Scalable Open Source Big Data Platform Migration for Credit Risk Calculation

The client currently relies on a legacy, single-server system for storing and processing big data related to credit risk assessments. Due to limited scalability and high costs associated with expanding server resources, the organization faces inefficiencies and risks in compliance and data security. The existing environment hampers the ability to process increasing data volumes efficiently, impeding operational agility and cost-effectiveness.

A large financial institution specializing in banking and credit services, managing extensive loan portfolios and regulatory compliance requirements.

• Implement a distributed, open source big data platform to enhance scalability and performance of credit risk calculations.
• Achieve near 100% accuracy and convergence in credit risk modeling comparable to previous SAS-based solutions.
• Reduce operational costs by replacing proprietary systems with open source technologies.
• Ensure data security, control, and offline accessibility within an on-premise infrastructure.
• Facilitate compliance with regulatory standards such as Basel III/IV through reliable and auditable data processing.

• Distributed storage architecture using an open source platform (e.g., Hadoop) capable of horizontal scaling by adding computing nodes.
• Fast data processing framework (e.g., Apache Spark) supporting in-memory computing for real-time analytics and high-performance querying.
• Ability to replicate existing SAS-based credit risk calculation models with minimal functional regression.
• Support for complex data transformations, macros, and processing logic equivalent to proprietary environments.
• Compatibility with existing data sources and integration points within the client’s infrastructure.

• Existing risk management analytics models and workflows
• Data sources for credit risk data (customer databases, transaction records)
• Regulatory reporting tools in use for compliance monitoring

• System scalability to handle expanding data volumes, supporting the addition of new nodes as demand grows
• Performance benchmarks enabling timely risk assessments (aiming for processing convergence within acceptable timeframes)
• Data security and control within an on-premise environment, minimizing exposure to external threats
• High availability and offline operational capacity

The new open source big data platform is expected to replicate existing risk calculation accuracy at approximately 100%, improve scalability and processing performance, and significantly lower operational costs. Enhanced data security and control will mitigate risks associated with data breaches, while increased system flexibility will support rapid growth in data volume and regulatory compliance, leading to more reliable risk management and decision-making capabilities.