Data Quality: A Pre-Existing Condition

HEADLINES WERE MADE AND A GAGGLE OF TALKING HEADS SPUTTERED ENDLESSLY THIS SPRING AS THE NEW TRUMP ADMINISTRATION AND CONGRESS RUSHED TO GET A HEALTH CARE BILL THROUGH THE HOUSE OF REPRESENTATIVES. A CRUCIAL LAST-MINUTE COMPROMISE ON THE THORNY ISSUE OF PRE-EXISTING CONDITIONS PROVED TO BE THE KEY TURNING POINT AND THE BILL PASSED THE HOUSE AND WAS SENT ON TO THE SENATE.

Unfortunately for U.S. banks and other institutions that own loans, such as private-equity firms, pre-existing conditions are still very much in play. A large number of these institutions do not have a strong or even adequate understanding of their own loan portfolios because their underlying data points are in the wrong place, incomplete or are missing altogether. This is an extremely dangerous pre-existing condition, especially at this stage of the business cycle, where data is the only way to determine the health of a loan portfolio. Institutions critically need better information to make good portfolio decisions so they can weather the next downturn or take advantage of the next upswing.

Consider, for example, the collapse in energy prices last year. As the price of oil fell below $50 a barrel, some institutions responded by looking at their entire energy portfolios and performing total portfolio stress testing and reserving. These institutions may have gotten some indication of their problems but these simplistic analyses completely overlooked the fact that some parts of their portfolios were in much worse shape than others. Loans tied to companies with operations in West Texas or Oklahoma might have been stressed but loans to businesses in North Dakota’s Bakken Formation were completely underwater. A financial institution cannot prosper, or even survive, with incomplete loan-level information.

Institutions need to understand their balance sheet loan by loan. They must have governance policies in place that specify what data points are collected, how they are collected and what data points need to be maintained in their database. Further, institutions need to establish data lineage. They need to be able to trace all their data back to the original loan documents, establishing a single source of truth or, what is sometimes referred to as “golden data.” Institutions need both current loan data as well as at origination, in order to do basic analysis for their asset liability modeling and for stress-testing scenarios, which the Federal Reserve is unlikely to eliminate no matter who is in the White House.

Some of the largest banks in the U.S. began to develop and implement rigorous data quality standards a few years ago under pressure from regulators. They invested substantial time and money to these efforts and looked for ways to leverage the work so it would not be merely an exercise to satisfy government regulations. As a result, these institutions now incorporate their new systems into all of their decision-making processes, as they recognize the critical value of high-quality data in portfolio management.

At the peak of the business cycle, dependable data points are more crucial than ever. Institutions need to have a granular understanding of the characteristics of each of their loans to accurately quantify the amount of stress their portfolios can withstand given their existing positions in commercial lending, housing, construction loans or other riskier assets. The data points will also be able to help financial institutions adjust their underwriting criteria by product so as the business cycle declines, they can take advantage of new opportunities.

Getting an institution’s data points to an adequate level is not a simple task and should not be taken lightly. Most institutions do not even have all the necessary data in an accessible format. Commercial loans, for example, are not standardized and data points are often difficult to find. An institution probably needs about 175 data points for each loan to reach an acceptable analytical level but may have only 75 data points in its servicing system, meaning 100 pieces of data are missing before the process even begins.

Situs has the experience and expertise to handle this type of project. Since 1985, Situs has been the premier global provider of end-to-end commercial loan advisory services and integrated solutions. We have helped institutions review and rectify their databases to comply with the Federal Reserve’s Comprehensive Capital Analysis and Review examinations and other regulatory data quality reviews.

For each client, Situs dispatches a dedicated team of senior professionals who identify all existing data, check the accuracy, access new loan file data where necessary and ultimately build a clean, accurate database. Situs further helps firms fix their data procedures and processes through a best-practices review and implementation. We review and repair quality-control processes when data are initially inputted, making sure not only that correct data points are entered, but are also entered into the proper locations. We make sure an institution’s current data system is updated so it is able to keep track of each quarter’s data and ensure they are correctly merged with all other data. When Situs is finished, our clients have up-to-date and accurate databases that enable them to manage their risks, excel at stress tests and take advantage of new business opportunities intelligently.

For more information, contact Ed Robertson, Co-Head & Managing Director, Situs Financial Institutions Group (FIG).

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