Developing Machine Learning Models for Real-Time Fraud Detection in Online Transactions

Abstract

This paper offers a detailed discussion of a large–scale, real-time architecture for fraud detection specifically for use in financial organizations to combat fraudulent activities in online transactions. The proposed system in this paper uses big data capabilities and a multi-stage fraud detection pipeline to detect and combat fraudulent activities efficiently. The implemented technologies include Apache Kafka, KSQL, and Spark alongside Isolation Forest algorithm for behavioral analysis of customer transactions. The presentation of the fraud detection pipeline as a series of layers exemplifies how a transaction goes through an exacting sequence of detection algorithms with very little delay and maximum precision. Verification by simulation uses the dataset of more than one hundred million Internet transactions, the performance indicators of which are a rather high F1-score of 91% and a recall rate of 97%. The results stress the advantage of the proposed methodology over conventional techniques, suggesting the possibility of real-time fraud identification. Furthermore, the paper outlines research directions where future work should focus, such as reducing computational complexity and applying deep learning solutions to enhance the detection of new types of fraud.