Risk & Compliance Adapting Mortgage Loan Price Optimization to Building Societies

House image with analytics
Apr032017

One of the hottest analytic technologies in mortgage lending is price optimization. This is the application of advanced analytics to pricing strategies, in order to determine the ideal price for every customer that maximizes profitability, given factors such as take-up, affordability, etc.

That’s great for banks, but what about building societies and credit unions? If profit isn’t your primary goal — if you exist to serve your members — does price optimization have a place?

The answer is definitely yes. Using pricing optimization, building societies and credit unions can develop strategic mortgage offers that target specific objectives, such as customer retention, without hurting the bottom line and ensuring that targets around Treating the Customer Fairly are met. This kind of optimization can really pay off – making an appropriate offer, to the right customer at the right time, typically results in 10-15% higher retention of existing customers.

When building societies use price optimization, what needs to change is the goal you are optimizing. Fortunately, that is precisely the kind of challenge that optimization can meet.

Optimization starts with the objective – what are you trying to optimize? With a Building Society the first thing we might do is change the objective from profitability to retention.  While retention targets can still seem cold, changing the objective is decisive: the bent of the optimization framework to throw higher rates and fees at customers who are seemingly unresponsive is replaced by a natural drive to find the best offers for members in order to retain them.  In other words, finding offers that make members happy becomes a natural outcome of a retention-oriented solution.

We still care about demand models because we need to know what will happen to our portfolio if we choose one retention strategy over another and we still care about profitability, in as much as we also need to ensure that we produce enough return on capital deployed to allow us to invest for the future.  But in a member-oriented world these considerations serve the wider  objectives of keeping members happy and retaining them throughout the full terms of their mortgages.

Secondly, the concept of price differentiation needs to be modified from one that uses more segmented pricing to target increased profitability, to one that targets the rewarding of members.  Most building societies currently only focus on LTV banding for pricing.  Further differentiation is not a pre-requisite for driving value from optimisation, but the practice of providing discounts to members with stronger relationships or longer tenure is seen only sporadically in the market.

In a world where we want to reward members and retain them, this ethos combined with the mathematics of relationship-based pricing push in the same direction: Loyal, longer-tenured members are more likely to be retained when they roll off future mortgage deals, so surely it makes sense to incentivise them to stay in the future by rewarding them today?

Gradually, institutions that are oriented towards delivering good service to members are realizing that analytics and optimization can be used to better match mortgage offers to member needs. Mortgage price optimisation can be applied in a way which is consistent with serving members and putting their interests first.

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