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Could Big Data Analytics And Deep Learning Have Detected India’s Largest Banking Fraud?

Understanding banking fraud

It is no secret that mitigating fraud is a top priority for banks. According to the Association of Certified Fraud Examiners, businesses lose more than US$3.5 trillion globally each year to fraud. The problem is pervasive across the financial industry including the insurance industry and is becoming more prevalent and sophisticated. As customers conduct more banking online across a greater variety of channels, devices and geographies, there are more opportunities and more “surface area” for fraud to occur. Adding to the problem, fraudsters are becoming more creative and technologically savvy. They’re also using advanced technologies like machine learning and new schemes to defraud banks. Conventional approaches to fraud detection and remediation are necessary but they remain effective to a point, as conventional tools simply cannot effectively and economically process what is known as big data. “Big data analytics” can enable companies to deploy and integrate rich and new data types to produce new and more sophisticated analyses against the fraudsters and continuously improve the loop of legacy approaches to the war on fraud. In a few test cases, these analytics are extremely effective at exposing not just the fraudsters themselves, but their networks and the people, places and processes they touch or will touch…

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