Banks Must Optimize Now to Ensure Profits in the Long Term

Frank Rohde, CEO of Nomis Solutions, outlines the long-term benefits of price optimisation for banks and their customers.

Price optimisation software uses analytics to help banks maximise profitability by interpreting customer behaviour data to measure bank customers’ price sensitivity and predict how they will react to changes in prices.

The Nomis Price Optimizer solution works by taking a bank’s historical transaction data across all customers and pulling it onto a single data platform, enabling the bank to view all of the pricing and performance data in one place.

This data – which usually goes back five years, although that period can be extended or shortened –can be analysed rapidly in a scalable format to help the bank understand what consumer behaviour looks like (particularly in relation to price), what customer price preferences are and how they have behaved when the bank – or any of its competitors – has changed prices. By performing this analysis the bank can determine the underlying price sensitivity of its customers.

Even the largest banks struggle to create the analytic view of their customer data at this scale. We work with major financial institutions who found it hard to pull together even two or three months’ worth of historical transaction data because their systems were not capable of aggregating data over a long period. To facilitate that process, we’ve developed a proprietary data ingestion engine that allows us to rapidly extract, transform, and present pricing and transaction data within our platform.

The next step in the process is the modelling and analytical effort of isolating consumer behaviour with regard to price.

Frank Rohde

We have developed a suite of proprietary models that are applied to measure price sensitivity, which is not an observable behaviour and therefore difficult to infer and isolate from the data. We can also use these models to determine what customers who don’t care about price are concerned about – for example, how much they care about advice, fees, branch access and/or product flexibility. Each of these preferences can be isolated within the data and measured.

The final step is to calculate the lifetime value and profitability of the customer relationship for the bank. We then add workflows around these analytical building blocks that enable the bank to forecast customer behaviour and what its portfolio might look like over the next 6/12 months.

The bank can calculate how that forecast would change under different scenarios, which allows it to simulate the effect of charging more or less for mortgages or loans, for instance, or determine how different interest rate environments and different competitor rates impact deposit balances.These simulations can be run to a very high level of granularity, which means the bank can now forecast how much money would flow from term deposits to liquid deposits if the rate was decreased for the former and increased for the latter, and how that flow would differ across varying customer types and term bands.

Simulations can also be used to calculate the likely impact of changes to deposit rates or mortgage prices by competitor price movement.

The ability to run every conceivable scenario means the bank can identify the combination of prices that offers the highest volume growth or profitability or a combination of both, which is where the value of optimisation lies. The software allows the bank to visualize the efficient frontier of volume vs profit for each of its products and pick the optimal operating point in that trade-off.

Once these prices have been put into the market, the bank needs to be able to track customer reaction and feed that learning back into the models. In order to support this we have developed what we call ‘Active Recalibration’. Any change in customer behaviour creates new data, which feeds into the software and recalibrates the predictive models based on the new information value captured in the market. Especially in a rising interest rate environment it is critical that banks create this feedback loop as market conditions, competitor actions, and consumer behaviour changes.

New originations and changes in retentions recalibrate all the underlying models and assumptions, which means that the bank has up-to-date data for its next pricing run. What makes this system unique is that the recalibration is done automatically, which enables banks to quickly respond to market changes.