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AI: The driving force for preventing, detecting, and tackling financial crime. Or is it?

  • Writer: Ryan Weatherley
    Ryan Weatherley
  • May 9, 2023
  • 9 min read

Financial crime is a growing problem in today's society, and the use of artificial intelligence (AI) has become increasingly prevalent in combating it. AI technologies such as machine learning and natural language processing can assist financial institutions in detecting and preventing financial crimes such as money laundering, fraud, and terrorism financing. However, AI can also be used by criminals to commit financial crimes, and the use of AI in financial crime is a rapidly evolving area that presents many challenges to law enforcement agencies. This article will explore the future of AI in financial crime, including the use of AI in prevention and how AI can be used by criminals, and the challenges it presents to law enforcement agencies.

The Use of AI in Preventing Financial Crime


The use of AI in preventing financial crime has become increasingly common in recent years. Machine learning algorithms, for example, can detect patterns and anomalies in data that may indicate a financial crime. These algorithms can learn from data sets and adapt their behavior accordingly, improving their accuracy over time. Natural language processing (NLP) can also analyse text data, such as emails or chat messages, to identify suspicious activity. The use of AI in financial crime prevention has the potential to significantly reduce the incidence of financial crime, and many financial institutions are investing in AI technologies to improve their anti-money laundering (AML) and counter-terrorism financing (CTF) capabilities.


According to a report by ResearchAndMarkets.com, the global market for AI in AML and CTF is expected to grow from $1.3 billion in 2020 to $4.8 billion by 2025, at a compound annual growth rate of 29.1% (ResearchAndMarkets.com, 2021). This growth is being driven by increasing regulatory pressure on financial institutions to prevent financial crime, as well as the increasing sophistication of financial criminals. Financial institutions are also looking to AI to reduce the number of false positives generated by traditional rule-based AML systems. False positives occur when legitimate transactions are flagged as suspicious, leading to unnecessary investigations and costs for financial institutions. AI-based systems can reduce the number of false positives by accurately identifying suspicious transactions.


The use of AI in financial crime prevention is not without its challenges, however. One of the main challenges is the explain ability of AI-based systems. Traditional rule-based AML systems are based on a set of predefined rules that are easy to understand and explain. In contrast, AI-based systems can be highly complex, making it difficult to understand how they arrive at their decisions. This lack of transparency can make it challenging to identify and rectify errors or biases in the system. To address this issue, the Financial Stability Board (FSB) has developed a set of explain ability guidelines for the use of AI in financial institutions (Financial Stability Board, 2021).


Another challenge is the potential for AI-based systems to be vulnerable to adversarial attacks. Adversarial attacks involve making small changes to input data, such as an image or a transaction record, to fool the AI system into making incorrect decisions. Adversarial attacks can be difficult to detect, and financial criminals may use them to circumvent AI-based AML systems.


Case Law


The use of AI in financial crime prevention has already been tested in the courts. In 2020, the UK Financial Conduct Authority (FCA) fined Commerzbank AG £37.8 million for failing to put adequate anti-money laundering controls in place. The FCA found that Commerzbank AG had relied too heavily on manual processes, which led to a backlog of alerts and a failure to adequately investigate suspicious transactions. The FCA also found that the bank had failed to take reasonable steps to ensure that its AML systems were effective, including failing to adequately test its AI-based transaction monitoring system (Financial Conduct Authority, 2020). This case highlights the importance of ensuring that AI-based AML systems are properly tested and validated before being implemented. Financial institutions must also ensure that they have adequate oversight and governance of their AI systems to ensure that they are effective in preventing financial crime.



The Use of AI by Criminals in Financial Crime


While AI can be used to prevent financial crime, it can also be used by criminals to commit financial crimes. Criminals can use AI to automate their activities and make it more difficult for law enforcement agencies to detect and prevent financial crime. For example, criminals can use AI to generate fraudulent documents, such as fake IDs or invoices, that are difficult to detect by humans. Criminals can also use AI to analyze large data sets to identify vulnerabilities in financial institutions' systems that can be exploited.

The use of AI by criminals in financial crime is a rapidly evolving area that presents many challenges to law enforcement agencies. Criminals are constantly adapting their techniques to evade detection, and AI technologies are making it easier for them to do so. The use of AI by criminals in financial crime is also likely to increase as AI technologies become more widely available and easier to use.

According to a report by the Royal United Services Institute (RUSI), the use of AI by criminals in financial crime is a growing trend (RUSI, 2019). The report found that criminals are using AI to automate their activities, such as money laundering and fraud, and to generate fraudulent documents that are difficult to detect by humans. The report also found that criminals are using AI to analyze large data sets to identify vulnerabilities in financial institutions' systems that can be exploited.

The use of AI by criminals in financial crime is not without its challenges, however. One of the main challenges is the difficulty in identifying and prosecuting those responsible for AI-enabled financial crime. Criminals can use AI to cover their tracks and make it difficult for law enforcement agencies to identify them. For example, criminals can use AI to generate fake identities or to hide their IP addresses, making it difficult to trace their activities.

Another challenge is the potential for AI to amplify existing biases in financial crime. AI-based systems learn from data sets, and if those data sets contain biases, the AI system may perpetuate those biases. This can lead to unfair outcomes, such as the over-representation of certain groups in financial crime investigations.

Case Law

The use of AI by criminals in financial crime has also been tested in the courts. In 2019, a UK court sentenced a man to four years in prison for using AI to generate fake invoices (BBC News, 2019). The man had used AI to create hundreds of fake invoices that were difficult to detect by humans. The case highlights the need for financial institutions to be vigilant in detecting and preventing AI-enabled financial crime.


Challenges for Law Enforcement Agencies


The use of AI in financial crime prevention and by criminals presents many challenges to law enforcement agencies. One of the main challenges is the difficulty in identifying and prosecuting those responsible for AI-enabled financial crime. As mentioned earlier, criminals can use AI to cover their tracks and make it difficult for law enforcement agencies to identify them. This requires law enforcement agencies to develop new techniques and tools for detecting and investigating AI-enabled financial crime.


Another challenge is the need for law enforcement agencies to keep up with rapidly evolving AI technologies. Criminals are constantly adapting their techniques to evade detection, and law enforcement agencies must stay ahead of the curve to be effective in preventing and detecting AI-enabled financial crime. This requires significant investment in training and development for law enforcement personnel.


A third challenge is the potential for AI to amplify existing biases in financial crime investigations. As mentioned earlier, AI-based systems learn from data sets, and if those data sets contain biases, the AI system may perpetuate those biases. This can lead to unfair outcomes and discrimination, particularly against certain groups that may already be over-represented in financial crime investigations. Law enforcement agencies must take steps to address these biases and ensure that their AI-based systems are fair and unbiased.


Recommendations for the Future of AI in Financial Crime Prevention


The use of AI in financial crime prevention is still in its early stages, and there is much work to be done to ensure that AI-based systems are effective in preventing financial crime while also protecting against the potential for AI-enabled financial crime by criminals. Below are some recommendations for the future of AI in financial crime prevention:


  • Increased Investment in AI Research and Development: Financial institutions and law enforcement agencies should invest in research and development to ensure that AI-based systems are effective in preventing financial crime. This investment should include the development of new AI-based technologies and the testing and validation of these technologies before they are implemented.

  • Collaboration Between Financial Institutions and Law Enforcement Agencies: Collaboration between financial institutions and law enforcement agencies is essential in the fight against financial crime. Financial institutions should share data with law enforcement agencies to help identify and prevent financial crime, and law enforcement agencies should provide guidance and support to financial institutions on the use of AI in financial crime prevention.

  • Oversight and Governance of AI-based Systems: Financial institutions must ensure that they have adequate oversight and governance of their AI-based systems to ensure that they are effective in preventing financial crime. This includes regular testing and validation of the AI-based systems and the development of clear policies and procedures for the use of AI in financial crime prevention.

  • Addressing Bias in AI-based Systems: Financial institutions and law enforcement agencies must take steps to address bias in AI-based systems to ensure that they are fair and unbiased. This includes ensuring that the data sets used to train AI-based systems are diverse and representative and that the systems are regularly tested for bias.

  • Investment in Training and Development for Law Enforcement Personnel: Law enforcement agencies must invest in training and development for their personnel to ensure that they are equipped with the skills and knowledge needed to investigate and prevent AI-enabled financial crime.

Conclusion

The use of AI in financial crime prevention is a rapidly evolving area that presents many challenges to financial institutions and law enforcement agencies. While AI can be used to prevent financial crime, it can also be used by criminals to commit financial crimes. It is essential that financial institutions and law enforcement agencies work together to address these challenges and ensure that AI-based systems are effective in preventing financial crime while also protecting against the potential for AI-enabled financial crime by criminals. This requires significant investment in research and development, collaboration between financial institutions and law enforcement agencies, oversight and governance of AI-based systems, addressing bias in AI-based systems, and investment in training and development for law enforcement personnel.


It is important to note that the use of AI in financial crime prevention is not a panacea, and it should be viewed as a tool to augment human decision-making rather than replace it entirely. Ultimately, the success of AI-based systems in preventing financial crime will depend on their effectiveness, fairness, and ethical considerations.


The use of AI in financial crime prevention is rapidly growing and can have a significant impact on reducing the occurrence of financial crimes. AI is particularly useful in detecting patterns and anomalies that may be missed by humans, and its ability to learn and adapt makes it a powerful tool in identifying and preventing financial crimes. However, it is important to consider the ethical and legal implications of using AI in financial crime prevention, particularly with regard to privacy and data protection.

Furthermore, AI can also be used by criminals to perpetrate financial crimes, as demonstrated by the recent rise in AI-enabled cyberattacks. This highlights the need for continuous development and improvement of AI technologies and regulations to stay ahead of criminal activities.

In conclusion, the future of AI in financial crime prevention is promising but complex. While it has the potential to greatly benefit society by reducing financial crime, there are also significant ethical and legal challenges that must be addressed. With continued research, development, and collaboration between industry, governments, and academia, AI can be used as a powerful tool in combating financial crime while upholding ethical and legal standards.


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