Price optimization, elasticity modeling, and forecasting are all analytical terms that bankers have heard of. But what do they really mean? What are the nuanced differences behind each analytical methodology? Before your bank invests big bucks to beef up its in-house pricing analytical capability, we will explain to you, in laymen's terms, what pricing analytics for banks should be about as well as some of the cautionary tales you should be aware of.
Here are some of the key questions we will address:
Why is it that we typically apply aggregate (classic) elasticity models to deposits but choice-based models to loans?
Which pricing analytical methodologies are suitable / pragmatic for a smaller vs. larger bank?
How to define business rules and constraints in a pricing optimization algorithm?
What kind of pricing algorithms are sold by pricing software vendors? How are they different from each other?