Compliance Digital transformation Smart decisions Smart data

How data and analytics, AI and machine learning can help fight money laundering and terrorist financing

24 Mar 2020

As criminals become more adept at manipulating financial institutions and shielding their illicit activities from oversight, combating money laundering has become an urgent topic on the international agenda.


“We must close off all opportunities for criminals and terrorists to abuse our financial system and threaten the security of Europeans,” Frans Timmermans, First Vice President of the European Commission, recently warned. Věra Jourová, Vice President of the European Commission for Values and Transparency, echoed this sentiment: “We don’t want to see any weak link point in the EU that criminals could exploit. Recent scandals have shown that Member States should treat this as a matter of urgency.”

This heightened urgency is likely here to stay, explains Erica Olivius, product manager at Dun & Bradstreet. “What led to these stricter regulations are the terror attacks that have targeted many countries in the 21st century. Thanks to the anti-money laundering directives, Panama directives and international organizations such as FATF, the regulations will continue to tighten in order to achieve transparency when it comes to financial transactions between individuals, businesses and countries.”

Of course, closing off said criminal opportunities is easier said than done. Or at least it used to be. According to Tania Cholet, Certified Compliance officer and senior product manager for AML compliance solutions in the Dun & Bradstreet group, detecting fraudulent activity has traditionally been a very labor-intensive process. “It is a real challenge for banks and financial institutions to in real time identify and block suspicious transactions, while processing extremely high volumes of legitimate ones. Financial institutions typically spend over 90% of the time investigating false hits.”

This is something that Dun & Bradstreet’s tools in the field of AI, automation and machine learning can streamline. “Smart data and analytics are our key competences,” Tania Cholet notes. By helping customers in recognizing patterns, connecting the dots, uncovering hidden risks, and predicting future behavior, bad decisions can decrease dramatically. Using AI and machine learning, Dun & Bradstreet is able to reduce false positives and increase the percentage of alerts that actually result in Suspicious Activity Reports (SAR). This is something that countless companies can attest to: “Dun & Bradstreet AML and fraud prevention solutions are used by hundreds of financial institutions and other obliged entities in Europe,” Cholet says. “Automation of customer onboarding helps establish processes that are standardized, independent of user, department or country, but on the other hand easily adjustable to reflect the local situation. This is extremely important for compliance officers who set compliance policies, code of conduct and give guidance company-wide, including subsidiaries located around the globe. Business practices in Serbia are obviously quite different than in Sweden.”

The fight against money laundering and financing of terrorism places a heavy strain on the financial sector. To stop criminals from being able to use either legitimate institutions or fraudulent companies to further their own interests, access to the right expertise, data and experience is imperative. But thanks to rapid advances in technology, organizations that are quick to adopt the latest advances in fraud prevention and AML stand an excellent chance of staying ahead of the curve – by ensuring compliance while offloading the costly, time-consuming task of screening for fraud to automated tools.

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"The fact that financial institutions around the world have started to invest heavily in these fields will greatly benefit their compliance work. This is the future.”