How Big Data is Being Used to Prevent Financial Crime

Fighting all types of financial crime is one of the biggest challenges facing financial institutions, says Mike Urban, the Director of Financial Crime Risk Management Solutions, Fiserv. And Big Data is changing the game.

Preventing financial crime and money laundering is both challenging and costly. Up until very recently banks have been dependent on traditional techniques such as transaction monitoring systems. Now Big Data analytics can help public and private enterprises look for behavioural patterns and establish which positions have the highest average losses and which employees might have the most incentive and opportunity to commit fraud.

Henry Ristuccia, a partner at Deloitte & Touche LLP, told the Wall Street Journal that Big Data can help to highlight potential areas of risk and allow them to become more targeted in their fight against financial crime. He proceeded to say that this allows the emphasis to be on prevention and early detection, which helps to avert scandal for companies.

It isn’t just scandal that is the problem though, A former counter-fraud detector Jim Gee claims that fraud is costing the NHS £5 billion a year, while IBM estimate that $3.5 trillion are lost each year from fraud and financial crime. It is therefore unsurprising that in 2015 the government announced a technology partnership with IBM worth £313 million that aims to boost big data research in the UK. Furthermore, it has been estimated that by 2016, 25% of large global companies will have adopted Big Data analytics for at least one security or fraud detection use case. Big Data is already being used to reduce crimes such as burglaries, presumably the trend will continue in financial crime.