Machine Learning System for Fraud Detection. A Methodological Approach for a Development Platform

Abstract

The democratization and massification use of credit cards lead inexorably to a high number of fraudulent transactions. Generally, the fraud detection is part of the anomaly detection problem. In this field, current approaches and techniques are constantly looking for optimized solutions to detect anomalies. Faced with a massive and growing data volume, these methods are put to the test, and thus lead to a large number of undetected anomalies. Real time fraud detection requires the design and implementation of scalable techniques capable of ingesting and analyzing massive amounts of data continuously. Recent advances in storage, data analytics processing, and open-source solutions open up new perspectives in the anomaly detection field and in particular fraud. In this article, we are interested in the design of a fraud detection system (FDS) based on open-sources Big Data technologies. Thus, a general methodology is proposed based on the formalization, the implementation and the technical design of a platform for fraud detection. The formalization part consists of four layers: distributed storage, data processing, model building, and finally the model evaluation. The implementation part uses Spark distributed data processing system. In particular, we are based on its framework dedicated to machine learning, called MLlib. The technical design part of the platform is based on the latest Big Data technologies such as Hadoop, Yarn, Livy etc.