of data and is generally centralized, scalable and opaque.

While blockchain struggles with scalability and efficiency, AI struggles with transparency and privacy, which makes the two technologies a perfect match, as each can address the weaknesses of the other. Blockchain provides trust, privacy, and accountability to AI, while AI provides scalability, efficiency, and security.

Use case

Establish trust

One of the difficulties that AI faces is accountability, which brings out distrust of AI results. The European Commission has proposed a set of regulations where reliability is at the heart, and to trust AI we need to be able to explain how AI algorithms work so that humans understand it and have confidence in it. the accuracy of AI outputs and results.

Blockchain’s immutable digital records can be a way to offer insight into the AI ​​framework and model for addressing the challenge of data transparency and integrity.

Blockchain’s immutable digital records can be a way to offer insight into the AI ​​framework and model for addressing the challenge of data transparency and integrity. Using blockchain can also improve data security and integrity by storing and distributing AI with an audit trail built into the blockchain. Having this audit trail ensures that the data used to train the models as well as the models themselves retain their integrity.

Another example of building trust in AI using blockchain goes back to the foundations of blockchain. AI technology is usually centralized, which leads to user distrust. Many people today don’t trust what government and big business do with their data. The challenge is to be able to provide assurance to users that the technology has not overstepped the mark and violated privacy.

Enter blockchain: a decentralized data storage that can act as an audit trail and provide that trust to users to show them exactly how companies and other centralized organizations are using their data. Additionally, blockchain ledgers can be used as a digital data rights management system so that individuals can own their data and provide the terms and conditions under which businesses and organizations are allowed to use their data.

Automation and efficiency

Automation is not a new concept for AI, but combining AI and blockchain can enable synergies in both scale and efficiency. Blockchain technology can eliminate unnecessary third parties from multi-party transactions, ultimately accelerating transaction speed and increasing transaction efficiency. Reducing friction between these transactions allows individuals to own their data, and blockchain ensures the security of the transaction process.

The combination of AI and blockchain can enable synergies in both scale and efficiency.

For example, blockchain technology allows us to create decentralized exchanges where the exchange of assets does not need to depend on a central authority or third party to approve the exchange. All transactions are recorded on the blockchain and exchanges are written directly to the blockchain. This type of order book eliminates the need for a central authority due to its open source nature and transaction transparency that anyone can audit.

AI can provide scale and automate transaction valuation processes. With the exponential increase in the amount of data, processing and consuming data without the help of AI will become impossible.

Fraud detection

When the blockchain is involved, the secure transaction cannot be tampered with and ensures that every transaction written complies with the rules predefined by the blockchain (either programmed into the platform or added as smart contracts). Blockchain security can help reduce the possibility of fraudulent transactions and improve fraud detection. AI overlay on top of transactions can detect anomalies in the blockchain at scale. Fraud detection involves sifting through huge amounts of data looking for unusual patterns.

AI overlay on top of transactions can detect anomalies in the blockchain at scale.

For example, if a client has an account that invests the same amount in an ETF every month, and suddenly one month the amount the client is investing is 10 times the normal amount, that transaction would be classified as suspicious and potentially fraudulent. This would trigger fraud detection algorithms in banks. AI and blockchain technology can be combined to detect suspicious transactions and activities and stop them at the source.

Smart Contracts: The Fall and Rise of the DAO

The first DAO was launched in 2016 as an investor-led venture capital fund. It was launched after a crowdfunding campaign via a token sale and quickly became one of the biggest crowdfunding campaigns in history. The goal was to provide a new decentralized business model based on the Ethereum blockchain with open source code. Financial transactions and DAO rules would be encoded on a blockchain to remove the need for a central governing authority, which in theory should reduce costs and provide more control and access for investors.

The first DAO ultimately failed because hackers exploited a security flaw in the code that transferred one-third of the Ether into a separate account under the DAO smart contract. Smart contracts are programs stored on the blockchain and executed when pre-determined conditions are met, typically used to execute an agreement without the need for an intermediary.

The problem with DAO was its inability to react quickly to unforeseen circumstances. In addition to this, the United States Securities and Exchange Commission (SEC) has ruled that the tokens offered by the DAO are securities and subject to federal securities law. This meant that the DAO was subject to the same regulations as all other centrally governed organizations.

Today, billions of dollars are invested in smart contracts in the Ethereum ecosystem (the top three projects alone hold over $26 billion). The failure of the first DAO paved the way for improvements in smart contract design and security and a healthier ecosystem. Now, smart contracts can be edited and voted on by the community/DAO, and smart contracts have emerged as one of the most effective and efficient data management solutions. Adding AI to this can help overcome data management challenges, including automation. For example, AI models could be embedded in smart contracts running on a blockchain, and the AI ​​model could make recommendations based on contract data, be it an expiration date , payments or even finding the most efficient shipping routes.

What does this mean for FinTech?

FinTech as we know it today is highly specialized and centralized. Blockchain and AI can be enablers for FinTech 2.0 by focusing on holistic solutions with increased transaction speeds, transparency and security. Additionally, DeFi can mean a larger pool of investors as more people gain access to financial markets. The more investors there are, the more data there will be that would be impossible to process without AI. Blockchain provides the basis for smart contracts to improve transparency and data management, while AI can be harnessed to scale processes, speed up transactions and extract insights from large volumes of data. AI and blockchain may not completely reshape the financial industry as we know it, but they will most certainly change the way we interact with financial data.

This blog post is for informational purposes only. The information in this blog post does not constitute legal, tax or investment advice. FactSet does not endorse or recommend any investment and assumes no responsibility for any consequences related directly or indirectly to any action or inaction taken based on the information in this article.