Tag Archives: Elasticity

Modeling Deposit Price Elasticity: Where’s the Value?

Deposit Price Elasticity Modeling FICO Modeling Deposit Price Elasticity: Where’s the Value?

The ability to model deposit price elasticity is becoming a core component of deposit portfolio management. In my previous posts on this topic, I discussed:

This post focuses on benefits once models have been completed and are in use. What should you expect to gain from deposit price elasticity models and what can you do with them to maximize benefit to the business?

The main function of a deposit pricing team is to forecast the future performance of their portfolio and a substantial amount of time is spent answering questions such as: How much new money will this rate bring in? How will this cannibalize my more profitable back book? What is the impact of the new rate on my portfolio P&L? What if the market changes rates?

Scientifically derived deposit price elasticity models streamline the answering of these and other questions. Moreover, as the core of a deposit forecasting solution, these models improve the accuracy, efficiency and accountability of the entire pricing process.


Price elasticity models are not affected by qualitative bias and provide a level of accuracy not achievable by gut instinct alone. Rather than focusing on individual products, the modeling suite should have the ability to create simulations of the entire portfolio, incorporating balance movements into and out of the bank as well as the effect price changes have on other products and the resulting impact on the bottom line. Simulations of future behavior should have the ability to predict the impact of price changes and allow the user to flex assumptions around competitor pricing, changes to the macroeconomic environment and internal profit assumptions.

Using this approach we achieve a better understanding of customer behaviors and the associated sensitivities of the bank’s liquidity as a whole. The best system of models tracks the impact of price changes so that previous decisions can be reviewed, appraised and the results fed back into the model calibration. This closed-loop process ensures models are continuously learning and adapting to changing market sentiment.


Another significant benefit of pricing analytics is that they accelerate the decision making process. Pricing managers can rapidly generate a number of different scenarios to study alternative pricing strategies, changes in competitor pricing assumptions or wider market factors. Providing the business with appropriate forecasting levers allows them to focus their expertise on pricing. Generation of evidence to justify pricing decisions becomes an automated process and this makes it possible to quickly iterate towards a pricing strategy that achieves the desired portfolio outcome.


When recommendations are based on transparent model drivers, conversations with internal stakeholders or senior management become easier as forecasted balances can be directly related back to internal or external modeling factors. The demonstrable action-effect behavior of pricing models also extends beyond the organization as they facilitate conversations with regulators.

In recent times, the regulatory burden on bank executives has grown such that transparency of the underlying pricing models is paramount. Pricing decisions must be explainable and the inputs and assumptions that sit behind them thoroughly documented. An ideal price-elasticity solution provides transparency to the underlying models, automates much of this governance process and provides an auditable structure for the entire pricing process.

Towards Optimization & Beyond

As I have discussed, price elasticity models that predict flows into, out of and between products are key to gaining a full understanding of a deposits portfolio. They serve as part of a broader analytic infrastructure underpinned by a strong data management system and a highly skilled analytic workforce. Ultimately, they empower the pricing team to make better decisions that are more accurate, quickly identified, and easily explained. These improved, data-driven processes have myriad benefits for everyone from the pricing analyst through senior management, partner stakeholders such as treasury, finance, marketing, and even external auditors.

However, the full value of a deposits portfolio can only be completely unlocked through the application of optimization, which is the final frontier in the construction of a comprehensive pricing solution. Full price optimization has the ability to discover revenue where a simple forecasting tool cannot. It optimally trades off balances and revenue across every product in the portfolio and finds the most efficient path to achieving the desired business outcome.

In my next post, I’ll discuss price optimization in more detail and how it can directly generate value to the deposits business.

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Modelling Deposit Price Elasticity: Challenges and Approach

This is the third in a series of blogs on deposit pricing, focusing on price elasticity modelling approaches and challenges.

The goal of any deposit price optimization solution is to make data-driven pricing decisions to manage portfolio balances and trade these off against the associated costs. These solutions should allow a pricing manager to prepare and run what-if analyses to assess the impact of pricing strategies, competitor price actions or movements in central bank base rates.

Fundamental to these solutions are price-elasticity models that capture and predict customer behavior as a response to pricing and other non-price factors. In this blog, we discuss the challenges and solution approaches for the development of robust price-elasticity models.

Price Response Signal

Price sensitivity can be measured with regards to product rate, market ranking, competitor rates or even interest paid to other products in the portfolio. The modelling challenge is not only to measure price sensitivity accurately, but to capture as much richness in pricing behavior as possible, e.g., variations in price sensitivity across different segments.

For example, ultra-low bank base rates have become the new normal in the US and Europe and it can be a challenge to isolate the price signal. There may be limited price variation in the modelling period or else one-time shocks caused by the presence of non-price related factors (such as Brexit) that drive balance flows. Previous experience of price sensitivity models across different markets, interest rate environments and transformation of price-related variables provides an anchor to avoid misdiagnosis of the price signal.


In order to truly understand the impact of pricing decisions, the flows between individual products and segments must be understood. This allows the prediction of the balance distribution by product and requires a tried and tested process for inferring balance flows. Understanding and predicting balance flows across products in turn allows pricing managers to assess the impact of cannibalization on pricing decisions and overall portfolio revenue.

Data Availability & Granularity

One of the biggest challenges for the development of price sensitivity models is data availability. The development of overly complex models with insufficient data results in the often-cited adage of garbage-in, garbage-out, so it is important to ensure that the modelling approach is appropriate to the data available.

Finer granularity, where more modelling segments are used, introduces richer pricing behavior and allows greater insights into pricing decisions. This must be balanced with the need for sufficient deposits within each segment to ensure a statistically significant price signal can be measured.

Another consideration concerns the model resolution: An established deposit portfolio with relatively stable balances might only re-price a few times a year and a monthly granularity is sufficient. On the other hand, a smaller bank that needs to make shorter-term funding decisions might buy balances through the introduction of market-leading rates. This type of pricing action typically occurs more frequently and would therefore need a weekly granularity.

Modelling Methodologies

Depending on a bank’s objectives and the availability of data, there are a number of modelling approaches that might be considered.

Deposit Price Elasticity Modelling Approaches FICO Modelling Deposit Price Elasticity: Challenges and Approach

Model Management

It is vitally important that all stakeholders from the pricing analyst to senior management have confidence in pricing models. The best way to achieve this is to ensure full transparency of the models, methodology and the factors that drive predictions.

With a full understanding of model drivers, the business can justify why particular pricing decisions are made to internal stakeholders and also answer any challenges posed by external regulators that a “black box” solution could not.

An ongoing model management process should monitor model performance against established accuracy thresholds to guide model recalibration and redevelopment. This ensures that models respond to changes in the market and provides a mechanism whereby the impact of recent pricing decisions feed back into the price sensitivity models.


In order to develop deposit price sensitivity models, careful consideration is needed to evaluate an organization’s requirements and mitigate some of the challenges discussed here. The deployment of such models offers substantial improvements on approaches that rely exclusively on expert judgment. It allows banks to more effectively manage their deposit portfolio, better understand their customer behavior and preferences, and identify new revenue opportunities. It is also a critical component on the journey towards full price optimization where banks derive all the benefits that data driven models have to offer.

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Modeling Deposit Price Elasticity: What Is It All About?

Deposit Price Optimization Modeling Deposit Price Elasticity: What Is It All About?

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 a product’s price change will impact the overall deposit portfolio, taking account of product cannibalization and source of funds
  • How to maximize revenue from the deposit portfolio using optimization (more on this in a future blog).

A price elasticity model enables the product team to set deposit rates with a sound understanding of the expected performance from the rate strategy, and understand the complex nonlinear relationship between product rates and demand.

Deposit Pricing and Customer Behavior

Analytically understanding customer behavior is critical in establishing the deposit pricing strategy. A large number of potential characteristics can be considered in the development of models, such as:

  • Product details relative to the whole market, or different competitor peer groups
  • Product details relative to neighboring products
  • Existing customer product holdings, including those reaching product maturity
  • Macro-economic data
  • Brand / market awareness
  • Seasonality trends
  • Indicator information (to take account of exceptional events)

The final model structure and characteristics are considered carefully to ensure robust relationships, across the suite of models. It is also critical to understand how the models interact and perform on data more recent than what was considered for the model development, as well as outside of historical price points. This helps gain buy-in from the product teams who are going to be using the models to set pricing.

Benefits seen from using price elasticity data driven models are:

  • Deposit Portfolio Impact: To understand the impact of a pricing action on account applications, bookings and funding, taking account of competitor pricing, central bank rates, yield curve and how the pricing action impacts other products.
  • Source of Funds: To predict the source of funds, which help determine if a pricing action is resulting in net new business or if the same deposits are being cannibalized from an older low-rate account to a new higher-rate account.
  • Regulation: Not having statistically built models to make pricing decisions will be viewed as guesswork from subject matter experts. You need to be able to point internal auditors / regulators to a price elasticity model that has gone through the bank’s governance process.

A further benefit of developing price elasticity models is to make yourself more price-elastic, and review the deposit products you hold!

In my next post, you’ll learn about some of the challenges and requirements when building effective price elasticity models. I invite you also to find out more about FICO’s deposit price elasticity modelling and optimization.

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