We can all see that the shock waves of the credit crunch continue to reverberate through the banking industry and that the whole episode is causing a radical rethink. How did it happen? Where did banks go wrong? Why didn't
risk management systems pick up the early warning signs? Coupled with this, there is a feeling that banking needs to go "Back to Basics", yet at the same time there are pressures for bankers to lend more in a deteriating economic climate. All of this adds up to a large management headache on to how to plan future strategy.
With hindsight it is clear that all was not well in risk management - or perhaps more precisely banks didn't have the complete risk management framework that they imagined. The explosion of ideas, new models and framework didn't capture the true exposures that were uncovered in the crunch.
It is clear I think, that now is the time for some fresh thinking, or perhaps more precisely new and wider thinking in risk and a return to older narrower ideas in products and services. The new ideas in risk don't involve ditching the existing VaR based models, but rather look to limit their use to risks with frequent and high quality data that is suitable for modelling. For the hard to model infrequent data sets, we should expect to see in greater use scenario planning, stress testing and more scepticism about single risk solutions.
The move back to older thinking in products and services really relates to two strategies banks have used in past downturns, a return to higher service levels, trying to generate more fee based income; and move away from risk capital and towards more syndication. This general risk reduction strategy is likely to be in vogue until there are clear signs that G7 economies have fully recovered from the credit crisis.
I believe that both of these developments will require agile thinking across the bank - and a new approach to information. Future software will still have to correctly process the "hard" data, but increasingly softer measures will also be incorporated. This may be very new for the banking industry, but significant progress has already been made in other industries. Aerospace, Pharma and Oil companies are well used to such approaches and there may well be a cross fertilisation of ideas and techniques into banking.
Key Challenges
• Risk management is opening up to new ideas. Simple normal distribution models have been found wanting and now bankers will look to incorporate tools based around scenario planning, sophisticated stress tests, and the leading edge ideas coming from complex adaptive systems
• Coupled with a rethinking of risk management - banks will remain risk averse in this part of the business cycle. They are likely to recast many of their products around high level service, and prospects for enhanced fee income. New investment in risk systems will be in the new ideas surrounding complexity, monitoring cascading risks and deeper and wider integration of risk management within the business
• All of this means the collection, aggregation, distribution and intelligent use of data will be more vital than ever. Scalable and flexible tools will be essential - legacy systems will no longer be tolerated, they will be viewed as toxic.
Final Thoughts
The lessons from previous serious banking and debt crises is that it takes a long time for markets and the business cycle to return to normal. There is often a long adjustment period before the components are in place to kick start a new positive up wave in economic activity. During the recovery period, banks are repairing their balance sheets by taking on less risk and returning to simpler business strategies.
I believe the successful institutions will be those that can balance embracing the new methods and tools in risk management whilst developing a lower risk profile business model that can be expanded as and when the global economy fully recovers.
Discussion Points
• Will banks employ risk experts from other industries?
• Are the regulators leading the debate or caught up in trying to make sense of the current crisis?
• What are the new data requirements that will be demanded in the newer areas of scenario planning, estimating tail risks and sophisticated stress testing?