Using Machine Learning Techniques for Fraud Detection: Machine prediction or Anomaly detection or Behavioral analytics Saksoft

Every now and then, there is a fraudulent activity masquerading as the original – no exception to the business world. And if fraud detection is about dealing with smokes and mirrors with barely any room for errors, then Machine learning and AI have grown into reckoned technology forces giving enterprises the hope of clearing the smoke and smashing the mirror. Given the most complex of a situation – to decide whether it is a fraud being perpetrated or an original one being conducted– and the need to combat even the most-modern fraud tricks, organizations across Banking, Fintech, Insurance, Retail and other industries are using machine learning techniques for fraud detection to unearth subtle fraud patterns, detect anomalies as well as suspicious behaviors, and prevent fraud. In using machine learning techniques for fraud detection, what sets the prerogative for using machine prediction or anomaly detection or behavioral analytics? Now, when we are entrusted with this fraud detection and prevention task, data would be the first stop to frame the solution strategy.