Scalable Database Designs for Credit Risk Assessment: Securing and Streamlining Data Pipelines in Modern Financial Systems

The complexity witnessed in modern-day financial data and the ever-stricter regulations necessitate the development, implementation, and deployment of scalable and efficient database systems to ensure efficacy and security in credit risk assessment and management. The complexity of current data in the financial world exceeds totals of available historical data, and traditional database architectures are proving to be exceedingly underpowered for today’s data sets and their scale, velocity, and variety. As a result, the ability of institutions to implement effective risk management strategies on a scalable level is significantly diminished, and fluctuations in financial data could result in a gap in financial risk management. Consequently, the following paper presents a unified hybrid database architecture that proposes the amalgamation of state-of-the-art technologies – cloud-native data lakes, distributed relational systems, and real-time stream processing architecture – to address these very challenges faced in the financial industry today. Results suggest an increase of 30% in average performance metrics (query processing), 40% average scalability, and a reduction of security threats by 25% using Advanced Encryption Standard (AES-256) – for data, role-based access control (RBAC) - for user access data management, and distributed ledger technology (DLT) over traditional legacy systems. The proposed architecture demonstrates wide-ranging capabilities for seamless integration of various data pipelines and compliance with the stipulated dates of 2023 financial regulations. It provides a robust, secure, and agile foundation to help modern banking institutions overcome present and future challenges of current trends that impact financial data management.