Rahul Vats

Generative AI in Credit Card Fraud Detection: A Comprehensive Overview

Abstract:

This work presents an overview of how Generative Artificial Intelligence (Generative AI) can transform credit card fraud detection in the modern digital economy. As financial transactions increasingly move to digital and mobile platforms, fraudsters exploit system vulnerabilities at unprecedented scale, creating significant financial losses and eroding consumer trust. Traditional fraud detection methods—ranging from rule-based systems to supervised machine learning and deep learning models—have improved detection accuracy over time but remain fundamentally reactive and dependent on historical fraud data. These limitations lead to high false-positive rates, delayed detection of new fraud patterns, and challenges caused by limited labeled fraud datasets.
To address these challenges, the study introduces the Generative AI Fraud Detection Framework (GAI-FDF), a novel approach that shifts fraud detection from reactive analysis to proactive simulation and adaptation. The framework integrates synthetic fraud data generation using Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) with adaptive anomaly detection models. These models learn representations of legitimate transaction behavior and identify deviations that may signal emerging fraud patterns. By generating realistic synthetic fraud scenarios, the system overcomes data scarcity and improves model training across rare fraud categories.
The framework also incorporates real-time detection pipelines and multi-level risk scoring mechanisms, combining anomaly scores, behavioral baselines, and fraud pattern recognition to enhance accuracy while maintaining regulatory explain ability. Case studies from financial institutions demonstrate measurable benefits, including significant reductions in false positives, faster adaptation to new fraud techniques, and substantial financial savings within months of deployment.
Overall, the proposed framework highlights how generative AI enables continuous learning, proactive threat simulation, and cross-institutional intelligence sharing, fundamentally shifting fraud detection from static rule-based systems toward adaptive, collaborative defense mechanisms capable of evolving alongside increasingly sophisticated fraud tactics.

Profile:

Rahul Vats is a distinguished Data and AI engineering leader with over 15 years of experience architecting and delivering AI-driven solutions for Fortune 500 companies. He specializes in Generative AI, Microsoft Copilot development, Retrieval-Augmented Generation (RAG) architectures, and Agentic AI—enabling enterprises to achieve scalable, intelligent, and cost-efficient solutions.
Currently a Senior Lead (Manager) at Capital One, Rahul leads a team of AI engineers delivering transformative solutions that integrate Generative AI with enterprise grade data systems. His strategic initiatives have advanced Microsoft Copilot integrations, enhancing productivity through intuitive, AI-driven experiences. Rahul's specializes in RAG architecture, building scalable knowledge retrieval pipelines for enterprise search, customer support automation, and decision support systems. His modular frameworks have enhanced system scalability, contextual relevance, and streamlined information retrieval for enterprise users. His recent work in Agentic AI involves designing intelligent agents that autonomously manage complex workflows, adapt dynamically to user intent, and integrate with enterprise APIs—driving automation and process optimization.
Rahul’s outstanding contributions to AI innovation were recognized with the prestigious Titan Innovation Gold Award (2025) for Best Artificial Intelligence Technology Innovation and AI-Driven Enterprise Transformation and Innovation, underscoring his impact on enterprise AI advancements. Previously at Microsoft, Rahul enhanced cloud data pipelines and implemented AIdriven solutions using Azure services. He successfully lead global teams, aligned technical design with business objectives, and drove Agile practices —accelerating time-to-market while improving system reliability.
Rahul's deep technical expertise, coupled with his leadership in AI engineering, has established him as a trusted partner in driving innovation, empowering teams, and delivering impactful business outcomes.