Many U.S. financial institutions are eliminating or reducing high-risk assets that require more capital in preparation for the new Basel III recommendations that go into effect, in phases, by 2019. This shift is generating pressure on pricing policies and procedures. Net interest margin is critical to financial services providers that are experiencing competition for high-quality loans while simultaneously facing increased regulatory compliance expense and recent legislation impacting fee income.
One approach to this changing environment is the pursuit of price optimization within loan origination. This strategy determines the individual applicant's price elasticity and calculates the optimal pricing for his or her loan. Individual price sensitivity can determine whether the borrower is seeking the lowest possible price or if he or she values other factors in seeking out a lender, such as convenience or building a long-term business relationship. Price optimization can also protect the lender with a logical and scientific pricing methodology that reduces or eliminates any unnecessary bias – this, of course, may be beneficial in regard to Equal Credit Opportunity Act compliance.
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Loan price optimization borrows from the success found in airline industry pricing: matching the best possible price with each individual customer offer. With price optimization, lenders can present custom-tailored pricing to each potential borrower, resulting in more loan volume and higher-quality loans.Â
However, price optimization can be complicated. Imagine the task of accurately predicting consumer-level price sensitivity across various loan products, channels and credit spectrums for each individual applicant. This requires innovative behavioral scoring, predictive insights about the price sensitivity of the customer and lots of data.
Nonetheless, most price-optimization solutions – whether created as a proprietary tool or provided by a third party – provide multiple benefits. These solutions are based on advanced analytics, historical data and price sensitivity. The key functionality of these systems may be prepackaged or available a la carte.
What does this require? The following are the most commonly requested features of a price-optimization strategy:
Rate sheets. Preparing rate sheets and fee schedules, including the necessary credit capital allocation criteria.
Existing portfolio pricing. Analysis and re-pricing of an existing customer portfolio can reduce attrition and manage credit risk.Â
Point-of-sale tools. Frontline employees can leverage real-time, transaction-level price optimization to sell more products at a higher profit margin. These tools may include relationship components and scenario capabilities for guidance on various options that achieve the desired margin.
Pricing process improvement. By providing a highly structured application process, pricing recommendations are seamless, and overrides are minimized. Some solutions can also provide alerts when return-on-equity thresholds are not met.
Reporting and tracking. Dashboard reporting provides insights into pricing compliance, price sensitivity, competitor pricing, attrition risk and opportunities for improvement. Ideally, the reporting will provide drill-down functionality by division, region, branch and employee.
Implementation and training. The proper installation and setup of the price-optimization solution is critical. Depending on the level of integration, ongoing support may also be required from various internal and/or external resources. It is important to consider this a dynamic solution with continuous process improvement versus a ‘once and done’ mentality.
Professional services. While a price-optimization strategy can be pursued internally, a third-party consulting and professional service can often be helpful in providing an objective measurement to the strategy's mission and goals.
So what are the results of pursuing this strategy? Consider the following items:
A unique result. Some customers are highly sensitive to changes in loan pricing; others show minimal impact from pricing increases or decreases.
Creating stability. Individual customers have a repeatable level of price sensitivity across various channels and lending products. This allows past client behavior to be used as a model for future price offerings.
Taking out the guesswork. Historical pricing, predictive credit file data, and demographic information can be combined to increase profitability.
Price optimization determines the price elasticity for a specific client in a particular transaction. The customer's willingness to pay is essential to pricing. Financial services institutions leveraging this data will outperform their competition.Â
In considering price optimization, there are several natural applications. For starters, direct marketing campaigns can leverage price-optimization data to enhance response rates. By promoting the ‘right product to the right customer at the right time,’ lenders can increase both application and close rates across multiple channels.Â
Price optimization can also be used for origination decisions in both a centralized or decentralized environment. By working with a rules-based engine, front-line personnel or distribution-channel partners can be provided with recommended pricing and also have defined pricing discretion levels.
Finally, price optimization can be used for managing existing portfolios – particularly lines of credit. Pricing decisions can include changes that increase yield, decrease attrition or improve utilization. Â
The home equity lending group of a top-five U.S. bank now uses price optimization for more than $6 billion in home equity originations annually. The team deployed the new solution to maintain volume and increase margins. As a result, this division was able to increase volume by 15.5% and increase profits by 4.9% as a direct result of implementing a price-optimization solution.
Price-optimization solutions can also have an internal benefit for the lender's operations: the opportunity to reduce management time in administering the pricing process. Instead of a pricing committee meeting to review pricing on a daily, weekly or monthly basis, the pricing committee can instead establish rules, alerts and triggers that manage and monitor pricing automatically. This frees the group to focus on more strategic decisions and rule modifications rather than on day-to-day pricing decisions – and, as the old saying goes, time is money!
Brian King is president of Wisemar Inc., based in Charlotte, N.C., and was previously senior vice president at BenchMark Consulting International and at Wells Fargo Bank. He can be reached at (704) 503-6008.