Fraud Prevention technology has made enormous strides from advances in computing speeds (high-performance analytics), machine learning and other forms of artificial intelligence (AI). Fraud touches every area of our lives; it raises the price we pay for goods and services, squanders tax money, pulls resources from innovation and even costs human lives.

Fraud Detection in today’s world involves a comprehensive approach to match data points with activities to find what is abnormal. Fraudsters have developed sophisticated tactics, so it’s essential to stay on top of these changing approaches of gaming the system.

The Fraud Detection and Prevention Technology that you choose should be able to learn from complex data patterns. It should use sophisticated decision models to better manage false positives and detect network relationships to see a holistic view of the activity of fraudsters and criminals. Combining machine learning methods – such as deep learningneural networks, extreme gradient boosting and vector machines – as well as proven methods such as logistic regression, self-organizing maps, random forests and ensembles – has proven to be far more accurate and effective than approaches based on rules.

How Fraud Prevention Works

Fraud detection and prevention is not a static process. There’s no starting and ending point. Rather, it’s an ongoing cycle involving monitoring, detection, decisions, case management and learning to feed improvements in detection back into the system. Organizations should strive to continually learn from incidents of fraud and incorporate the results into future monitoring and detection processes. This requires an enterprisewide analytics life cycle approach. 

Your goals may involve fraud detection, compliance or security. As technologies like artificial intelligence and machine learning have become more prevalent, the next generation of technologies is automating manual processes associated with combining large data sets and employing behavioral analytics