Risk Data Silos

In an article published in Future Banking the authors have lucidly presented a  case for using Business Intelligence to manage the chaotic world of data silos. They have made specific reference to risk data silos in the context of Basel III. According to Chartis, Banks and financial institutions worldwide will invest $27 billion in their risk management initiatives – that will include tools to dig into these silos - to provide actionable risk intelligence. These initiatives will form the crux of the post crisis response to a new regulatory regime as well as information consumption paradigms that are emerging.  

When we look deeper into these extant data silos, we definitely see more chaos than order even in large financial institutions.  In fact I would argue that the chaos is independent of the size or market share of the bank. 

The center piece of credit risk management is customer intelligence.  From a bank’s perspective, knowing the customer – beyond getting the contact address and drivers license info – is key to managing its default risk. 

Using their customer insights, Banks typically cross sell a variety of loan products   - from home equity lines to credit cards -to existing DDA customers.   

Below are some examples that I have seen in my experience while working at large banks.

a) the credit card SBU would be offering  lower credit line to a high net worth customer resulting in poor customer experience and potential loss of business

b) the loan officer would be happily underwriting a mortgage loan to a customer who  currently has past due payments on Credit card

b) the credit card SBU would  continue to send mail solicitation to its retail banking customer who has just started experience difficulties in making monthly mortgage payments

( Note: Much of this intelligence cannot be garnered from credit bureau data because there is a time lag before the bureau reports it)

All these could have been easily avoided if the bankers had timely and complete vision of truth or appropriate business intelligence.

The recent financial crisis spawned a massive loss mitigation effort by the banks. Many creative strategies have been developed to proactively manage default risk. For example, retail banking relationship – defined as having a checking account or a credit relationship (example – auto loan) provides powerful insights and often early warnings on loan performance.  Intelligence on presence or absence of significant retail relationship is now a key component of a bank’s new account acquiring strategy. However, the existence of data silos is a hindrance to providing a 360^o view of the customer.  

Interestingly, banks have the analytical capability to compute the benefits or loss savings that will accrue if they remedy the situation .  In other words they know in dollar terms how much this intelligence costs them.

Another dimension that forces attention on the chaos is the new regulatory regime such as Basel III. Compliance to the new regulations would automatically mean better data management leading to timely risk intelligence. Banks will need additional investments in time and money for compliance.

  

These insulated data warehouses and silos often weaken the integrity of a comprehensive credit risk management system.