Leveraging Advanced Data Science Pipelines and Explainable AI to Identify Systemic Vulnerabilities in Financial Transactions and Medical Billing Systems

Fritz Otto Noah

Authors

Fritz Otto Noah Business Intelligence (BI) Developer, Germany. Author

Keywords:

Systemic risk, Explainable AI, Fraud detection, Medical billing, Anomaly detection, Model interpretability

Abstract

Systemic vulnerabilities in financial and healthcare transaction systems present significant operational and compliance risks, particularly in increasingly digitized and automated environments. This research introduces a unified framework that integrates advanced data science pipelines with explainable artificial intelligence (XAI) to detect anomalies and reveal latent structural weaknesses in both financial and medical billing systems.

The study employs machine learning models embedded with interpretability mechanisms to enhance transparency in anomaly detection and fraud analytics. Through case-based analysis and model evaluation, the framework demonstrates improved capability in identifying transaction irregularities and non-compliant patterns, offering both predictive accuracy and interpretive clarity. Findings suggest that the incorporation of XAI significantly improves system auditability and supports regulatory compliance by providing stakeholders with actionable insights into model behavior and underlying risk factors. This approach contributes to the development of more trustworthy and resilient digital infrastructures in high-stakes domains.

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