Case Study: Liquidity Risk Management System

Insurance

Liquidity Risk Management System

The Challenge: Post-Crisis Risk Management

A systemically important insurance company was grappling with serious deficiencies in its liquidity risk management framework. The organization operated 15 fragmented systems across its subsidiaries, resulting in a cumbersome three-week process for cash flow forecasting that had a 40% deviation in accuracy. Critical functions like asset-liability matching were being managed in isolated spreadsheets, while the inability to model regulatory stress scenarios posed significant compliance risks. The lack of clear risk governance and metrics, combined with 25+ data sources using conflicting classifications, created a complex web of inefficiencies that threatened the company's ability to meet regulatory requirements and manage risk effectively.

Our Solution: Enterprise Risk Platform

Lydatum designed and implemented a comprehensive enterprise risk management platform that transformed the insurer's ability to monitor and manage liquidity risk. The solution encompassed three main components:

• Core Risk Infrastructure: We built a robust technical foundation using Oracle Database as the system of record, integrated with SAS for statistical modeling and Python for sophisticated financial calculations. The architecture incorporated SQL Server data marts for specialized risk analytics, while custom C# applications provided targeted functionality for specific business needs.
• Data Integration and Reporting: IBM DataStage was employed to create a unified data pipeline that standardized information from over 25 sources. This was complemented by Cognos for regulatory reporting and MicroStrategy for real-time risk dashboards, providing comprehensive visibility into liquidity positions and risk metrics.
• Governance Framework: The technical implementation was supported by a robust governance structure that included direct channels with Federal Reserve Bank of New York (FRBNY) supervisors, an active C-suite steering committee, and a cross-functional Liquidity Risk Committee. This was coupled with a comprehensive process audit and the design of a future-state operating model.

The implementation followed a quarterly release cycle, allowing for incremental capability deployment while maintaining business continuity. A major focus was placed on data standardization across lines of business, ensuring consistent risk measurement and reporting across the enterprise.

The Impact: Enhanced Risk Control and Regulatory Compliance

The enterprise risk platform delivered transformative results across multiple dimensions:

The impact extended far beyond pure technical metrics. Reporting time was drastically reduced from over three weeks to just two days, while the successful unification of 25+ data sources created a single source of truth for risk data. The automation of 85% of liquidity calculations not only improved efficiency but also reduced operational risk. Perhaps most significantly, the improved risk management capabilities enabled the company to exit FRBNY supervision in just 2.5 years, while creating a solid foundation for enterprise-wide risk management that positioned the company for future growth and regulatory challenges.

Technologies Used: Oracle Database, SAS, Cognos, IBM DataStage, MicroStrategy, SQL Server, C#, Python