Home Research The Case for Putting Artificial Intelligence at the Heart of Digitally Robust Financial Regulation

The Case for Putting Artificial Intelligence at the Heart of Digitally Robust Financial Regulation

by surfsidefinance
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One example is the $800 billion Paycheck Protection Program, established by Congress in 2020 to provide government-backed loans to small businesses affected by the epidemic. More than 15 percent of PPP “loans” – or $76 billion – contain evidence of fraud, according to a study released last year. Many of the cases involved loan applicants who used false identities. Imagine if the lenders submitting loan guarantee applications or the small business management systems that are reviewing them had sophisticated artificial intelligence-based systems that could have flagged suspicious behavior. They could have detected misrepresentations and prevented fraudulent loans, thereby protecting taxpayer dollars and ensuring that their precious funds helped small businesses in need, not funded thieves.


Two examples can be found in the war in Ukraine. The Russian invasion triggered a whole new set of sanctions against Russian oligarchs who hid their wealth in shell companies and scrambled to move money undetected. Financial institutions must screen accounts and transactions to identify the transactions of sanctioned entities. What if they and law enforcement agencies such as the Financial Crimes Enforcement Network (FinCEN) had the artificial intelligence analytics to extract and aggregate data from all areas of global transactions and find patterns that reveal the activities of sanctioned parties? Unfortunately, most financial institutions and government agencies do not have these tools today.

A second example comes from the fact that the rapid exodus of millions of refugees has drawn traffickers to the country’s borders in an attempt to trap desperate women and children and sell them into slavery for work and sexual services. Banks are required by law to maintain anti-money laundering (AML) systems to detect and report financial flows that may indicate human trafficking and other crimes, but these systems are mostly simulated and notoriously ineffective. The United Nations Office on Drugs and Crime estimates that less than 1 percent of financial crimes are caught. Artificial intelligence-driven compliance systems would have a better chance of flagging criminal groups targeting Ukraine. Moreover, if such systems had been in effect in recent years, the human trafficking trade might not have flourished. As things stand, an estimated 40 million people are held in modern-day human slavery, a quarter of whom are children.

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