AI has already benefitted the public directly with a variety of consumer-side applications, but behind the scenes, its rise has also led to a revolution in regtech. Through big data analytics, it can help financial institutions or regulators find problems more easily, and even detect problems in advance. Nevertheless, it also gives rise to a variety of problems, which should be prevented, studied, and solved in advance in order to make full use of the potential of new technology.

Systemic changes will make regulation more complex

Countries are actively integrating and consolidating compliance, fraud, and cyber threat systems for risk management. They are establishing comprehensive statistical monitoring systems for cyber finance, and adopting comprehensive and accurate real-time risk alert systems to protect consumers and guide development of the industry.

Following the global financial crisis in 2008, many large international financial institutions shut down or were taken over by the government, resulting in a crisis of market confidence, which in turn triggered an economic downturn. Subsequently, financial regulators put forward reform proposals and created financial operating systems order to prevent a recurrence. The changes in these systems have imposed additional compliance requirements on financial institutions. Due to differences in accounting standards and political and banking systems, the reforms in Europe and the United States have not reached consensus on uniform applicability of laws and regulations, which has made compliance more complicated for multinational financial institutions, and led to the development of regtech.

Regtech, an amalgamation of regulation and technology, utilizes technology to collect information on the regulatory systems and requirements of various regulators, providing analysis and management tools to automatically assist financial institutions to reduce operational risks. These tools include legal/regulatory gap analysis, global compliance, information management, compliance, regulatory reporting, transaction reporting, training, activity monitoring, risk data warehousing, case management, and more.

Powerful AI and machine learning can help make supervision smarter

As AI becomes more widespread, it is transforming compliance and risk management operations. It facilitates the use of powerful algorithms and machine learning to automate and optimize regulatory processes. The Artificial Intelligence in Regtech Global Market Report 2024 forecasts the market for AI in regtech to grow rapidly, from $1.37 billion in 2023 to $1.89 billion in 2024, and growing at a compound annual growth rate (CAGR) of 36.9% to reach US$6.64 billion by 2028. Factors contributing to its growth include increasing compliance costs, growing demand for automation, increased data, and a focus on enhanced risk management. North America was the largest market for AI regtech in 2023.

One of the most important aspects of AI regtech is fraud detection. Advanced algorithms and machine learning techniques can be utilized to examine a wide range of data from a variety of sources. According to the US Federal Bureau of Investigation, the significant increase in cryptocurrency investment fraud targeting individuals aged 30-49, with losses rising from $907 million in 2021 to $2.57 billion in 2022, has been a key catalyst for the growth of AI regtech market.

Fintech financing trends

Source: PitchBook

AI has been used to solve complex problems in finance and has great potential to reshape regulatory processes. Regulators are using it to more effectively enforce complex rules. Its ability to analyze large data sets and identify pattern could be game-changing – for example, it could help identify irregularities or discrepancies in transactions which may indicate a breach, fraud or other illegal activity. It can also enable faster and more effective supervision, with more accurate detection of patterns, anomalies and potential compliance violations.

A responsible AI regulatory framework

AI also can automate much of the regulatory reporting process, making it more accurate and less labor-intensive, providing data in a clear and understandable manner, increasing transparency, and facilitating decision-making by internal stakeholders and regulators. Nevertheless, it also brings considerable challenges, such as a lack of transparency and risk of bias and discrimination, with downstream issues such as data privacy, security, and liability. One potential solution may be a regulatory framework for responsible AI, such as explainable AI with regulatory sandboxes, etc.

Regulators such as the European Central Bank (ECB) aim to identify problems by measuring the consistency, timeliness and accuracy of regulatory submissions over time. The Monetary Authority of Singapore (MAS) is developing a practical new system to detect and prevent money laundering using generative AI. The UK's Financial Conduct Authority (FCA) is using generative AI to develop a new system to create synthetic cyberattacks, which are then used to test the security of financial institutions’ IT systems and processes. The Australian Prudential Regulation Authority (APRA) is also using generative AI to develop a new system to assess the risk levels of financial institutions.

Gen-AI holds great promise for regtech by streamlining data management. As the financial industry continues to evolve, it can become a strategic advantage in compliance, reducing costs and mitigating risk. However, it is critical that the implementation reflects a commitment to ethics, privacy, and responsibility.

Individual financial institutions are also now making aggressive use of AI-driven analytics tools capable of processing big data to detect anomalies, patterns and potential risks in transactions, improving the accuracy of risk assessments, and enabling proactive monitoring, allowing organizations to stay ahead of regulatory requirements. AI enables continuous transaction monitoring and surveillance to detect anomalous activity in real time which could signal money laundering, fraud, or other compliance issues, enabling Fis to act quickly to explore potential issues and proactively manage risk. AI-powered regtech improves risk management by automating compliance to help banks comply with regulatory standards and anticipate and resolve hazards before they occur, reducing the risk of costly penalties and reputational damage.

Synergies between regtech and AI

Two further areas which have received recent attention include blockchain technology and decentralized finance. Blockchain is increasingly being adopted in the regtech space due to its potential to enhance transparency and security. One example is the use of blockchain in the identity verification process to comply with Know Your Customer (KYC) and Anti-Money Laundering (AML) regulations. Furthermore, the rapid growth in decentralized finance (DeFi) and adoption of cryptocurrencies poses regulatory challenges which require innovative regtech solutions.

Regtech and AI can create powerful synergies which can optimize operations along the value chain, increase efficiency, improve resource allocation, and achieve full chain efficiency. Involvement of AI in regtech is expected to increase significantly as AI develops and matures. Advances in natural language processing, deep learning, and explainable AI will enhance the ability of AI-driven regtech systems to enable more complex compliance and risk management processes. The integration of AI with other emerging technologies such as blockchain and the Internet of Things (IoT) will open up new avenues for secure and transparent regulatory reporting, identity verification, and fraud detection.

AI is transforming regtech by improving compliance and risk management in the financial sector. It will undoubtedly become an integral part of the ability of the financial sector to cope with a complex regulatory environment. At the same time, however, ethical considerations will be critical to ensuring that it is used to its full potential to create a trustworthy and resilient financial system.