How to Use AI to Fight Financial Crime

As technology continues to advance at a rapid rate, the fight against financial crime has taken on a new dimension. Traditional methods of detecting and preventing fraud and illicit activities are no longer sufficient in the face of increasingly sophisticated criminals. This is where artificial intelligence (AI) comes in. AI is a powerful tool that is revolutionising the finance industry’s approach to combating financial crime and keeping pace with new criminal tactics.

We’ll run you through some of the most effective ways to harness the power of AI to tackle financial crime and provide you with actionable advice on how to implement AI in financial crime prevention for your business.

What’s in this article?

• The rising challenges in financial crime
• 5 Ways to use AI to fight financial crime
• How to implement AI in financial crime prevention
• The future of financial crime prevention

The rising challenges in financial crime

According to Kroll’s 2023 Fraud and Financial Crime Report, 68% of respondents expect financial crime to increase over the next 12 months, with evolving technology posing one of the largest challenges. In the banking and finance industry, more than £1.2 billion was stolen through fraud in 2022 (which equates to just over £2,300 per minute), highlighting just how severe the issue has become.

Financial crime poses a multifaceted threat to financial institutions. It jeopardises their reputation and client trust, potentially leading to customer attrition and revenue loss. Compliance failures result in substantial regulatory fines and legal consequences. Direct financial losses, operational costs, and the risk of insolvency further compound the impact.

High-profile incidents can also erode market confidence, affecting not only the institution involved but the broader financial landscape. The interconnectedness of global finance means that international financial crimes can have far-reaching consequences. Therefore, combating financial crime is crucial to safeguard the stability, integrity, and trustworthiness of financial institutions within the global economy.

Read more about the top ten fraud risks to watch out for in 2023.

Sonia Jain, Consultant Operations Manager, FDM in Toronto and in-house AI and machine learning expert believes –

‘Financial crime involves illegal activities that aim at acquiring financial gain. Financial crime can have serious societal consequences which can adversely affect the shape of global economy. With the help of AI, we can leverage its ability not just to combat the crime but also to monitor the financial activities in real time to prohibit the very occurrence of it.’

5 Ways to use AI to fight financial crime

There are numerous ways AI can be used to fight financial crime, including:

1. Data analysis and pattern recognition
2. Real-time monitoring
3. Behavioural analysis
4. Natural Language Processing (NLP)
5. Machine learning for predictive analysis

1. Data analysis and pattern recognition

One of the primary strengths of AI is its ability to analyse vast amounts of data at lightning speed. Financial institutions deal with massive datasets daily, making it challenging to identify suspicious activities manually. AI algorithms excel at identifying patterns and anomalies within these data, helping to flag potentially fraudulent transactions or activities that might otherwise go unnoticed.

2. Real-time monitoring

AI-powered systems play a pivotal role in the battle against financial crime by enabling real-time monitoring of financial transactions. This capability is instrumental in swiftly identifying and addressing potential threats. Suspicious activities, such as unusual transaction patterns, can be automatically flagged by AI algorithms, triggering immediate investigation.

By detecting and responding to illicit activities promptly, financial institutions can mitigate risks before they escalate and prevent crime from occurring in the first instance. The real-time nature of AI-based monitoring not only enhances security but also serves as a deterrent to potential criminals, as they are more likely to be caught in the act, thus reducing the overall occurrence of financial crime.

3. Behavioural analysis

AI’s ability to construct detailed user profiles from transaction history and behaviour is a game-changer in financial crime detection. By establishing baseline behaviour for each customer, AI can promptly identify deviations from these norms. For instance, if a user typically conducts small, domestic transactions but suddenly initiates large withdrawals or transfers to high-risk countries, the system will trigger alerts for immediate scrutiny.

This proactive approach enables financial institutions to swiftly respond to potential threats and investigate suspicious activities, enhancing their capacity to prevent money laundering, fraud, and other illicit financial behaviours while safeguarding the integrity of their operations and the interests of their customers.

4. Natural Language Processing (NLP)

Financial criminals frequently communicate through digital channels, leaving behind a wealth of text-based data that can be a treasure trove of evidence. Natural Language Processing (NLP) algorithms are instrumental in sifting through this textual data, scanning emails, chat logs, and other messages to identify suspicious or incriminating conversations.

These algorithms can detect keywords, phrases, or patterns associated with financial crimes, helping investigators uncover hidden connections, illegal activities, and nefarious intentions. NLP’s ability to parse and understand human language allows financial institutions and law enforcement agencies to stay ahead of criminals who attempt to mask their activities in written communication.

5. Machine learning for predictive analysis

AI’s capacity to learn from historical financial crime data is a strategic advantage in the fight against illicit activities. By training on past cases, AI can construct predictive models that identify emerging threats and evolving criminal tactics. These models continually evolve and adapt, staying one step ahead of wrongdoers who seek to exploit vulnerabilities in financial systems. As AI systems become more attuned to nuanced patterns and emerging trends, they offer a proactive defence mechanism, helping financial institutions anticipate and tackle financial crime.

How to implement AI in financial crime prevention

To harness the full power of AI in the fight against financial crime, financial institutions should consider the following five steps:

1. Data integration: Ensure that all relevant data sources are integrated into a unified system for analysis. This includes transaction data, customer profiles, and external data sources that provide context. The synergy of these data streams enhances the accuracy of AI-driven analyses and enables a comprehensive view of potential risks.

2. Choosing the appropriate AI tools: Choose the right AI tools and algorithms that align with your institution’s specific needs and objectives. Customisation is key to maximising effectiveness. Some examples include Google AML AI, Automation Anywhere’s Intelligent Automation, and NICE Actimize.

3. Continuous training: AI models require ongoing training to adapt to evolving threats. Regularly update and refine your AI systems to stay ahead of financial criminals.

4. Consider regulatory compliance: Ensure that your AI systems comply with all relevant financial regulations and data privacy laws. Transparency and accountability are crucial when implementing AI in finance.

5. Collaboration: Collaborate with other financial institutions and law enforcement agencies to share insights, best practices, and intelligence. A collective effort is often more effective in combating financial crime.

The future of financial crime prevention

Ultimately, AI is not a silver bullet, although it can significantly enhance the financial industry’s ability to combat financial crime. By leveraging AI, financial institutions can stay one step ahead in the ongoing battle against financial crime, protecting both their assets and the global economy. As a result, 99% of UK firms plan to reassess their risks and 69% are planning to increase compliance headcount as a response too.

This figure is representative of the high demand for financial crime professionals, especially in the KYC and AML sector. Hiring the right staff is invaluable! Skilled professionals bring the necessary expertise to understand the nuances of evolving criminal tactics, navigate complex regulatory landscapes, and identify potential vulnerabilities within the institution’s operations. However, these experts are also integral in supporting AI systems. They play a pivotal role in training AI models, fine-tuning algorithms, and interpreting results. Human insights are essential for refining AI’s accuracy and relevance. The synergy of human expertise and AI-driven tools creates a robust defence against financial crime, where human intuition and analytical skills complement the speed and scale of AI systems, resulting in a more resilient and effective strategy.

At FDM, we offer AML-KYC PODs services, which are designed to help financial businesses mitigate crime risks. The FDM PODs provide a proactive model to efficiently scale your operations, with highly-skilled full-time employees who are up-to-date with best practices and who work in agile teams. So, if you’re one of the 69% of businesses that are planning to increase compliance headcount, our PODs could be just the solution for you, providing a long-term solution that evolves with the quick-changing landscape.

Learn more about how to future-proof your solution to financial crime risk or get in touch for more information about FDM’s AML-KYC PODs.