The Risk of De-Risking

Given the current climate of regulation over extended banking relationships and service, which include correspondent banking and MSB (money services businesses), many financial institutions have significant concerns that reputational risk and financial penalties might well outweigh any potential positive returns. As a result, a number of banks have taken to employing de-risking strategies.

Absent additional transparency, as well as more efficient, effective and cost-effective practices and methodologies for identifying suspicious patterns and activities that indicate money laundering in third-party relationships, financial institutions have been forced to go to extraordinary lengths to mitigate the risks presented by these “third-party” relationships. Specifically, these banks have opted to end relationships with jurisdictions which they deem high risk, or to exit entire lines of business.

The problem with this strategy is that while erring on the side of caution there is additional regulatory scrutiny based upon the potential for abandonment of jurisdictions and business lines. Regulators have been extremely focused on the impact of financial institutions’ abilities to leave market segments and, even, entire countries without access, due to de-risking strategies.

In addition to this additional regulatory scrutiny, a bank may be walking away from perfectly legitimate business opportunities to its financial detriment. In employing a de-risking strategy, a bank can also create a perception among potentially profitable customers that it is a difficult bank to do business with – a bank that really can’t distinguish between honest business owners and outright criminals. So, while it may be understandable for banks in the current regulatory climate to minimize their risks, in fact, by de-risking they may actually be placing their institutions at another type of risk – namely being uncompetitive.

In general, banks have done a good job documenting customer transactions and accounts. However, this information has not provided detailed, end-client risk insight. At its most basic level, artificial intelligence (AI) and machine learning technology can leverage a bank’s own data not only to prevent, but also solve criminal activities.

AI enables financial institutions to go well beyond client self-reporting to make KYC and KYCC determinations. Through the use of advanced algorithms and greater reliance on independent and external sources of information, AI can be harnessed to define specific risk factors for affiliated institutions or customers and predict future risk trends.

With AI and machine learning employed, banks and other financial institutions will be able to efficiently and effectively distinguish legitimate clients from bad actors, enabling them to provide services to legitimate clients and emerging economies, while also choking off the flows of illicit funds around the globe.