Real-Time Fraud Detection in Payment Systems Using Kafka and Machine Learning
Sai Prasad Veluru · Journal of recent trends in computer science and engineering. · 2019
The increase of instantaneous digital transactions in the modern digital economy makes actual time fraud detection increasingly essential for maintaining the integrity of their payment systems.Often resulting in money losses & also reputation damage, conventional batch-processing methods are insufficient for actual time identification of dishonest behavior.The dynamic aspect of fraud, marked by always shifting techniques, is a complex problem needing clever, flexible, scalable answers.This work investigates a real-time fraud detection system combining machine learning models tuned for fast and exact anomaly detection with Apache Kafka, a powerful distributed streaming platform.The ingestion, filtering & analysis of high-velocity transactional data find a strong basis in Kafka's actual time processing capabilities.Kafka helps to spot more abnormalities in actual time and construct prediction models that continuously learn from previous patterns when coupled with ML.This mix allows horizontal scalability to control growing data volumes & increases both detection speed & accuracy.Comprising elements for data intake, preprocessing, model inference & alerting, the suggested system design minimizes their human participation and guarantees their complete automation.Experiments show that compared to more conventional rule-based systems, utilizing Kafka with ensemble learning techniques greatly reduces their detection latency and improves accuracy.The strategy helps with model retraining to change with the times for fraud techniques.The article also covers more deployment issues like controlling imbalanced datasets, lowering faulty positives & guaranteeing low-latency responses under load.Deep learning models, edge processing for IoT-based payments & federated learning for inter-institutional fraud intelligence are all included into the approach from a foundation.This study highlights how combining modern streaming infrastructure with advanced algorithms may transform fraud detection from a reactive to a proactive, actual time defensive system in the dynamic field of digital payments.