IFRS 9: Evaluating Changes in Credit Risk

Determining whether an unimpaired asset’s credit risk has meaningfully increased since the asset was initially recognized is one of the most consequential issues banks encounter in complying with IFRS 9. Recall the stakes:

  • The expected credit loss for Stage 1 assets is calculated using the 12-month PD
  • The ECL for Stage 2 assets (defined as assets whose credit risk has significantly increased since they were first recognized on the bank’s books) is calculated using the lifetime PD, just as it is for Stage 3 assets (which are in default).

To make the difference more concrete, consider the following:

  • A bank extends an interest-bearing five-year loan of $1 million to Richmond Tool, a hypothetical Virginia-based tool, die, and mold maker serving the defense industry.
  • At origination, the lender estimates the PD for the next 12 months at 1.5%, the PD for the rest of the loan term at 4%, and the loss that would result from default at $750,000.
  • In a subsequent reporting period, the bank updates those figures to 2.5%, 7.3%, and $675,000, respectively.

If the loan were still considered a Stage 1 asset at the later reporting date, the ECL would be $16,875. But if it is deemed a Stage 2 or Stage 3 asset, then the ECL is $66,150, nearly four times as great.

Judging whether the credit risk underlying those PDs has materially increased is obviously important. But it is also difficult. There is a “rebuttable presumption” that an asset’s credit risk has increased materially when contractual payments are more than 30 days past due. In general, however, the bank cannot rely solely upon past-due information if forward-looking information is to be had, either on a loan-specific or a more general basis, without unwarranted trouble or expense.

The bank need not undertake an exhaustive search for information, but it should certainly take into account pertinent intelligence that is routinely gathered in the ordinary course of business.

For instance, Richmond Tool’s financial statements are readily available. Balance sheets are prepared as of a point in time; income and cash flow statements reflect periods that have already ended. Nonetheless, traditional ratio analysis serves to evaluate the company’s prospects as well as its current capital structure and historical operating results. With sufficient data, models can be built to forecast these ratios over the remaining life of the loan. Richmond Tool’s projected financial position and earning power can then be used to predict stage transitions.

Pertinent external information can also be gathered without undue cost or effort. For example, actual and expected changes in the general level of interest rates, mid-Atlantic unemployment, and defense spending are likely to affect Richmond Tool’s business prospects, and, therefore, the credit risk of the outstanding loan. The same holds true for regulatory and technological developments that affect the company’s operating environment or competitive position.

Finally, the combination of qualitative information and non-statistical quantitative information such as actual financial ratios may be enough to reach a conclusion. Often, however, it is appropriate to apply statistical models and internal credit rating processes, or to base the evaluation on both kinds of information. In addition to designing, populating, and testing mathematical models, FRG can help you integrate the statistical and non-statistical approaches into your IFRS 9 platform.

For more information about FRG’s modeling expertise, please click here.

Turning a Blind Eye to the Risky Business of Incentive-based Sales Practices 

Should you be monitoring your sales activities to detect anomalous behaviors?

The use of sales incentives (commissions, bonuses, etc.) to motivate the behavior of salespeople has a long history in the United States.  We all hope to assume the initial structuring of incentive-based pay is not intended to have nefarious or abusive impacts on its customers but, in a number of recent and well-publicized stories of mistreatment of both customers and customer information, we have discovered that these negative consequences do exist.  Likely, the business practice of turning an administrative blind eye to the damage done to consumers as a result of these sales incentive programs has played an even greater role in the scale of abuse that has been uncovered over the last decade.  In the most recent cases of unchecked and large-scale customer abuse, with particular attention focused on the financial services industry, this business paradigm of tying employee benefits (defined as broadly tying employment and/or income potential to sales) were resolved through arbitration and frequently typecast as “a cost of doing business”.

Today, are you putting your business, and all those associated with its success at risk by turning a blind eye to the effects of your business practices, including your employee incentive programs?  There are new consequences being laid on to corporate leaders and board members for all business practices used by the company, and the defense of not knowing the intricacies and results of these practices does not protect you from these risks.

We have developed a methodology to detect both customer sales and individual product behaviors that are indicative of problematic situations that require additional examination.  Our methodology goes beyond the aggregate sales, which are primarily discussed in the literature, to highlight individuals and/or groups that are often obviated when analyzing such data.

A forthcoming  paper, “Sales Practices: Monitoring Sales Activity for Anomalous Behaviors” will explore these issues, and a resolution, in depth. Visit any of our social media channels for the link.

 

 

 

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