Poor Data Undermines Risk Management, Warns Celent

Financial firms that hold ambitions to improve risk management will need to be both resolute and creative to address issues associated with risk data quality and to achieve data mastery, says a report by Celent.

 

Since the crisis, there has been much discussion of risk measurement and management, but a shortage of the data on which financial firms depend. "Poor data can undermine not only risk management but also most of the actions a bank takes. Be it a strategy decision or something as specific as setting prices for consumer loans, the story is the same: the quality of the decision depends on both the skill of the individuals involved and the information. If data quality is poor, the information will be poor, and only luck can prevent the decisions from being poor," notes the report.

 

Paul Mee, coauthor of the paper and a Head of Oliver Wyman's Strategic IT and Operations practice, says, "Data is the lifeblood of any bank yet it is too often unattended, unclean, and unsuitable for the satisfactory functioning of the organs of the enterprise."

 

In the context of growing emphasis on risk systems and capabilities, data quality is a recurring problem that is unaddressed within many firms. With a number of risk management renewal initiatives within tier 1 and even tier 2 institutions, there have been renewed efforts to address this aspect in a more concerted effort (again).

 

“Banks’ ability to develop and execute winning strategies is undermined by the quality of the fact base available to support decision making. Data quality is a significant strategic risk for many institutions,” says James Mackintosh, a Partner in Oliver Wyman's Finance and Risk practice.

 

According to Celent, data quality programmes start and often end with a framework consisting of measurement, governance, ownership, processes, and organisation. Although these are important, implementing such frameworks has failed to improve information at many banks.

 

“Tried and tested” process-type data frameworks are failing the tests. We believe more creative approaches need to be considered," says Celent.

 

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