Why Basel III

2007 to 2009 is a period that will remain etched in the minds of financial industry policy makers for years to come. Complex financial instruments, wrongly priced risks coupled with pressure to churn ever higher employee bonuses and bank profits all combined to create the biggest financial crisis since the Great Depression. The US and EU financial markets, once assumed impregnable, faced the real possibility of collapse pulling down the global economy in the process. As hundreds of billions of dollars in government bailouts were pumped into banks and insurance companies considered ‘too big to fail’, regulators had to come to grips with the fact that the Basel II guideline on bank risk management had loopholes and inadequacies that had to be addressed to prevent a recurrence. And with that, Basel III was born.

A summary of Basel III requirements

The fundamental objectives of Basel III are not too different from those of Basel II. The new framework simply aims to make banks more resilient in the face of macro-economic shocks, to sharpen their risk management and to increase overall transparency. Basel III achieves these objectives through a combination of additional capital and more stringent controls on funding than Basel II.

In order to better understand why data warehouse infrastructure will be so central for Basel III implementation, one must acquaint themselves with the main elements of Basel III. The complexity of Basel III makes it near impossible to discuss it comprehensively in this article. But let us briefly touch on two of its most important changes vis a vis Basel II:

● Capital Requirements - Given the events of 2007-2009, it probably comes as no surprise that higher capital requirements are probably the most significant change from Basel II to Basel III. Basel III goes into a more extensive and explicit definition of capital. This includes specifics on the assets that can be used to calculate capital requirements and what minimum quality/characteristics such assets must possess to qualify for inclusion.

● Liquidity Requirements - Just like for capital, Basel III raises the bar on liquidity requirements. Two parameters signal the new liquidity requirements - LCR (Liquidity Coverage Ratio) and NSFR (Net Stable Funding Ratio). LCR is a measure of the highly liquid assets a bank possesses that are available to meet sudden or disruptive short-term liquidity obligations. Such liquid assets include treasury bonds and cash. NSFR is a measure of a bank’s stable long term funding in proportion to the bank’s long term assets. Stable long term funding includes customer deposits, equity and long term interbank funding. NSFR was factored into Basel III because a number of the banks that collapsed in 2007-2009 had demonstrated an over-reliance on short term funding sources such as short term inter-bank lending before their downfall.

Data Quality and Basel III Enterprise Risk Management

While most banks engaged in international banking will have to comply with Basel III’s new capital and liquidity requirements at some point, the detail, quality and age of data will be critical in determining which banks can comply quickly and efficiently. Poor quality data can for instance, lead to either over or under allocation.

Ultimately, effective risk management and compliance with Basel III boils down to a banks ability to collate, relate and analyze all relevant data. For very small banks, managing and correlating data may not be extraordinarily strenuous. But for the majority of banks and more so multinationals spanning multiple jurisdictions and with a ‘supermarket’-like assortment of financial products, identifying and pricing risk is a far more complex affair.

Nothing short of a well-defined, automated data management platform will do. Of course, complex real time data management and analysis was already a necessity for Basel II compliance. Basel III takes it a notch higher - more detailed data, longer time modeling and extensive stress testing. The data warehouse takes on a new importance. Overall, better data will enhance the bank’s competitive advantage.

When management makes decision based on enterprise risk data , they must have an assurance that the information is dependable and a true representation of facts on the ground. The key characteristics of quality data are integration, completeness, integrity, accessibility, extensibility and flexibility.

Three Approaches Toward a Risk-Aware Data Warehouse

Whereas Basel III is a new risk framework (albeit improved an improvement of Basel II), enterprise risk management has been at the core of the banking industry for decades. Senior management and key bank decision makers in Basel III implementation would do well to learn from the pros and cons of past approaches to developing, configuring and implementing enterprise risk management platforms and data warehouses:

● Approach 1 - Focus on business applications and the key reports of each. This is the fastest way to get a basic risk management framework off the ground. But it is ultimately the most expensive in the amount of rework it takes before it is Basel III-compliant.

● Approach 2 - Focus on management reports. This is probably the most common approach. Problem is that risk reports tend to fall into a second tier report category as senior management are more likely to focus on ‘the bottom line’ reports e.g. profit, revenue growth reports. If the enterprise risk management platform is built around data required for management reports, tweaking the data warehouse and developing the risk reports later on tends to be tedious and expensive.

● Approach 3 - Setup a risk management framework around Basel III while allowing for the integration of additional risk requirements unique to the bank itself. This often costs the most at the beginning but is cheaper and more efficient in the long run. It is the best approach. Banks that had already built their risk management framework and systems around a risk-aware data warehouse model are likely to have an easier time transitioning to Basel III. Similarly, banks that build their risk framework from scratch and centred on Basel III requirements will ultimately send less than those that opt for Approach 1 or Approach 2.

After all, Basel III is likely no going to be the last revision to banking risk. Put differently, data infrastructure architecture blueprint where enterprise risk management is at the heart provides a faster, more efficient and more sustainable path toward Basel III compliance. Such a model is more adaptable to the changing needs of bank risk regulation. The ideal risk-centric data model should facilitate data sharing between departments and consistent definitions of entities (for instance, what is a customer in the finance module is also a customer in the HR module).

Author's Bio: 

Graz Venture provides financial services players with the most cost-effective way to access, manage, and analyze their data. Using the flexible data management platform HINC, Graz’s data warehouse infrastructure helps manage tens of thousands of investment portfolios for several institutions including 9 insurance companies, 120 banks and the largest fund manager in Scandinavia. For more information, visit www.graz.se