Digital Themes


Financial crime (FinCrime) refers to the unlawful acquiring of financial assets, and the term applies to various types of financial crime.

These activities include money laundering; funding for organized crime groups and terrorist organizations; cybercrime; insider trading; the use of shell banks and shell corporations to avoid taxes; insurance fraud; and political bribery. Key legislation, like the Bribery Act 2010 (U.K.) and the Foreign Corrupt Practices Act (1977, U.S.), help governments and corporations prosecute individuals and groups.

In early 2024, Nasdaq CEO Adena Friedman noted that $3.1 trillion USD in illicit funds flowed through the global financial system in 2023. Nasdaq’s report also acknowledged that the true cost of financial crime evades discovery: "The true scale cannot be accurately measured in numbers, given how much crime goes unreported by victims and undetected in the current financial system.”

In the fight against FinCrime, corporations continue to invest in various technologies and, more recently, artificial intelligence (AI) tools. Additionally, anti-money laundering (AML) software supports detection and prevention: Companies can identify money laundering risks before the malicious activity occurs, and they can also streamline their investigations.

Napier, an end-to-end intelligent compliance platform, identified three main elements of an anti-money laundering framework: Know Your Customer (KYC) procedures, which verify customers’ identities; transaction monitoring supported by intelligent analysis; and Suspicious Activity Reports (SARs).

Companies that fail to address criminal activities can suffer severely, as the fallout from a FinCrime episode is often devastating. However, a strong financial crime risk management framework can stop bad actors in their tracks.

Business benefits of FinCrime monitoring include the following:

  • Real-time detection of threats
  • Improved predictions surrounding malicious behavior and intent
  • Pattern recognition achieved with the help of machine learning (ML) models
  • Reduced alert fatigue and notification fatigue for customers
  • Alignment with AML regulatory bodies
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