Prospective loan product and pricing engine (PPE) customers invariably want the vendor to provide a statistic revealing the accuracy of its engine. It usually comes in the form of a seemingly straightforward question: ‘How accurate are you?’ It sounds like a simple enough question, but the answer is elusive.
If you run a single search through a pricing engine and evaluate 100 programs and you find one error, would that make the system 99% accurate? What if that one program was the one your loan officer chose? Wouldn't that now make it 100% inaccurate?
The percentage given can obviously be skewed by the numbers used to derive the answer. Pricing engines are not perfect; there are a certain number of errors that will happen due to the enormity of information contained in engines and the ever-changing mortgage environment in which we live.
One thing we can be certain of is that by utilizing this technology, the time savings and corresponding efficiency gains reaped from automating loan product and pricing searches far outweigh the minimal errors that occasionally arise.
A true PPE combines fully adjusted pricing with borrower-specific program eligibility. This requires the PPE to manage the programs and pricing adjustors for multiple lenders in one all-inclusive system.
Even after the subprime collapse and with thousands of programs going away, there is still an enormous number of programs available to search. Multiply this number of programs by the number of variables used to determine eligibility, then by the number of pricing points on each program, and you can see how computing an actual number regarding accuracy can get complicated.
The process of deriving an exact numerical expression will depend on how you calculate your answer.
Errors come in the form of false positives, qualifying a program incorrectly, or false negatives, incorrectly disqualifying a program. These errors occur as a result of the way each program is scripted through each PPE's proprietary scripting editor. In a rules-based system, if one line is in the incorrect location, either a false positive or negative can result.
For example, the engine could display a qualified loan with correct pricing that, based on the borrower profile, should not have shown in the system as qualified; provide a loan that does not qualify because of an error in the documentation provided by the investor; or simply fail to show that a qualified loan product is available.
There are also errors that occur because not enough data is provided to the PPE for it to make a complete evaluation. Errors experienced in the system that were caused by poor or incomplete data entry by the end user are outside the control of the PPE; however, they can be drastically minimized through training and having a user-friendly interface.
With constantly changing loan products, the ability of the vendor to completely eliminate errors within the engine can be challenging. The engine can present the best pricing from investors, but how do you guarantee that 100% of the information is correct? Some online pricing engines put an exact percentage on the accuracy of their products, but where does this figure come from?
A single submission can search over thousands of different loan products, but because the investor guidelines for those products were interpreted by people and subsequently those people entered rules into the PPE that govern a submitted loan's qualification, there will always be an opportunity for errors. PPE vendors address this by attempting to standardize data within their own system to make sense of each lender's individual guidelines and adjustment sheets.
As a result of this standardization effort to port data into an engine's own language and format, some errors can still occur. Obviously, the PPE vendor's goal is to eliminate as many errors as possible.
The better question to ask is what quality control features are in place to effectively minimize the number of errors experienced? Understanding the processes that a PPE vendor has in place to combat human errors will provide insight into their attention to accuracy.
This might include the automated testing of the PPE's rules in order to draw attention to unforeseen results, the automated monitoring of the rates posted by the lender, or other proprietary systems that oversee parts of the process.
For example, the rate grids and adjustments on a given rate sheet can be compared to the previous day's rate sheet through an automated process, so customers do not have to worry about keeping up with the changes their investors may be making.
Also, automated processes can check rates continuously to ensure their accuracy throughout the day and instantly update intraday price changes. Other quality control processes should be implemented to improve the accuracy of the lender-specific pricing adjustments as well as the guidelines used to qualify the programs being searched.
Safeguards should be built into the PPE to give more control to the system administrator and/or the secondary marketing desk. Typically, it is a system administrator or a privileged user that discovers an error and is responsible for rectifying any pricing issues with the loan officer. A quality PPE has checks and balances in place that will assist the administrator when errors are encountered.
Errors and other issues should be resolved quickly in any of a number of ways. Some PPE vendors use ticketing systems that automate the resolution of problems through standardized processes.
Other vendors provide complete customer care through individual account managers who resolve any issues. The good news is that a quality vendor should be able to resolve any error quickly.
The emergence of loan PPEs is a great example of computer automation genius in mortgage banking. Within seconds, a PPE can sort and identify thousands of products and pricing options that would take hours for a mortgage professional to find. Loan officers and brokers can search, manage, price and lock loans in real time.
If loan officers are not using a PPE to find qualified loans and the best price, additional profits may be lost on every loan. Be sure to ask the right questions when shopping for this extraordinary technology, and don't be swayed by unrealistic statistical percentages.
To determine the viability of their services, ask for recent references, look to the amount of time the PPE has been in business and ask to take the pricing engine for a test drive – after all, talk is cheap. Just make sure you realize that because people are still involved in the process, a certain amount of errors are to be expected.
Mason Rees is the sales and marketing director for Dallas-based Sollen Technologies. He can be reached at (800) 582-1074.