Bank Systems & Technology: Cognizant’s Vice-President of Enterprise Information Management Writes About Leveraging Big Data to Revolutionize Fraud Detection

“Banking is a massive, complex industry with many facets -- retail banks, credit card lenders, managed investing, risk management -- all of whom approach fraud detection and prevention differently,” writes Karthik Krishnamurthy. “The sheer volume of loss attributed to fraud is pressuring financial services companies to devise solutions to prevent and identify fraud, while continuing to provide a positive and customized experience for an increasingly sophisticated customer.”

According to Krishnamurthy, in order to achieve a more accurate and less intrusive fraud detection system, banks and financial service institutions are increasingly investing to perfect the algorithms and data analytics technology used to spot and combat fraud. “This technology uses large volumes of data being generated at a high velocity to increase confidence and accuracy in fraud detection,” he notes.

“As a highly customer-centric industry, banks and financial service providers need to make sure they are attacking fraud strategically and not disrupting their customers’ banking experience,” he writes. “The key to accurate and non-disruptive fraud detection is to implement emerging technology that allows banks to gain a holistic view of customers. This view of fraud detection uses data available from a variety of sources -- mobile data, along with social data from Facebook and Twitter -- and uses it to distinguish fraudulent activity from normal activity.”

Highlighting a few possible approaches, Krishnamurthy cites using social data to ‘cluster’ information and machine learning, which takes place when agile systems are configured to learn from one another. “Banking and financial services institutions are increasingly aware of the benefits of leveraging big data in fraud detection, but often struggle with where to get started,” he says and lists a few best practices for developing a strategy: Start with small and specific uses for big data, ensure you’re working with high quality data, know your regulatory environment, and ensure IT and business units are collaborating.

“Technology will continue to advance and offer new strategies for optimizing fraud detection. Financial services organizations should ensure their internal teams are informed about trends and developments, or are partnering with experts who effectively help them stay ahead of the technology curve,” he writes. “Big data allows financial services firms to greatly enhance the speed of fraud detection and prediction using massive amounts of data from a hybrid of sources: point of sale, social media, customer databases, and external sources from data vendors.”

Krishnamurthy adds, “In the future, big data will help deal with the globalization of fraud itself. The challenges that the financial services industry faces with fraud have an enormous impact on customer service and the fight to lower fraud loss. Real-time analytics and machine learning built on top of a big data repository represent the solution platform for fraud detection and predictive/preventable fraud while maintaining a high level of customer satisfaction.”

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