How Banks are Using AI & Big Data to Sidestep Financial Crime Exposure

Concerned about the exposures created by exponential increases in data sources and partner vendors? See how banks are unlocking the power and efficiency of AI and big data to steer clear of financial crimes.

Today's financial institutions have it tough. Compliance requirements are becoming almost impossibly rigorous. Data is growing exponentially, both in terms of sheer volume and number of sources. Financial crime is on the rise and is so hard to combat, it often seems like fighting a multi-headed beast with a ball-point pen.

Consider the stress for financial institutions still relying on manual processes to identify and address suspicious activity. The goal is to avoid doing business with third parties that could pose a risk. The question is, how? As it happens, many banks are turning to advanced analytics techniques like machine learning and AI in order to automate processes and get a leg up on third-party risk.

The aggregated impact of financial crime and corruption is staggering. In fact, the projected total global cost of financial crime compliance across all financial institutions stretches into the hundreds of billions of dollars each year.

The 2022 Global Economic Crime and Fraud Survey by PwC revealed some sobering statistics about the impact of financial crime. Among those companies surveyed:

• 46% reported experiencing fraud, corruption or other economic crimes during the past 24 months
• 18% of larger companies experiencing fraud reported US$50 million or more in financial impact from the most disruptive fraud incident

Worries around financial crime have become a major ESG consideration as well since the profits from such crimes are often used to fuel nefarious things like human trafficking, drug trafficking and forced labor. The knock-on effect for government is significant too; less corporate revenue means less tax revenue, thereby impacting critical public services like education and healthcare.

Financial institutions that get caught up in money laundering or other financial crimes, even indirectly through a third party, face serious consequences in the form of reputational harm, steep regulatory fines and–potentially–prosecution. It’s easy to appreciate the sense of futility they may have around this topic as they do more–and spend more–than ever to combat financial crime but still find themselves struggling to keep up.

Unfortunately, the bad guys are getting better at what they do too. As Paresh Chiney said in a recent Dataversity article, “Today’s white-collar criminals are smarter and more technology-savvy, often exploiting complex and siloed systems and circumventing often archaic fraud- and compliance-monitoring solutions used by corporations and government entities.”

Using AI to Fight Financial Crime

Now more than ever, banks need help in pushing back against financial crimes and those who perpetrate them. With risk alerts flying in left and right, teams need tools that can automate archaic manual processes. With an eye toward the future, it’s time for large financial institutions to leverage advanced data and analytics techniques such as artificial intelligence, machine learning, natural language processing and cognitive automation.

This is the future of preventing, detecting, investigating, and remediating white-collar crime.

Here’s what forward-thinking financial institutions are doing to unlock the benefits of big data.

• They are aggregating data from disparate sources.

Analysing individual data sets is useful but bringing multiple sets together from different sources can take you to the next level in terms of identifying trends or making correlations.

• They are expanding their focus to include less traditional data sources.

By linking internal data sources with non-traditional external data sources like adverse news , practitioners can more effectively cross-reference data and compensate for weak spots in investigations and monitoring.

• They are leveraging rules-based tests to raise red flags.

By creating a data repository containing multiple sources, teams can then apply rules-based tests to surface red flags or anomalies that indicate possible misconduct or compliance issues.

• They are setting up risk scoring systems.

Data scientists can distil results from various data-driven tests to determine and create a composite risk score for an individual or entity.

• They are uncovering anomalies with predictive modelling.

Machine learning and artificial intelligence are driving enormous improvements in anomaly detection. This comes as sources of reliable, real-time data proliferate.

• They are creating interactive visualizations that present data in more compelling ways.

Teams can leverage dynamic visualizations to clarify the most important aspects of huge data sets and use things like geographic mapping and temporal analyses to track or even anticipate high-risk activities.

The best way to fight financial crime? Strong collaboration among banks, regulators and law enforcement and the ruthless application of the latest available technologies. Your efforts are only as good as the data you have in front of you so be sure your company is embracing the latest AI-driven tech.