The impending financial reform bill in the United States will have a substantial impact on the consumer finance business. As with any such omnibus legislation, the devil will be in the details and the details will be worked out in coming months. Still, the broad outlines of some of the “consumer protection” aspects of the legislation are clear and they point to a sea change in the way banks, consumer credit companies and credit card issuers will be doing business. You don’t have to be a fan of the bill to recognize that many consumer-focused US financial services companies have pursued a strategy that I would characterize as sloppy and ultimately stupid: Make money by heavily penalizing your customer for any minor misstep and forcing them to work hard to avoid being tricked. The latter includes low introductory “teaser” rates and recurring fees that kick in after a trial period. Some change the terms and conditions (fully disclosed in four pages of small type) to expand the potential to significantly increasing fees and charges after a trivial lapse. So the consumer end of the financial services business will need to come up with a better, more intelligent way of making money in an environment that disallows many well-worn customer abuse tactics.
Luckily it can with pricing and profitability management software. Pricing optimization has been in use for decades in many industries. The earliest adopters included airline and hospitality companies, which have had to respond to a highly competitive, commodity-like pricing environment involving large volumes of transactions. They employ methods that enable them to consistently achieve higher yields using variable pricing structures than they could get with a flat rate. Using price differentiation techniques, they have been able to take advantage of the fact that different customers are willing – and able – to pay different fares or room rates for the exact same seat or bed. They have been able to avoid customer arbitrage, structuring prices so that those willing to pay more were unable to take advantage of the lower prices offered to those wanting to pay less. They have learned how to price for those looking for convenience (charging more for non-stop flights or travel at peak hours) or the nature of the trip. Using predictive analytics they have managed available discounts based on whether they were ahead or behind in expected bookings. Other companies in a wide range of industries, such UPS, Sysco, AT&T and Amazon, also use the technique.
And the same basic principles can be applied to consumer loans, mortgages, credit cards, automobile loans and, on the other side of the balance sheet, deposits. It has been possible to segment customers based on their credit scores, their existing relationship with the company and other factors in such a way that improves risk-adjusted returns. The techniques for doing this are far from straightforward. Moreover, the nature of the “product” in financial services does not lend itself to broad-based price optimization analytics because of industry specific issues. For instance, financial services companies face a “moral hazard” constraint: if they price credit too high their returns will drop faster than typical price elasticity models would predict because they rapidly lose good credit risks (those who have other options) and wind up with a higher percentage of deadbeats. This latter group may include someone with a great credit score who has just lost their job and therefore will jump at the chance to have credit line regardless of the rate.
Nomis Solutions has been a pioneer in applying pricing and profitability management techniques to the financial services industry. Their software and the consultative services that go with it are designed to enable financial services companies to improve portfolio yield and customer acquisition and retention. I just attended their users’ group meeting in San Francisco and had two key take-aways. The first is that regulatory reform will force US-based financial services companies to do a strategic rethink of how they price and set terms and conditions across a whole spectrum of credit and deposit products aimed at consumers. The second is that, after altering their strategy for the new environment, many of these companies will conclude that price optimization is a competitive necessity. To be sure, optimization is not going to be a panacea. Some of the responses by financial institutions to the new rules are likely to be tried-and-true throwbacks to an earlier age, such as charging everyone account fees on checking if balances are below a certain level, rather than offering “free” checking and relying on hefty overdraft penalties levied on a few to subsidize the offering. Still, I expect pricing and profitability management will gain substantial traction over the next few years. The results banks and consumer finance companies have achieved using these techniques (and the Nomis Price Optimizer specifically) through both good times and the recent turmoil have demonstrated its value.
Nomis also announced it will be making a “Nomis Score” available as a tool for more accurate pricing. The “Score” is a measure of the price sensitivity of individuals, just as FICO Scores (and others) are summarized measures of characteristics that drive creditworthiness. For consumer finance companies, using price sensitivity in conjunction with credit score in a risk-based pricing discipline can improve yields because even people with high creditworthiness (and who therefore could be picky about finding the best offers) may not care to take the time to do so or may value other product attributes (an existing relationship with the company) in ways that offset interest in price. Nomis’ analysis shows this is a significant segment of the market, one that could increase realized returns on loans and other financial products. Nomis’ challenge at this point will be to show that the theory works in practice. If its customers demonstrate that it has value, I expect it will further differentiate its solution.
In the broader scope of things, Nomis Solutions is yet another example of how the use of analytics can create breakthrough results by redefining how financial services companies do business.
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Robert D. Kugel CFA - SVP Research