By Mike Urban, Director, Financial Crime Risk Management, Fiserv
Fighting payments fraud has become one of the biggest challenges for financial organisations across the globe. In recent years there has been an explosion in both access channels and real-time payment methods meaning that monitoring financial transactions and ensuring that they are secure and legitimate has become increasingly challenging.
The introduction of the Faster Payments Scheme in the UK in 2008 unsurprisingly led to increases in online banking fraud losses. Enter “Big Data.” Big Data analytic tools will play a critical role in helping institutions adapt their existing crime-fighting strategies to meet the rapidly evolving techniques of fraudsters.
Financial crime has become an arms race between banks, risk managers and criminals. Real-time analytics to detect crime have now become essential as fraudsters are using rapidly evolving attack scenarios, exploiting multi-channel vulnerabilities and compromising payments systems on an expanded scale. The explosion of access channels in payments– through online, mobile, apps and increasing transaction volumes have escalated the rate of false positives from standard fraud detection rules.
Strategies to combat financial crime today are, in many ways, similar to the strategies first employed by financial institutions when digital payments were first introduced many years ago. Predictive analytics have long been a powerful weapon in the fight against criminals, and variations of other financial crime fighting techniques – behaviour monitoring, network analysis, pattern recognition and profiling – have been key components of banks’ toolkits for decades. But today, Big Data is changing the game.
While banks have been employing these strategies for decades, Big Data has enabled banks to deploy real-time analytics on a massive scale to meet these growing threats. Financial fraudsters are becoming increasingly sophisticated and daring, raising the potential for serious disruption to the entire financial system. Financial institutions must have effective, real-time crime detection analytics in place.
To meet the financial crime risks that could accompany real-time payments, institutions must implement a financial crime risk management strategy that employs on a multi-faceted analytic approach to detecting and mitigating financial crime. A range of techniques are used to detect financial crime but the core of any analytics system is built around behavioural profiling. By profiling and tracking the behaviour of an individual account from initial client onboarding, through to transaction monitoring and customer management, it becomes possible to detect unusual account activity. A combination of behavioural profiling, real-time detection scenarios and predictive analytics provides the most accurate results. Big Data enables financial institutions to provide these services on a scale that simply wasn’t possible five years ago.
Today’s financial criminals are becoming ever more sophisticated and varied in their attack methodologies. As new forms of payments emerge so, too, do emerging forms of financial crime. The best financial crime detection systems, will employ automated, Big Data analytics which use behavioural profiling and scenario event detection to flag anomalies in real-time.
