The Rubik's Cube is one of the most popular toys in history. It was invented in 1974 by Hungarian sculptor and architecture professor Erno Rubik. As a teacher, Rubik was always looking for new ways to stimulate learning, and he created the now-famous 6-color cube to explain to his students about spatial relationships. His solid cube twisted and turned, yet it did not break or fall apart. Rubik never imagined how popular his Magic Cube - as it was first sold to the public in 1977 - would become. He also didn't expect the puzzle to be quite so hard - Rubik himself couldn't solve his mastermind creation for well over a month.
Oftentimes, banks make the mistake of viewing their loan portfolio at the individual loan basis, instead of looking at the pieces collectively as part of a larger puzzle. This is also where loan pricing software comes in.
In theory, loan pricing software puts every loan on a level playing field for comparison purposes. It allows you to compare deals with different structures, balance sizes, amortization terms, maturity dates, repricing frequencies and other variables. Such software allows you to take the different data elements and easily compare one loan to another and evaluate which one is more profitable for the bank. If you're seeing a pattern of less profitable loans by product category or loan size, you can shift your strategy and pricing to go after different products simply by leveraging loan pricing. There are so many variables that without the software, it's not easy to compare all the various loans on your books. Indeed, many banks might be surprised to learn how unprofitable some loans in their portfolio may be.
Pricing software also helps banks price loans based on risk vs. to whatever the competition may be doing. Yet, a significant portion of banks still do not use software to standardize pricing for loans and instead rely on common structures they have grown accustomed to. The issue here is that loan structure value changes for many reasons including daily market movement and future expectations. That is why dynamic adjustment tools can give banks an edge when it comes to pricing and winning loans.
Say, for instance, that a bank prices every commercial real estate deal at a fixed coupon rate of 4.50%. Since we all know intuitively that no two deals are the same, this seems to be inherently flawed. Consider that by definition, some deals will have better credit and structures, while others will be weaker. Pricing all 5Y loans for instance at 4.50% means you are pricing strong credits too high (so will see higher prepayments or lose more of these) and weaker credits too low (so you will win more of these and capture riskier credits over time in the portfolio).
Large banks constantly adjust pricing based on a variety of factors that are too complex for humans to process in context. The variables can run into the millions and that is just too high for the human brain. As such, having a tool that does so is critical. This tool can then be leveraged with human intelligence to better target the exact sort of clients your bank wants to capture, while ensuring you have a much better idea of the cost of doing certain things when lending (structure, prepayment, term, etc.).
To be sure, no loan pricing software can replace a strong lender, but having a tool can make everyone better. Such tools are great in providing educational background when trying to figure out the value of each loan subcomponent as you price to win deals. Of course, a weak model that does not use forward rates, adjust for risk or allow for more of a relationship picture may actually do more harm to the bank than good, so be careful when looking around.
Loan pricing software should be one of many tools in a banker's toolbox, so you can capture more empirical information about the profitability of individual loans to go along with the softer pieces that only a lender will know about a customer. It's not a hard puzzle to solve, but you can't do it without the right approach.