Magazine article The RMA Journal

CREDIT RISK DATA INTEGRITY: THE LOAN ORIGINATION: Early Bird Gets the Worm

Magazine article The RMA Journal

CREDIT RISK DATA INTEGRITY: THE LOAN ORIGINATION: Early Bird Gets the Worm

Article excerpt

IN JANUARY 2013, the Basel Committee on Banking Supervision published BCBS 239, "Principles for Effective Risk Data Aggregation and Risk Reporting." (1) This document was the committee's response to the financial crisis after supervisors found that many banks were unable to quickly and accurately aggregate their risk exposures and identify concentrations appropriately, whether across lines of business or between the legal entities of banks and borrowers.

Weaknesses in risk-data aggregation capabilities and risk-reporting practices were identified in some banks that were unable to manage their risks properly. These issues clearly had severe consequences--to the banks themselves and to the stability of the financial system as a whole. The ability to demonstrate validated loan data, which serves as the critical basis for accurate, timely portfolio risk-data aggregation and reporting, remains a serious and ongoing concern.

BCBS 239 applies to global systematically important banks (G-SIBs) and set a January 1, 2016, deadline for their compliance. However, financial institutions of all sizes face similar regulatory scrutiny and pressure to demonstrate their ability to assess risk exposures on an ad hoc basis and to address loan-data integrity concerns related to outmoded technologies, manual and poorly documented processes, and procedural shortfalls.

A second BCBS report, "Progress in Adopting the Principles for Effective Risk Data Aggregation and Risk Reporting," (2) published in January 2015, summarized the findings of a 2014 survey of all GSIBs, which were asked for their views on compliance and implementation issues encountered with the key principles of BCBS 239. The survey findings reinforce the continued challenges that even the largest banks continue to face:

* "Compared to the 2013 results, many banks continue to encounter difficulties in establishing strong data aggregation governance, architecture, and processes. Banks reported that they often rely on manual workarounds."

* "Rating downgrades were reported in at least one principle by 16 banks. In particular, there were more downgrades in the areas of governance and infrastructure and risk data aggregation capabilities, than in risk reporting."

* "In comparison with the results of last year's stocktaking, execution risk appears to have increased. Overall, 14 G-SIBs indicated that they will not fully comply with at least one principle by the deadline, compared with only 10 banks in the 2013 exercise."

A recent KPMG report, "Ten Key Regulatory Challenges Facing the Financial Services Industry in 2016," (3) cites new and ongoing regulatory themes that are projected to remain at the forefront of industry attention. The improvement of data quality, so critical in aggregating risk data and in generating accurate on-demand risk reporting, is forecast as one of the top 10 continuing industry challenges for many reasons, but especially the following:

* Increased, and likely continued, complexity of the regulatory environment.

* Continued industry and regulatory focus on data integrity, governance, and accountability.

* Higher expectations--from customers, investors, and counterparty stakeholders--for risk-data accuracy.

* Industry recognition that risk-data collection processes, systems, and reporting capabilities require continual evaluation for improvement.

Defining Loan Data Early On

Contrary to common perceptions, this issue isn't solely about having the ability to nimbly generate portfolio risk data across organizational or IT "siloes" or across borrower/relationship entities to the satisfaction of regulators, stakeholders, and bank management. It's equally about having a demonstrable, comprehensive approach to accurately defining loan data at the earliest point in the life of a loan, validating that data, and defining the data collection process in a way that maximizes the likelihood of data accuracy. …

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