As technology continues to advance at a rapid rate, financial crime has taken on a new dimension, posing a multifaceted threat to financial institutions, writes Sonia Jain, Consultant Operations Manager at FDM Group…
According to Kroll’s 2023 Fraud and Financial Crime Report, 68 per cent of respondents expect financial crime to increase over the next 12 months, with evolving technology posing one of the largest challenges.
Not only does it jeopardise businesses’ reputation and client trust, but financial crime can also result in direct financial losses, operational costs, and the risk of insolvency.
Traditional methods of detecting and preventing fraud and illicit activities are no longer sufficient in the face of increasingly sophisticated criminals, but 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.
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 the 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.
Here are five ways businesses can use AI to fight financial crime:
- 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 an 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.
- 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.
- 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.
- 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.
- 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.
