Risk management is a hugely information-hungry process. Keeping an up-to-date record of banking or fund winners and losers is a data-intensive exercise. Completeness and the accuracy of the data are the bedrock for corporate scrutiny. Banks can be strange animals when it comes to releasing data; they may not even be fully aware of their own profitability. The new Basel II regulations will try to enforce mandatory reporting of profits and losses in detail. This is where the Basel II regulations come in to encourage more banking transparency. Losses are bad news, and sometimes nothing is worse than the embarrassment of a public loss or reputational damage.
A failure or degradation in these operations tends to make existing customers and counter-parties defect, resulting in an economic loss to the firm. Reputational effects are particularly important for larger more well-known retail banks in competitive markets whose customers can easily transact elsewhere.
Knowing how much you have lost, and why, is a sign that you are making progress in retailing. Knowing how to stop or reduce your losses is an indication of business success. These are known as “risk triggers”, while in auditing they are called “red flags”.
Adapting this sort of analysis would be useful in banking and fund management. Furthermore, on the larger corporate scale, banks under the stiffer Basel II operational risk measures will rarely wish to confess to the central banking authorities that they are “banks at risk”. Certainly, banks rated as more risky will be forced to pay higher operational costs in terms of a capital adequacy ratio of reserves held in the central bank under new Basel II rules. We can adapt one feature, the recording of losses, from the Basel II banking regulations and adapt it for the retail industry. This forms the first one-line building-block approach of our Basel II Loss Database for dealing with operational risk in the finance industry. This is a simple prototype approach towards identifying areas for monitoring, i.e. being risk proactive against solely reactive. It can identify business areas for improvement, certainly when you data-mine by business lines and deeper detail. This is a potentially rich area for business intelligence development.