Data Quality In Risk Management And Basel II

Compensating For Data Shortcomings Within The Banking And Financial Sector

Poor data quality is endemic in every organisation. Generally speaking, most organisations accept this as a day-to-day operational challenge and devise both simple and complex work-arounds to compensate for the data's shortcomings.

Organisations involved in financial risk exposures suffer from poor data quality, but nonetheless are able to function with apparent efficiency. For example, a recent Data Quality Assessment conducted by Informatica revealed that a particular bank had over €2 billion (AU$3.3 billion) in corporate loan exposures without maturity dates.

This finding highlighted both poor data quality and poor business process. But while the results are shocking, such data quality did not seem to have affected the bank's business - up until the recent credit crunch.

So in the past, the bank would not have prioritised this data deficiency. It would have deemed other issues more worthy of attention and budget. Now, senior level executives recognise data quality as critical in supporting a range of banking reports.

Basel II is one of the major drivers of change within the banking world. Because it is used to assess risk, the underlying quality of the data is critical to being able to deliver a report with any level of confidence. Financial institutions are adopting Basel II not simply because it is a compliance directive but also because it is for many the embodiment of best practice.

Regulations

Basel II (and particularly Pillar II of the Accord) puts responsibility on financial institutions in the area of data quality and data management. Banks must look at the accuracy of their risk exposure calculations throughout the entire business. For many, this encompasses the exposures from businesses in many different countries.

Regulators such as the Financial Services Authority/FSA (United Kingdom), the Federal Reserve
(United States), and the Bundesbank (Germany) have made it a requirement that banks self certify the accuracy, completeness, and appropriateness of Basel-critical data. Banks must now tailor their data management strategy to meet this requirement.

An example of the explicit requirements for data quality is highlighted in the FSA's application pack for internal ratings-based (IRB) approvals:

"Describe how the firm ensures that IRB (internal ratings based) data standards are met, and in particular how it ensures the accuracy, completeness, and appropriateness of the data underlying the firm's regulatory capital calculations."

This criterion effectively moves data quality out of the "it-would-be-nice-to-fix" status into an issue that must be addressed to comply with banking regulations.


Key Priorities

To become Basel II IRB compliant, banks need:

• Quantitative assessment of data quality


• Efficient business-specific strategies to cleanse data


• Key changes in business processes to maintain data integrity


• A framework to measure and manage data integrity on an ongoing basis

Banks need to establish quantified and documented targets and robust processes to test the accuracy of data in the following ways:

• Reconcile inputs and outputs of capital calculation with accounting systems


• Assign every exposure a probability of default (PD), loss given default (LGD) and, if applicable, a credit conversion factor


• Establish key risk indicators to monitor and ensure data accuracy


• Fully document processes for business and IT infrastructure


• Set clear and documented standards on ownership and timeliness of data


• Develop a comprehensive quantitative audit program

Source: FSA: CP 05/3 (January 2005) and BIPRU (section 4.2.5)

These priorities require consolidated data collection across the institution, so that data from all business units is brought together into a single source, typically a data warehouse from which reports are generated for risk and Basel II related decisions.

Data Quality and Basel II

Almost all leading banks have addressed these key priorities by investing in the data infrastructure: data warehouses, risk engines, business intelligence (BI) layers, and data integration software.

But at no point in the data stream is data quality managed as an explicit function. Instead, it is dealt with by tools not designed specifically for the purpose. This is an important oversight because data quality is a vital intersection point of infrastructure and the business.

More importantly, data quality is an explicit requirement for Basel II compliance. And, therefore this article will address the Basel II solution that has been designed by Informatica.

Scorecarding became a focal point for data quality in Basel II when the FSA's CP 189 proposed scorecarding as an external audit point.

Informatica has successfully used this scorecarding approach to assist banks around the world.

Data Quality Firewalls

Informatica has extended this compliance scorecarding approach to apply "data quality firewalls" in front of the risk engines, be they in-house ones or those from third parties.

The firewall's main function is to identify poor data quality before it goes into the engine, which removes the requirement for manual data remediation on the risk engine's log files and ensures that only high-quality data enters the risk engines.

Firewalls perform both automated and manual tasks. For example, errors innontransactional client reference data can be automatically standardised, cleansed, and/or enriched on the f y. Errors in transactional data are identified and presented to business analysts for rapid remediation.

Informatica's risk solution performs analysis on all types of master data:

• Customer and counterparty data


• Market and credit data


• Financial, reference, and transactional data

Therefore, this includes key data related to:

• Probability of default


• Loss given default


• Exposure at default

Risk and Basel II DQ Management

Informatica solutions provide a data quality management framework that gives the business total assurance to:

• Manage data quality on an aligned and integrated basis, meeting best practice on legacy data management and new business development


• Measure and to monitor the data quality using:
  
  
  • existing and newly created internal reference data sources
  
  
  • third-party reference data sources
  
  
  • Informatica's own reference data
  
  


• Act on areas identified for improvement without threatening the quality of existing data


• Handle change requests and new developments without threatening the quality of existing data


• Empower the data owners and Risk/Basel II Analysts by letting them change the business rules themselves in a controlled and auditable manner


• Report in a consistent manner across business units and product lines


• Guarantee to senior management and the board the accuracy of the data being stored, being generated, and being used for decisions


• Match data against trusted reference sources for validation and enrichment


• Furnish consistency throughout new data management


• Monitor and cleanse, on an ongoing basis, gaps in data accuracy and identify incidences of non-conformance


• Deploy a data quality firewall ensuring that new data is consistent with the risk management and Basel II requirements


• Support data remediation, data stewardship, and data governance

Informatica has worked closely with its clients when they make their Basel II submission to the regulator. Having done this a number of times, Informatica is pleased to be able to offer a submission template. To learn more about data quality, please visit www.informatica.com


About the author
RICHARD JONES is Informatica's ANZ regional sales manager. He has over 15 years of experience in data management and business intelligence. His main experience has been in government and the financial services sectors. Formerly, Richard held positions overseas in Britannia Airways and Business Objects and has held positions in Australia at St. George Bank and most notably as Practice Manager at Business Objects, where he grew his line of business by 120 per cent within an 18 month period.