How Price Optimization Is Adopting To Market Changes
Many U.S. banks are eliminating or reducing high-risk assets, including real estate and mortgage backed assets that require more capital in preparation for the new Basel III recommendations that phase into effect by 2019. This shift is generating pressure on pricing policies and procedures.
According to a recent study by Economic Intelligence Unit and IBM’s Institute for Business Value, 90% of the world’s top banks recognize the need for transformation in pricing processes, methodologies, and use of client data. Net interest margin is critical to financial services providers. They are experiencing competition for high quality loans, increased regulatory compliance expense, and recent legislation impacting fee income.
Most experts agree that lending will increase in 2011, and 2012, as banks are eager to originate quality loans and lines of credit with solid interest margins and control risk on existing assets including pricing for deposits. Price optimization is equally important to lenders and servicers dealing with the still high volume of mortgage loan delinquency rates and the need to execute successful workouts.
Independent research and observation shows banks typically use one or more pricing philosophies. Pricing includes interest rates and fees that impact the Annual Percentage Rage (APR). Often, a hybrid or combination of several strategies may be most effective.
• Flat Rate Pricing. These bankers have a one-size fits all pricing strategy which often results in a disproportionately higher number of lower credit quality loans. These lower credit quality applicants are more likely to close because the pricing is more attractive versus other banks offering risk-based pricing. Higher credit quality borrowers may apply, but are less likely to close as they can generally obtain better rates elsewhere based on their lower probability of default.
• Loan Balance and Relationship Pricing. Often lenders tailor the interest rate and origination fees based on loan size. Most lenders also provide pricing incentives for multiple products or relationships in an effort to promote cross-selling among various product divisions.
• Risk Based Pricing. All credit-trained lenders should understand higher risk equals higher pricing. Risk based pricing aligns loan pricing with expected loan risk (probability of default and severity of loss given default). The pricing is generally adjusted based on the applicant’s credit profile, credit bureau score, or bank calculated custom credit score. The Fair and Accurate Credit Transactions Act of 2003 (FACT Act) requires banks to provide risk-based pricing notices as appropriate, but does not discourage the practice or limit a bank’s ability to appropriately price for risk.
• Price Optimization. It helps determine the individual applicant’s price elasticity and calculates the optimal pricing. Banks using this strategy have been able to increase margins and loan volume. Few banks can implement this strategy internally. Most benefit from an external partner in this space.
While each of these pricing strategies are interesting, the price optimization philosophy can be used as an addition to any existing pricing strategies.
Ideally, loan-pricing software provides a consistent pricing process that is both rational and analytical. At the same time there must be a balance between customer demand, the market environment, and bank profitability. Since customer price sensitivity is not necessarily correlated with credit risk, a data driven decision process allows the lender to maximize the loan close rate and net interest margin simultaneously.
Individual price sensitivity determines the customers that seek the lowest possible price versus those that value other factors instead such as brand, convenience, rewards, or relationship. Price optimization can also protect the bank with a logical and scientific pricing methodology that reduces or eliminates any unnecessary bias. This may be beneficial in regard to Equal Credit Opportunity Act (ECOA) compliance.
Loan price optimization borrows from the success found in airline industry pricing: to match the best possible price with each individual customer offer. Without price optimization, higher loan pricing generally results in fewer loans. With price optimization, lenders can present custom-tailored pricing to each potential borrower. The result can be more loan volume, higher quality loans, or both.
Unfortunately, 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 and lots of data.
Some larger banks have implanted internal price optimization models into their organizations. This can be effective if the firm has the time and money to continually monitor, enhance, and upgrade the systems as appropriate. Many banks instead select an industry vendor partner that specializes in price optimization.
Industry vendors providing price optimization solutions show impressive results for their clients. Nomis Solutions shared that “since 2004, its pricing technology and process customers have run over 100,000 pricing scenarios, priced over $400billion in consumer accounts, and generated over $400MM in incremental profits.”
Some of the advantages of a price optimization tool are the ability to provide real-time pricing, dashboard reports on production and profitability, and compliance adherence. Two best practices are the ability for a lender to provide clients with alternative pricing scenarios and management reports that allow a drill down into detail data.
Ideal pricing is beyond a simple risk-based credit decision, loan balance, or relationship focus. Price optimization is the culmination of all these factors and more into a rules based decision engine that provides “the right price for the right customer at the right time.”