New accounting standards will be a big line item for banks forcing them to rethink the way they account for losses, according to Troy Haines, senior vice president of the global software firm SAS.
Set up by the International Accounting Standards Board, the International Financial Reporting Standards (IFRS) 9 addresses the accounting for financial instruments. The standards are set to take effect in January 2018 in most jurisdictions. Canada will move to adopt the framework in September this year. The US will adopt the CECL – a slightly modified version of IFSR 9 in 2019.
Under the new framework, banks will have to adopt a new way to reserve for losses. One of the most significant changes is the shift from using an “incurred loss” approach to and “expected loss” approach. This means that banks have to reserve for the potential loss of a loan the day the loan is booked on the balance sheet.
The standards also mean that banks will need to account for the loss over the life of the loan. The projections around the loss sensitivity of the loans must also be based on macroeconomic factors – unlike the Basel requirements, which looks at 12 month forward looking statements.
This means that banks have to reserve for the potential loss of a loan the day the loan is booked on the balance sheet.
The standards also mean that banks will need account for the life of the loan. Projections around the price sensitivity of the loans must also be based on macroeconomic factors – unlike the Basel requirements, which looks at 12 month forward looking statements this accounting standard accounts for the life of the loan.
“The standard will change the way the finance and risk teams within the banks talk to each other,” Haines said. “Banks will have to integrate their data, modelling frameworks. They have never had to do that in the first place.”
The North-Carolina-based executive was recently visiting Australia to meet with clients. The former “long-term” banker has been with the technology giant for two years – “a long honeymoon period which I am enjoying”.
His experience in banking has given Haines an understanding of what bankers want from their technology providers. In fact, Haines was appointed by SAS chief executive Jim Goodnight to spearhead a new department in financial risks management. The division reports to Goodnight with Haines responsible for research and development, product management and marketing.
After an “ambitious hiring period”, the division now has offices around the world including the EMEA, Asia Pacific and the Americas. The team works with all types of banks, from global players to the smaller regional banks.
“My experience in banking helped me transition into risk management. It's also been great working for a company like SAS.”
It’s also a role that provides Haines with a bird’s eye view on the big regulatory themes impacting banks around the world.
In some ways, determining expected credit loss under IFRS is similar to stress testing. Haines says what this means now is that banks will have to implement models that recognise the exposure and sensitivity of their assets to different economic scenarios.
“Banks will need to have the right data to run those models so that they can effectively report to the regulator and allow them to conduct their own internal capital planning."
The Basel framework is also very much a priority for Haines, with the group investing in liquidity analytics and the latest revisions to the Accord to ensure banks are prepared for then new regulatory capital management regime.
Another area of focus is that of model risk management. This is usually placed under the umbrella of governance, risk and compliance, but Haines says it is an increasing critical area, particularly given the need for banks to address IFRS and stress testing.
“Speaking first hand as a former banker, I know there can be hundreds of models to manage when it comes to risk management. It’s important therefore to understand how they interact with each other to ensure you comply with the new accounting standards as well as existing stress testing requirements.”
Credit scoring is another important focus area where banks are looking for a more efficient and effective modelling process across the traditional credit risk management lifecycle to support their customer strategies and embrace big data and new technologies like machine learning.