New accounting standards means big data problem for Canadian banks

New regulations meant to prevent the type of toxic loans seen in the aggregated mortgage products that triggered the 2008 recession will require Canadian banks to get a masterful grip on their data, according to one big data analytics software vendor.

The ninth edition of the International Financial Reporting & Assurance Standards (IFRS 9) will require banks to not only report on issued loans that have already gone bad, but to predict which issued loans will turn sour in the future and represent it on their financial statements. Requiring more complex predictions than banks have done to date, banks around the world have doubled their implementation budgets for the new standards during the past 12 months.

In most of the world, IFRS 9 goes into effect Jan. 1, 2018, but Canada gets the distinction of implementing it first – Nov. 1, 2017 – to coincide with the end of the fiscal year for Canada’s major banks at the end of October.

In a new whitepaper, Achieving Optimal IFRS 9 Compliance, SAS Institute Inc. details the key challenges banks face in putting the new accounting standards in place without taking a hit to their bottom line. It’s a new type of big data analytics problem that has affected banks worried, says Darryl Ivan, national lead or risk for SAS Canada.

Deloitte survey - IFRS 9
Deloitte survey results on bank expectations around IFRS 9 (Image courtesy SAS).

“It has significant implications on banks that are creating a bit of angst,” he says. “You have to predict the likelihood someone is going to default and the likelihood of the underlying asset you’ve loaned against will depreciate.”

Getting a grip on risk

One of the downsides of the new regulation is that banks will have to increase their provisions as a result. Provisions are money that’s taken out of income and set aside to cover any losses related to loans. A Deloitte survey of banks shows that most anticipate increasing their provisions by 50 per cent. But if banks are able to demonstrate that their predictions models are strong, then the provisions can be kept lower.

“You have to demonstrate that you have good enough models,” Ivan says. “One of the positives to be drawn here is that you can see where you have risk in your business and whether you’re pricing well for it.”

More risky loans will require more provisions even if the models are perfect, he points out. But if a good prediction model shows a loan is less risky, then a bank can avoid setting aside more provisions than necessary. Developing these models is tricky, because regulations require that it’s done not only for each individual customer of a bank, but for each loan that customer might have.

Banks will have to store this individual account level information and then reconcile it with their general ledger or an aggregated level. For each account, predictions will have to include both historical and forecast information.

“You have to churn out fairly sophisticated models that represent the life of the loan,” Ivan says. “There’s no silver bullet solution.”

No silver bullet solution

Finding the correct method to approach IFRS 9 calculations is where banks will need to call on big data applications. Business managers will need to pull data from many different sources and from different divisions of the company, then calculate how adjusting their models will impact scores for millions of customers and products. Ivan advocates for creating a sort of sandbox environment that can simulate various models – using the same data used in production – before the model is committed to and put into production itself.

Banks won’t commit to one model to measure all their customers either, the white paper says. Rather, customers should be segmented for different calculation approaches. Segments might include the following types of rules:

  • If the financial instrument is performing, underperforming, or non-performing.
  • The expected lifetime loss using both a simplified and complex approach.
  • Expert adjustments or approvals in the ECL calculation process.
  • Financial instruments being treated as individual or as a group.
  • Assigning a specific model to the parameters.

“It’s a principles-based guideline,” explains Ivan. “It’s not overly prescriptive and that means you have to be that much more transparent.”

Enforced by the Canadian Accounting Standards Board, the hope is all this work will stave off further recessions spurred by irresponsible loan products.

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Jim Love, Chief Content Officer, IT World Canada

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Brian Jackson
Brian Jackson
Editorial director of IT World Canada. Covering technology as it applies to business users. Multiple COPA award winner and now judge. Paddles a canoe as much as possible.

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