AI’s Role in Helping Banks Fight Fraud and Financial Crimes

Easy money attracts bad actors. 

In today’s increasingly digitized and borderless business landscape, the threat of attack has only grown more pointed.

That’s why it is so important for banks and other financial institutions to ensure that their fraud and financial crime defense programs are sophisticated enough to impede the strategies of today’s bad actors.

“While financial fraud and financial crime are two different things, what they have in common is that they’re both ongoing challenges,” Michael Shearer, chief solutions officer at Hawk AI, told PYMNTS.

Shearer added that Nasdaq CEO and President Adena Friedman recently told the crowd at the World Economic Forum in Davos that there was $3 trillion worth of money derived from financial crimes flowing through the system in 2023, and $500 billion from financial fraud.

“You can debate the figures,” he said, “but there’s no doubt that the problem isn’t going away.”

Financial fraud involves seeking personal gain through deception, misrepresentation or false information. On the other hand, financial crime is a broader term that encompasses activities like money laundering, bribery, corruption and sanctions evasion. Both require financial organizations to deploy effective countermeasures.

“It is essentially an adversarial game; criminals are out to make money, and the financial community needs to curtail that activity. What’s different now is that both sides are armed with some really impressive technology,” Shearer said.

“It is incumbent on everybody in the network to use this technology as effectively as they can and play their part in driving up the difficulty and the risk of abusing the financial system such that it becomes prohibitive and the reward is not worth it for criminals,” he added. 

Turning Easy Targets Into 10-Foot Walls

Traditional approaches to defending against fraud and financial crime are no longer sufficient in today’s rapidly evolving landscape, and Shearer emphasized the importance of understanding the risks associated with products and services, and the geographies in which businesses operate.

“The fundamentals haven’t changed. You need to understand your overall risk position and your appetite, and then divide the steps that your human resources can take and the steps that your machine-led resources can take,” he said.

Automation, artificial intelligence (AI) and machine learning are crucial tools for organizing and connecting data to build a richer context and make better decisions.

“On the automated side, it’s all about data. It’s all about organizing and connecting your data together, understanding the signals that you have so you can build a richer context and make better decisions. But you’ve got to have that information there, and you’ve got to connect it together. That’s step one,” Shearer said. 

Data sharing between organizations, without compromising privacy, is also crucial for training AI systems on a broader pool of data and to gain clearer insights for investigations.

Shearer said that AI and data analytics are essential in the fight against financial crime. AI enables organizations to make discerning and efficient decisions, detect and prevent more crimes, and keep up with the rapidly changing tactics of criminals. It also offers the advantage of easy retraining to adapt to the latest developments — a task can be challenging when relying on legacy methods.

“You can find more crime and prevent more crime” with AI, he said. “You are in an adversarial world, and it’s important to keep up.”

Additionally, he highlighted the need for human vigilance and critical thinking to identify suspicious activities or offers that seem too good to be true.

Trends and Future Innovations in the Fight Against Financial Crime

Managing the costs of anti-money laundering compliance, fraud mitigation and regulatory requirements can be challenging for management teams.

Shearer suggested starting with effectiveness and focusing resources on controls that make a real difference. AI serves as a force multiplier, making machines as good as the best investigators and working 24/7, he noted, while consolidation of financial crime controls can also lead to cost savings and operational efficiencies.

Looking ahead, Shearer predicted that greater data sharing and the harnessing of AI will be the latest tools in the fight against financial crime. Joining up data and leveraging generative AI to find anomalies will provide a significant advantage in detecting and preventing crimes, while the ecosystem will continue to focus on real-time interdiction with governments deploying a more surgical approach to sanctions controls.

“Data sharing is a key part of the solution to global financial crime, and if we do that, it opens up the world of AI because we can train it on a much broader pool of data,” Shearer, who wrote a book on the subject, said.