Fighting Financial Crimes with Artificial Intelligence

Hollywood has long tainted the image of artificial intelligence (AI) with its movies and television dramas about the hyper-intelligent machines taking over the world. However, the truth is that AI today is doing the world a lot of good, from helping us dial our smartphones hands free to allowing us to fly our aircrafts safely.

Another key area in which artificial intelligence is proving to be highly beneficial is in the prediction, detection, and prevention of criminal activities such as money laundering and identity fraud. The fact of the matter is that criminals are often more nimble than the financial organizations they victimize, which allows them to develop surprisingly agile and clever practices that take advantage of the vulnerabilities of these organizations. In this article, we’ll discuss some of the ways artificial intelligence is helping security people address this challenge.

The Scourge of Financial Crimes

Financial crimes is described as an intentional act of deceit that involves financial transactions for one’s own personal use and benefit. Often, they are committed by individuals who are highly knowledgeable about how these transactions are carried out, taking advantage of their skills or credibility to facilitate their criminal activity. Examples of financial crimes include identity theft, bank fraud, credit card fraud, scams, embezzlement, and money laundering.

With financial fraud, it is often the case that the criminals intend to blend in with actual customers who go through these transactions every day. The goal is to fly under the radar by obfuscating the data. They try to mimic the qualities of good customers, or they use automated techniques in order to facilitate cyber-attacks that make their jobs easier.

For example, they can automate the malware and phishing attacks to infect large numbers of computers and devices. Cybercriminals can then use these devices to steal customers’ personal and financial information, or they can be employed as botnets to carry out distributed denial of service (DDoS) attacks against banks. Such DDoS attacks can serve as a method of distraction, confusing IT and security personnel while acts of fraud or outright stealing of financial assets are committed in the background.

Another problematic area of regulation for banks and other financial institutions is anti-money laundering (AML). And because this particular challenge is often tied to another, more nefarious criminal activity—terrorist financing—banks have become more and more vigilant with the financial transactions that have taken place in their systems in recent years. Moreover, non-compliance to AML regulations or failing to have adequate processes to deter such activities can result in huge regulatory fines worth millions or even billions of dollars. However, while deterring, detecting, and reporting financial crimes and terrorist activities are at the top of banks’ list of priorities, they also have to protect the privacy of legitimate, law-abiding customers.

Artificial Intelligence and Machine Learning to the Rescue

When it comes to anti-money laundering, then, it is important to strike a delicate balance between signal and noise in order to help maintain the integrity of banking systems. One key area of efficiencies is the reduction of false positives in the detection of money laundering activities. False positives can run as high as 95% or more for many banks, but by using the right AML artificial intelligence solutions, a bank can reduce the rate of false positives to as low as 25% without affecting the number of suspicious activity reports.

As for the problem of fraud, identity theft, and malware, financial institutions are also increasingly using AI to analyze the behaviors of both the criminals and the legitimate customers. These days, the key to detecting malware and suspicious activities lies in the analysis of both big data and small data, which is only possible with the help of self-learning technologies. This is because the landscape of financial crimes change rapidly, with criminals developing new techniques on a consistent basis.

By adopting and deploying software solutions that use machine learning—a subset of AI—financial institutions will be able to quickly identify malware and analyze abnormal user and network behaviors. One way to do this is by automatically assembling self-similar groups of customers and creating accurate predictions off these groupings. This way, security people and investigative teams are afforded enhanced resolution on each individual case.

Machine learning is also adept identifying changing patterns and suggesting updates to segments and rankings based on all these information. Because of this, delicate patterns that suggest nascent behavior are identified immediately for security personnel to consider.

Indeed, artificial intelligence and machine learning are changing the ways we tackle financial crimes. With agile and efficient AI tools on their hands, financial institutions are able to deploy a vast array of defenses that will help them protect their customers and business interests.

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