The ENHANCED FINANCIAL SYSTEM VALIDATION: USING KERNEL PCA, WEIGHTED KERNEL K-MEDOIDS, AND MUTATION-BASED TESTING FOR ACCURATE RISK ASSESSMENT AND COMPLIANCE

FINANCIAL SYSTEM VALIDATION

Abstract

The current investigation presents a sophisticated methodology for validating financial systems that combines weighted
kernel K-medoids, mutation-based testing, and Kernel PCA. Enhancing risk identification, system resilience, and regulatory
standard compliance are the goals. Financial datasets can be searched for hidden patterns using Kernel PCA, critical data
points can be clustered using Weighted Kernel K-Medoids, and system resilience can be evaluated through mutation-based
testing. The Integrating advanced machine learning techniques gives financial institutions a scalable framework for refining
risk assessment, detecting fraud, and improving compliance. This approach improves financial risk management by
combining Kernel PCA for pattern recognition, Weighted Kernel K-Medoids for clustering, and mutation-based testing for
robustness. The proposed validation framework ensures secure and efficient operations while simultaneously promoting
openness and trust in the financial ecosystem. The suggested approach beats traditional CNN and RF models in a
performance comparison on a several metrics, including accuracy, precision, and compliance adherence. This solution
promotes strong regulatory compliance and guarantees accurate risk assessment.