DYNAMIC SELF-LEARNING SYSTEM FOR AUTOMATICALLY CREATING NEW RULES FOR DETECTING ORGANIZATIONAL FRAUD

A fraud detection system that applies scoring models to process transactions by scoring them and sidelines potential fraudulent transactions is provided. Those transactions which are flagged by this first process are then further processed to reduce false positives by scoring them via a second model. Those meeting a predetermined threshold score are then sidelined for further review. This iterative process recalibrates the parameters underlying the scores over time. These parameters are fed into an algorithmic model. Those transactions sidelined after undergoing the aforementioned models are then autonomously processed by a similarity matching algorithm. In such cases, where a transaction has been manually cleared as a false positive previously, similar transactions are given the benefit of the prior clearance. Less benefit is accorded to similar transactions with the passage of time. The fraud detection system predicts the probability of high risk fraudulent transactions. Models are created using supervised machine learning.