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Finding the value in risk systems

ROI measured in new business, risk mitigation, productivity

In 2008, Southern California was like a Florida – not quite Nevada, but bad. We had a region of Southern California called the inland empire. Houses that were originally valued at $60,000 increased to $300,000 and on up to $500,000. If a lender took on a loan with 25 percent loan-to-value, they thought there was no way a house could drop below 30 percent loan-to-value for a loss. Well, we now know that it can and, it did.

At Wescom Credit Union – just as with many or most other financial institutions in the US at the time – we had to figure out where the worst point was going to be so we could stave off more losses. We used analytics to predict loan losses, and then plan for risk mitigation to this apex and risk management beyond that. We went make-or-break with forecasts that showed July, August and September 2009 as the apex of the crisis. We had extreme confidence in our analytic forecasts.

We had to change policies to meet our predictions. We started off being conservative. Credit lines – the place where people go when they run into trouble – were examined first. Our competitors were cutting $20,000 credit lines down to $5,000 or shutting them down outright. But at Wescom, our credit union members are our owners, our bosses in a way, so we didn’t want to do that to them.

Mitigation equals 30% ROI

Instead, using analytics we filtered the entire credit union membership to figure out which individuals were most likely going to be unable to meet loan obligations resulting in  chargeoffs, bankruptcies and loan losses. Then, we either reduced their credit lines – say from $10,000 to $7,000 – or suspended their accounts until they were paid down. Of the members that received suspension and reduction actions, 32 percent actually completely charged off the entire balance, which means we mitigated 30 percent of the loss.

That result shows clear ROI on our risk systems. There could be more because there’s no way to tell how much we saved by not allowing people to extend themselves further.

We went make-or-break with forecasts that showed July, August and September 2009 as the apex of the crisis.

We also relied on analytics to predict delinquencies, or late payments. In 2009, the credit risk team found that by the end of 2011, we would have $93 million in delinquencies – and that’s actually a small number. In 2012, we predicted that number would drop by half to about the $45 million range. There was some questioning in the organization on how delinquencies could be reduced by that much so fast without charging everybody off or stopping the processing of new loans.

The data was accurate and the analytics capabilities really delivered what the organization needed.

New income, new loans, productivity

A win like that proves ROI. Like most financial services organizations, Wescom has a strict view of expenditures, so every time we want to purchase a new analytics package or upgrade, we have to go through a multi-step process. First, we show the opportunity the purchase opens us up to. Then, we derive the potential from that opportunity such as new income and new loans that could be delivered with the new software. We monetize that based on risk and return, and that’s the ROI we look for from that purchase.

The analytics capabilities really delivered what the organization needed.

And, it’s not just about profitability – it’s also about productivity. During the financial crisis, there was a need to do as much – or more – in the credit risk department with as many or fewer people. We wondered how many more scorecards we could generate or how many more analytic processes we could support using only the team we had.

Proving that risk systems would help us plan and forecast synonymously was key. We weren’t planning A, executing with B and winding up with C, a number that was far from our original expectation. Instead, this system allows us to forecast recommendations that are accurate, complete and in-depth.

Starting in early 2010, we used the risk systems to bring back pre-approvals. Having that lead time gave us a comfort zone when everyone else was still in panic mode.

Giving decision-makers choices is perhaps the primary factor in determining ROI. We come up with recommendations and support for all dimensions of the credit union business. Not only do you need the appetite for risk, but the analytics to support that appetite.


Read other risk officers’ opinion about how to convey the value of risk systems to executives. Also download No Silver Bullet. Measuring Return on Investment in Risk Systems for best practices and tips from risk officers around the globe.

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