Rules performing well but could always be better
Fraudsters are relentless in their attempts to beat existing bank systems. This is why banks need to be constantly upping their game and proactively improve their fraud detection capabilities. With a host of flexible and configurable rules already in place, overlaying with machine learning can help improve detection rates. This is true particularly where data sets are large and identification of fraudulent payments, rather than false positives, needs a greater level of sophistication.
Increasing the sophistication of detection
Business rules can produce payment scores, improve fraud detection and limit the number of fraudulent payments. Scoring rules can be refined and improved by the payments team to enable the bank to respond quickly to limit new types of fraudulent activity. However, fraudsters keep changing how they game the system and, for them, it is a numbers game. Time to up the ante once more.
Deploying artificial intelligence for the right task
Using a combination of machine learning and a rules-based approach, we are able to extend the level of automation and improve detection rates for potentially fraudulent payments. Xceptor's native machine learning puts the power of artificial intelligence in the hands of operational users. This is powerful as it takes away the need to source scarce data scientists and also enables loyal, valuable staff to skill up. The machine learning model eats up the vast amounts of data being produced by various systems, makes sense of it and constantly learns. This leads to improved confidence scores and a constantly evolving fraud detection system.