Liquidity Risk Compliance: a Liability or an Opportunity?

The pressing importance of Basel II liquidity regulation was again highlighted in January. In the UK, the FSA sent letters to the CEOs of 2,800 firms, giving them one month to confirm they have taken measures to comply with the new liquidity reporting regime. Basel II requires banks, building societies and some investment firms to allocate sufficient capital to mitigate credit risk, operational risk and market risk. The new framework aims to create a more risk-sensitive approach, and demands that banks either accept the FSA's model for calculating the amount of capital they must reserve - which is sure to err on the side of caution - or create their own internal ratings-based (IRB) model. The pressure to develop, test and implement an IRB model that fulfils the specifications laid out by the FSA is being felt by all levels of these organizations.

Banks face a number of challenges, not least the demand for an auditable trail containing 95% of their operational data. The FSA itself remarks in its letter of January 13 that "the liquidity data we plan to collect from firms will be the cornerstone of the new regime." Therefore, it is imperative banks take steps now to ensure they can provide and trust this data.

Many large banks have grown through a combination of mergers and acquisitions, leaving them with a legacy of multiple systems for different areas of business, multiple CRM applications and numerous data warehouses - all running simultaneously.

Developing a transparent, robust, documented process to demonstrate the underlying validity of data used to calculate capital requirements is a huge undertaking. Here, an old computing adage is appropriate, 'put garbage in and you'll get garbage out.' The data input across the business must be accurate or the model used to calculate capital requirements will produce skewed results. In an age of such complexity, we trust and rely on models. Because of this, the dangerous possibility exists that management might blindly follow models that are based on inaccurate foundations.

The data quality issues highlighted above can be relatively straightforward to address. First, the application of data quality technology can help ensure duplicates or inconsistencies in data are eradicated before they enter the process, removing the margin for error in the model's calculations. This is achieved by validating and consolidating information and designing business rules that can spot and resolve data problems as they arise. This technology is not only a fundamental investment that will ease the implementation of the IRB model - it is a stipulated requirement of Basel II compliance.

In addition to the application of the technology, both people and process changes are required. Good data governance practices are essential to harness the benefits offered by the technology. The quality of a bank's data decays naturally over time. Like any other asset, data ages and becomes unfit for purpose without effective management and resources. A data governance strategy assigns responsibility for the quality of information, bringing together both the IT specialists that run the systems and the subject matter experts from within the business that can design effective business rules.

Aside from achieving the objective of regulatory compliance, a data governance program can be extended to other areas of the organization to meet a wide variety of needs. An improved data quality standard is a crucial tool in fraud prevention, allowing banks to identify persons attempting to gain credit to which they are not entitled. Automation of data quality tasks can reduce the need for manual intervention when responding to movements in the capital markets such as managing complex events. It also paves the way for banks to establish a single consolidated view of their customers - helping them offer more efficient services.

The subject of compliance is often viewed within a negative or punitive context but, as far as data management is concerned, it should certainly be viewed as an opportunity.

- Colin Rickard is managing director, north and west Europe at DataFlux.