In the wake of the financial crisis, regulatory requirements have increased for banks to be able to precisely document their ability to withstand economic turbulence in the global markets.
This means that the ability to stress test credit performance and revenue is no longer sufficient – measures such as liquidity and full balance sheet projections are also requested. To cope, banks are taking a more systematic approach to stress testing. It needs to be transparent, auditable and clearly documented, as described here by Troy Haines, SAS’ Senior Vice President for Risk Research and Quantitative Solutions.
Here in the Nordics, banks are also gearing up for increased regulatory scrutiny by using advanced analytical solutions. One example comes from Swedbank, which operates in Sweden and the Baltics and serves more than 9,5 million private clients and more than half a million corporates.
Swedbank is expanding its usage of SAS® Expected Credit Loss software to be able to model, calculate, monitor and carry out scenario analyses. This includes forecasting of both expected and unexpected credit loss, as well as risk exposure amount evolution to ensure that the bank lives up to the IFRS 9 reporting requirements, as well as the IFRS 9 impact on stress testing analyses, such as the EBA Stress Testing exercise.
In doing so, Swedbank joins more than 300 banks worldwide, both large and small, that have chosen SAS technology for precise expected credit loss calculations and the transition to IFRS 9 accounting standards.
Requirement as a competitive advantage
Having to organise and invest for regulatory measures may not be a preferred business priority for financial institutions, but there is a significant upside to getting a better grip of the market influence on capital holdings. The flexibility in the analytical models means that many different scenarios can be put to the test to help support investment decisions or ensure preventive measures against losses.
Leaders need to practice crisis responses using analytically crafted scenarios so they can react to market downturns and financial crises as quickly as customers expect. Robust and forward-looking scenario models also enables you to better understand your data and how it can be used to effective and strategic decision making.
When risk managers and investment managers sit down in a close collaboration around analytical scenarios, there is significant opportunity for better business decisions, and thus a more robust and profitable banking operation.