In short, risk-based pricing is when lenders offer consumers an interest rate based on the estimated risk that the consumer will fail to pay back the loan.
The problem is that the market isn’t currently designed to respond to sudden change. The average lender will keep a loan product catalogue with up to hundreds of different product variants when it’s simply not possible to successfully manage a portfolio at this scale. Product and pricing changes take a long time. It’s tedious, needs manual effort, and adds unnecessary costs. In other words, it’s a major headache.
In addition, making changes takes far too long. At the moment, it takes mortgage lenders anywhere from several days to a few weeks to make pricing changes or introduce new loan products. Consider the past twelve months, a tumultuous period with shifting base rates, geopolitical situations, and a Mini Budget that sent the mortgage market hurtling over choppy waters. Lenders need to respond to such events faster, with more definitive and fairer solutions, if anyone is to stay competitive.
Let us be frank: for many lenders, introducing pricing changes is make-or-break. For example, if a competitor has reduced rates on a specific segment, responding too late runs a real commercial risk to the business. The existing structural restrictions are holding innovation back and preventing lenders from introducing new products and better rates to serve customers, at pace.
It’s clear the mortgage market is in a tangle. So, what’s the solution to this particular knot? As we know, lenders need to price products on a dynamic basis according to any combination of factors. The industry is crying out for cutting-edge tech tools that will empower lenders to rapidly adjust rates and adopt risk-based pricing. The need is urgent, and it is high time for tech providers to occupy this space.
Let us take an overview. The prominent industry loan products and distribution methods have created a very rigid business where risk is measured in large intervals. It’s not unusual to see rates jump by 5% or even 10% in LTV brackets. The problem is, while risk can vary considerably within a given LTV bracket, the interest rate remains fixed within that bracket, meaning it does not always accurately match the risk. The current industry format only allows for very rudimentary risk-based pricing – and it’s not fit for purpose.
A better pricing tool might look a little like this: It would allow lenders to set up a small number of core products and manage all variations dynamically using tables and formulae. This would remove the need to enter each product variation as a unique product, eliminating the need for an unwieldy catalogue without reducing the variations. In fact, it would allow lenders to increase variations without increasing complexity. The result? Less effort, errors, and stress.
The current system cannot leverage rich data, either. There is so much data available to us, data that could reveal useful information about the property and applicant, creating opportunities for lenders to tailor their loan products. Consider EPCs, flood risk, property type and medical information. Accessing this data holds the key to developing personalised pricing, leading to better and fairer consumer outcomes - the latter being key in complying with the new Consumer Duty.
The facts are undeniable. The mortgage market must embrace a new generation of advanced pricing technology. Combine algorithmic price optimisation with powerful machine learning techniques, and you’ll have a robust tool for performance analytics. It is time to take a big step forward to making the market a simpler and smoother place for lenders to do good business.
.jpg)
