Explainable AI-Driven Secure Multi-Modal Analytics for Financial Fraud Detection and Cyber-Enabled Pharmaceutical Network Analysis

Abstract

The rapid growth of digital financial transactions and interconnected pharmaceutical supply chains has increased exposure to fraud, cyberattacks, and complex systemic risks. Traditional analytics approaches often struggle to handle heterogeneous data sources while maintaining transparency and trust. This paper proposes an Explainable AI-driven secure multi-modal analytics framework that integrates financial, transactional, network, and textual data to enhance financial fraud detection and cyber-enabled pharmaceutical network analysis.  

The framework leverages multi-modal big data analytics to capture complex patterns across diverse data types, while advanced machine learning models identify anomalous behaviors and hidden relationships. To address the lack of transparency in black-box models, Explainable AI (XAI) techniques are incorporated to provide human-understandable justifications for predictions, supporting regulatory compliance and informed decision-making. Security mechanisms, including data integrity validation and cyber-threat awareness, are embedded to ensure robustness against adversarial attacks and data manipulation.

Experimental analysis demonstrates that the proposed approach improves detection accuracy, interpretability, and trust compared to conventional methods. By unifying explainability, security, and multi-modal analytics, the framework offers a scalable and trustworthy solution for combating financial fraud and extracting actionable insights from pharmaceutical networks in cyber-risk-prone environments.