Citation:

Jigar Mahendrabhai Solanki, 2025. "AI-Enhanced Regulatory Compliance: Processing 15M+ Records with Spring Batch in CCAR", ESP Journal of Engineering & Technology Advancements  5(3): 168-174.

Abstract:

This study deeply explores challenges of processing, validating, and securely managing financial datasets that are extremely voluminous—more than 15 million+ transactional records—in response to Comprehensive Capital Analysis and Review (CCAR) regulatory reporting requirements. It is a strictly engineered artificial intelligence(AI)-assisted Spring Batch architecture, complemented by advanced parallel processing expertise, intelligent workload partitioning, minute memory optimizations, predictive data-driven models for dynamic configuration tuning, anomaly detection in real-time, together with adaptive validation logic. Integrated into a fault-tolerant pipeline of this magnitude, these components shall cohesively offer high throughput, performance, and eliminate possible places where OutOfMemory could happen to be detected while ensuring auditing grades for tracing back to compliance when such large volumes of financial data processes occur.

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Keywords:

AI, Anomaly Detection, CCAR, Data Validation, ETL, Fault Tolerance, Financial Data, Large-Scale Data Processing, Machine Learning, Memory Management, Parallel Processing, Regulatory Compliance, Scalability, Spring Batch.