All posts by Neel Williams

This is the second in a series of blog posts on deposit price elasticity, focusing on the modelling data requirements. There are several different modelling techniques and approaches to measure deposit price elasticity, which is dependent on the actual business problem and model usage. The exact data requirements might need to be amended to account for the modelling technique, but a large number of data items are consistent across all approaches. As with all modelling projects, it is good to initially take a step back and think about what type of information you would expect to be predictive, impact deposit price elasticity, and make you move your savings across different products and / or financial institutions. We would suggest that considering the below fields would be best practice for a deposit price elasticity model development: Product details: Historical interest rate of the product(s) to be modelled, this would include retention... [Read More]

Many top financial institutions have begun using predictive modelling and optimization to improve deposit pricing. This requires an understanding of customers’ deposit price elasticity — how sensitive are they to pricing changes, and what is the relationship between price and demand at the customer, segment and portfolio level? I’m going to explore this topic in a series of posts, which should be useful both to deposit portfolio managers and analytics teams. To start with, let’s look at the basics. Price elasticity is the study of responsiveness, and how the demand of a product changes with respect to price (and/or the price of competitors). Understanding deposit price elasticity, or having models that predict this, means you can quantify: Impact of a product’s price change on the deposit product How competitor price changes impact a deposit portfolio Impact of changing macro-economic conditions, such as a change in central bank lending rate How... [Read More]