Privacy-Aware Explainable AI Framework for Multi-Modal Big Data Analytics in Real-Time Payment Fraud Detection and Pharmaceutical Network Intelligence
DOI:
https://doi.org/10.15662/IJRAI.2025.0806815Keywords:
Explainable AI, differential privacy, real-time analytics, payment fraud detection, pharmaceutical network intelligence, multimodal machine learning, counterfactuals, privacy-preserving MLAbstract
Financial fraud in real-time payment systems and intelligence extraction from pharmaceutical networks both rely on fast, accurate, and trustworthy machine learning systems that consume increasingly large and heterogeneous data. This paper proposes a unified, privacy-aware, explainable AI (XAI) framework designed for multi-modal big data analytics that simultaneously addresses latency constraints, regulatory privacy requirements, and the need for interpretable decisions in high-stakes domains. The framework integrates streaming data preprocessing, a modular ensemble of modality-specialized encoders (transactional sequences, device telemetry, text logs, and molecular/biological networks), privacy-preserving computations (differential privacy, secure aggregation, and selective homomorphic operations), and a two-tier explanation system combining local exemplars (counterfactuals and feature attributions) with global concept-based explanations. We evaluate the framework in two case studies: (1) real-time payment fraud detection on a synthetic but realistic streaming dataset reflecting imbalanced classes, latency constraints, and adversarial noise; (2) pharmaceutical network intelligence for drug–target interaction prioritization using multi-omics and literature-mined relations. Results show that the proposed architecture achieves competitive detection and prioritization performance while producing explanations that improve analyst trust and comply with privacy budgets. We conclude with deployment considerations, limitations, and a roadmap for future research bridging privacy, interpretability, and multi-modal real-time analytics.
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