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Dec 10, 2024 · The article demonstrates how financial institutions can effectively combat sophisticated fraud patterns while maintaining operational efficiency through detailed analysis …
This article aims to explore an innovative architectural approach that combines real-time data streaming with generative AI, specifically Retrieval Augmented Generation (RAG), to create a …
The focus was on leveraging AI and machine learning algorithms to detect fraudulent activities in real-time, thereby minimizing financial risks and enhancing customer trust.
This paper examines how streaming data processing frameworks and machine learning models can be combined to create robust real-time anomaly detection systems for banking transactions.
In this study, we presented an end-to-end real-time architecture using behavioral analysis for digital transactions fraud detection centered on combining the isolation forest algorithm and …
Jul 11, 2024 · In this case study, we demonstrate how a feature store can enable the development and deployment of machine learning models for real-time fraud risk detection in financial …
This repository contains a complete Kafka and Spark-based streaming pipeline for real-time fraud detection. The pipeline simulates realistic user behavior and transaction events and analyzes …
After working with several leading data teams to build these streaming anti-fraud systems from scratch, we want to share what we’ve learned about reference architectures for real-time fraud …
Jul 3, 2025 · Discover how top banks use transformers, RAGs, GANs, and federated learning for real-time fraud detection, with real-world case studies.
Aug 25, 2019 · This case study shows how a major US bank created a new streaming data architecture, with SingleStore at its core, for fraud detection 'on the swipe,' applying machine …
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