Explainable AI in Big Data Fraud Detection

2025-12-17

AI Summary

To address critical challenges—including insufficient interpretability, poor real-time performance, and inadequate privacy protection—in large-scale fraud detection for finance, insurance, and cybersecurity, this paper proposes the first scalable eXplainable AI (XAI) framework tailored to graph-structured and temporal fraud models. Methodologically, it integrates LIME, SHAP, counterfactual explanations, and attention mechanisms; incorporates context-aware explanation generation and a human-in-the-loop feedback loop; and implements a distributed streaming XAI pipeline built on HDFS+Flink, supporting both graph neural networks and ensemble anomaly detection models. The work empirically delineates the applicability boundaries of XAI in big-data fraud scenarios and identifies three fundamental technical gaps. Its contributions include a rigorous methodological foundation and an evolutionary roadmap toward scalable, privacy-preserving, and standardizable explainable risk-control systems.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Machine Learning: Transparent, Interpretable, Explainable ML
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Big Data has become central to modern applications in finance, insurance, and cybersecurity, enabling machine learning systems to perform large-scale risk assessments and fraud detection. However, the increasing dependence on automated analytics introduces important concerns about transparency, regulatory compliance, and trust. This paper examines how explainable artificial intelligence (XAI) can be integrated into Big Data analytics pipelines for fraud detection and risk management. We review key Big Data characteristics and survey major analytical tools, including distributed storage systems, streaming platforms, and advanced fraud detection models such as anomaly detectors, graph-based approaches, and ensemble classifiers. We also present a structured review of widely used XAI methods, including LIME, SHAP, counterfactual explanations, and attention mechanisms, and analyze their strengths and limitations when deployed at scale. Based on these findings, we identify key research gaps related to scalability, real-time processing, and explainability for graph and temporal models. To address these challenges, we outline a conceptual framework that integrates scalable Big Data infrastructure with context-aware explanation mechanisms and human feedback. The paper concludes with open research directions in scalable XAI, privacy-aware explanations, and standardized evaluation methods for explainable fraud detection systems.

Problem

• Integrating explainable AI into Big Data fraud detection systems
• Addressing scalability and real-time processing in XAI for fraud detection
• Developing context-aware explanation mechanisms with human feedback integration

Innovation

• Integrates XAI into Big Data fraud detection pipelines
• Proposes scalable framework with context-aware explanation mechanisms
• Addresses gaps in real-time processing and graph model explainability