Deposit pricing is a lot more complicated than it used to be. On top of the usual desire to minimise funding cost in a balance-neutral way comes the regulatory need to manage the distribution of funds across the entire portfolio, from retail through corporate, as a result of the Liquidity Coverage Ratio (LCR) and Net Stable Funding Ratio (NSFR) rules within Basel III. And this needs to be co-ordinated at the Treasury level, not left to individual business units.
Pricing is only one of the three main levers at a bank’s disposal to re-engineer its deposit portfolio – the others being proposition design in its widest sense, and marketing activity to attract different types of customer. However, these other levers take time to have any effect, and are something of a blunt instrument, difficult to control with any finesse.
Price changes, on the other hand, can have an immediate impact and are highly controllable, but predicting the impact with any accuracy is complex and difficult, and attempts to use pricing to optimise the regulatory liquidity position have the potential to damage margins, and vice versa.
To even attempt this kind of price-based portfolio management, a much more sophisticated and robust pricing toolset is required, ultimately based on customer-level analytics.
However, the prize can be substantial compared to the cost of making the investment – an additional £10m of revenue for every 1bp of sustainable improvement per £100 billion of deposits, not to mention the benefits of an improved liquidity position.
Developing a new analytical toolkit
The challenge with pricing analytics has always been the data is incredibly noisy; there are so many factors other than price that influence inflows and outflows.
The solution is to provide a priori structure to the analysis based on overall insight into how pricing works, rather than expecting the right structure to arise naturally from the analytics – in our experience, the latter approach is doomed to drown under the weight of its own complexity.
Rather than regarding the problem as a single, hugely complex analytical problem, we deconstruct it into four individually simpler challenges, as follows:
- Understanding fund behaviour
- Understanding actual tenor
- Understanding price elasticity
- Understanding customer value
Together, these form the basis of a pricing model that can be used to understand the likely impact of price moves on customer-level balances, and thus on the future shape and size of the overall funding portfolio.
This highly modular approach has a number of operational benefits:
- It allows effort to be focused on the more complex or more important parts of the deposit portfolio, while less important parts can be given a simpler treatment, saving time and effort.
- The model can easily be built on over time – enabling a staged approach in which further refinements can be incorporated on an on-going basis.
- It makes the toolset more robust – the overall pricing model is the sum of a large number of individually simpler components, rather than a smaller number of more complicated models.
- It allows for more straightforward on-going maintenance of the analytics; if better data becomes available on one particular part of the deposit portfolio, that part of the pricing toolkit can be updated without touching the rest.
We then undertake the analysis independently on different segments of the portfolio, defined according to customer and product dimensions that are likely to exhibit a very different price response.
We use customer type as one dimension because customers have an intrinsic price sensitivity. However we also use product as a segmentation dimension because the same customer may show quite different price sensitivity for different products, or even where there are different use cases for the same product. We see this behaviour in almost every industry sector we have worked in.
- A retail term deposit used for “long term savings” will be much more rate sensitive than an instant access account used for “rainy day money”…
- … but often retail savers use instant access accounts for long term savings; used in this way, price sensitivity will be higher than if the same product was used for rainy day money.
- A non-banking example: the same person will typically be willing to pay considerably more for a pint of beer in a bar on a Saturday night than the exact same drink on a Wednesday lunchtime, and drink considerably greater volumes.
To illustrate: a simple example of a customer x product price response heat map we created for a client with roughly £100 billion on deposit is shown below. The shading from dark red through dark blue represents high to low elasticity; grey indicates no elasticity assessment was possible.
Finally, beyond creating the pricing analytics, there is also a need to develop a pricing process, managed centrally by the Treasury function then cascaded out into each business unit and ultimately to individual decision makers. Even with the most sophisticated tool, deposit pricing cannot be reduced to an automated process. Analytics can show the likely impact if certain pricing actions are taken – not whether those actions should be taken given the wider margin, liquidity position and other objectives of the bank at any given point in time.
We define four different “usage levels” for a deposit pricing toolkit, and suggest starting with level 1, then working upwards.
Level 1: re-price the book to optimise margins given the current portfolio composition. In other words, re-price inelastic customers who are currently being offered rates higher than they need to be.
Level 2: re-price the book to further optimise margins taking into account cross-flow between products for specific customer types, aiming for funding neutrality.
Level 3: once the portfolio is optimised for margin, re-price again to obtain an improved liquidity position given the current customer mix. This will likely involve making trade-offs between margin and liquidity: essentially, how far is the bank willing to deviate from margin-maximising pricing for the sake of improved liquidity? It does of course assume that at the individual customer level, there is a choice of deposit products available to that customer with different run-off rates, which will not always be the case.
Level 4: finally, re-price to shift the customer mix towards a target margin / liquidity position. This comes last because it will be a longer term game that may be better achieved using other levers, such as marketing campaigns designed to target specific customer or product types.
Having access to sophisticated tools to support deposit pricing decisions has never been more important. Building such tools requires investment, as well as a firm business-wide commitment by a bank to re-engineer its pricing processes. However, the financial prize on offer far outweighs the cost of implementing such an approach, and in our view, banks adopting these kinds of pricing tools will gain a major competitive advantage over those who do not.
If you are interested in discussing this topic further, please feel free to contact the author.