As the global economy swoons from the pandemic impact, the level of concern among banks surrounding loan defaults and credit losses has grown from the equivalent of ankle-deep to knee-high water, and it continues to rise.
Financial and consumer debt in both Europe and the U.S. has been on the rise. Credit losses are likely, be it for sub-investment grade company debt, car loans, auto loans, student loans or credit card debt amid job losses and bankruptcies.
To paraphrase John Maynard Keynes, the question banks and other lenders face at the moment is: Can we stay solvent longer than the market remains irrational?
The Accounting Catch-All
Global regulators have been pushing the banking industry towards creating one unified infrastructure to prevent extreme credit losses and write-offs, an approach made all the more relevant and pressing by current events.
The IFRS 9 accounting rule (and its U.S. counterpart, CECL) seek, in part, to align the bank’s capital with its risk-taking activities and shore up the bank’s balance sheet against credit losses before they occur, not account for them after.
Financial institutions including retail, commercial and investment banks are therefore all required to map out the Expected Credit Loss (ECL) for each loan immediately. The ECL is reached by multiplying the probability of default (PD), the loss given default (LGD) and exposure at default (EAD), within a certain timeframe.
The regulation, meant to thwart the “originate-to-distribute” loan model of high-risk lending then offloading the debt to third parties for repackaging without holding accountability for the credit risk, has the potential to heavily impact a bank’s core regulatory capital.
Loan losses under IFRS 9
IFRS 9 has three categories for booking loan losses. In the first stage, the bank must account for expected losses for the first 12 months when a loan is originated or purchased. In the second and third stages, when a loan shows a “significant” increase in credit risk or is in fact impaired, the bank must account for expected losses over the life of the loan.
The first challenge of IRFS 9 is one of volume and precision. With hundreds of thousands or even millions of loans to consider, each with hundreds of related data points (including geography, interest rate variation and associated hedging instruments) over at least a 12-month period, the ECL calculations require intensive computing power, with a double caveat:
- It cannot be done only at an aggregated level: regulators demand that banks account for every individual loan
- Calculations cannot be left to an EOD batch process: regulators demand that banks be able to report their ECL levels on-the-fly
The second challenge, arguably greater and costlier, is related to the classification system chosen by regulators.
The “SICR” Challenge
The “Significant Increase in Credit Risk” (SICR), a key tenet of the regulation but one that is not very well defined, creates a series of potential pitfalls in classifying loans.
SICR determines in which of three categories a loan falls depending on the risk metrics outlined above (PD, LGD and EAD) as well as qualitative criteria much harder to parse.
Current market volatility necessitates the reclassification of a loan every time a market shift justifies it. The challenge then is to identify which loans among hundreds of thousands need reclassifying. The most practical solution here is to “tag” loans with relevant attributes and perform multi-dimensional analysis. Once one type of loan deteriorates and requires reclassification, it becomes easy to identify similar ones also at risk.
The implication of misclassifying loan values on the bank’s balance sheet could add up to millions of dollars of expected losses per year and, over time, a weaker capital ratio. With the uncertainty introduced by SICR, it is especially important to get the figures and the analysis right the first time and anticipate potential loan degradations to avoid having to make costly adjustments or having the regulator force those adjustments.
An All-in-One Solution
Over the past couple of months, ActiveViam has implemented several credit risk projects at large European banks to address these issues. The banks’ credit risk and finance departments were seeking to employ a common technology that allowed them to interactively monitor country and counterparty credit risk down to the individual loan level. The main benefits are:
- A streamlined, unified platform shared by all stakeholders that integrates all the relevant data for interactive analytics saves clients hours every day on credit risk management. As a result, they can redeploy their teams to more profitable tasks.
- Having a clear, detailed and comprehensive view of every loan, the ability to classify the loans swiftly and precisely as well as an efficient way to detect loans that need reclassification will save clients potentially into the millions of dollars every year compared to what they could achieve with other solutions.
ActiveViam measured precise ROI for each client on both points through a proof-of-concept run and a back-testing analysis on historical data to evaluate the yearly cost savings.
The Capital Factor
In light of current circumstances, regulators have provided some relief for banks in terms of how they are allowed to provision expected credit losses, but the wave may be bigger than the lifeboat.
Precisely calculating risk and exposure is not only key for proper capital calibration, but extends to a bank being a “model pupil” and securing more support from regulators, especially if the macroeconomic situation further degrades.
Imprecise loan classification under IFRS 9 can adversely affect the regulatory capital ratio leading to, if nothing else, an increased compliance burden down the road.