Capgemini can help your organization establish superior liquidity risk management while meeting aggressive compliance timelines.
New regulations, new challenges
During the recent financial crisis, some financial institutions were found to have inadequate liquidity risk management. In the wake of this, the U.S. government issued two new regulations: the U.S. Liquidity Coverage Ratio and, as required by section 165 of the Dodd-Frank Wall Street Reform and Consumer Protection Act, a Final Rule to strengthen supervision and regulation of large U.S. bank holding companies and foreign banking organizations. The Final Rule requires all covered companies to be compliant by January 1, 2017, and has proposed a transition period to become fully compliant.
The right data foundation
Start your liquidity risk management program off with the right data foundation
Built on our proven data management and risk and compliance expertise, our liquidity risk management solutions include:
- Data provisioning
- Establishing a data quality framework
- Data integration
- Liquidity risk management warehouse and stress testing
- Liquidity risk reporting and analysis
- Model validation and governance
Capgemini has deep experience and a track record of unparalleled success in data management with a proven ability to:
- Manage and report liquidity positions across the banking organization
- Manage intraday data
- Improve predictive capacity
- Aggregate disparate data through a centralized liquidity warehouse solution to support mitigation
- Address data quality issues, including data accuracy, completeness, and consistency
Capgemini uses proven accelerators to jump start your initiative
- Critical Data Elements. The biggest challenge banking organizations face in complying with liquidity risk management regulations is intraday reporting — the challenge of daily data acquisition into a bank’s LRM system. We help address this challenge by defining critical data elements unique to each bank which cover all types of products using general business definitions necessary for liquidity risk management.
- Data Quality Framework which implements a structured approach to data quality— define, access, improve, and control
- Stress Testing Framework which is central for a liquidity-risk stress testing implementation. Our framework outlines how to formulate solutions to key challenges such as stress scenarios.
- Model validation is vital to ensure accuracy of liquidity risk reporting. Capgemini accelerators include Model Validation Standards, as well as a Model Risk Governance Maturity Model, which provides end-to-end coverage to assess and plan for all aspects of model risk management.