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- NatWest to use biometric payment approval for all business payments
- UniCredit joins Plug and Play to discover new innovative solutions for the Group and clients expired
- Grass is greener for environmentally friendly businesses, finds Barclays expired
- Credit Kudos raises £2.2m in latest round of funding expired
- Customer engagement key differentiator for banks as total redress payments increase expired
- BNY Mellon publishes mixed results for Q1 2019 expired
- KeyCorp Q1 results reflect solid underlying trends in core businesses expired
6th November 2018
Banks should use AI to prepare against Brexit shock, says SAS
Following the news that the majority of banks have shown themselves to be well prepared for economic shock in the European Banking Authority’s 2018 stress test, Lee Thorpe, head of risk business solutions at analytics leader SAS, considers the need to continue to bolster planning efforts using advanced AI in the run-up to Brexit.
He says: “With Brexit just around the corner, it’s good to see that the banking sector has proved itself well capitalised for a severe but plausible stress scenario. Given the levels of uncertainty currently at play, forewarned is forearmed. But that’s just the problem – with the details of any potential deal (or no-deal) still unclear, how forewarned can banks actually be without testing hundreds of possible permutations?
“Following on from the test, financial institutions must continue to ensure that they map out as wide a range of scenarios as possible, to mitigate the potential consequences of Brexit. They need to constantly update crisis responses using analytically-crafted scenarios so they can react to any market restrictions that result in an economic downturn as quickly as customers and the nation reacts.
“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 understanding of the market influence on capital especially during a crisis. The automation of processes to execute the analytical models means that many different scenarios can be put to the test to help support strategic decisions or ensure preventive measures against losses are implemented rapidly.
“We’ve come a long way since the 2008 crash. Artificial intelligence (AI) is now starting to be utilised in the banking world. With advanced analytics at its core, AI can now help risk teams map highly complex situations and approximate potential outcomes before humans make a final decision. Banks need to collaborate with AI to build the strongest possible understanding, informed by deep analytical insight.
“The biggest test is still to come in March – banks must go beyond the regulatory requirements and ensure they have the foresight to prepare for multiple worst case outcomes, even while hoping for the best.”