FINANCIAL INSTITUTIONS – USING BIG DATA ANALYTICS TO REDUCE FRAUD AND MONEY LAUNDERING
RC: How would you characterise the problem of fraud and money laundering in today’s business world? Given that financial institutions are often on the front line, how are they responding?
Clandillon: The pattern of fraud and financial crime in today’s environment is somewhat different than we have seen in the past. Fraud typologies which were previously characterised by individualistic and opportunistic attacks have become much more complex. Up to 80 percent of attacks experienced by financial institutions today are organised and collusive, they often happen cross channel, are difficult to detect with the siloed detection capabilities most commonly employed today and can result in substantially greater losses than previously experienced. Results from a recent study of the problem, which we conducted across 500 financial institutions worldwide, reveals a clear dichotomy in response depending on the size of the institution in question. While 52 percent of larger institutions recognised superior fraud and financial crime prevention as a significant competitive differentiator, only 1 percent of smaller organisations shared that perception. At the same time, smaller institutions are experiencing proportionally higher levels of exposure to direct fraud losses, with 42 percent reporting fraud losses of greater than 10 basis points of revenue compared to less than 10 percent of larger institutions. So, we are seeing a bifurcated response between these two groups. Many of the larger institutions have recognised both the challenge and the opportunity presented by the changing nature of fraud and financial crime, and have either started or completed transformation programmes to effectively address the issue at an enterprise level. However, smaller institutions have, in many cases, not responded as quickly or effectively to the growing threat.
Jul-Sep 2016 Issue
IBM Cognitive Solutions