The adoption of CHIP cards in the US has pushed fraudsters further into retail risk as they focus their efforts on stealing from retailers through Card Not Present schemes and ever increasing use of mules to pick up items in stores. Retailers are scrambling to find better ways to detect fraud.
Our approach to solving retail fraud involves use of Hybrid Analytic techniques and adaptive machine learning where self learning models using multiple sets of internal and external data can adjust to new patterns of fraud quickly. Our approach is to retailers internal data and leverage external consortium data to build the best model.
Retail Fraud and Risk Models are built and deployed on order processing systems or hosted by PointPredictive. Data Scientist and Business Consultants help you deploy the best model to reduce false positives and increase fraud detection