After a long period of negative policy rates within Europe, the past two years marked a period with multiple hikes of the overnight rate by central banks in Europe, such as the European Central Bank (ECB), in an effort to combat the high inflation levels in Europe. These increases led to tumult in the financial markets and caused banks to adjust the pricing of consumer products to reflect the new circumstances. These developments have given rise to a variety of challenges in modeling non-maturing deposits (NMDs). While accurate and robust models for non-maturing deposits are now more important than ever. These models generally consist of multiple building blocks, which together provide a full picture on the expected portfolio behavior. One of these building blocks is the calibration approach for parametrizing the relevant model elements, which is covered in this blog post. 

One of the main puzzles risk modelers currently face is the definition of the expected repricing profile of non-maturing deposits. This repricing profile is essential for proper risk management of the portfolio. Moreover, banks need to substantiate modeling choices and subsequent parametrization of the models to both internal and external validation and regulatory bodies. Traditionally, banks used historically observed relationships between behavioral deposit components and their drivers for the parametrization. Because of the significant change in market circumstances, historical data has lost (part of) its forecasting power. As an alternative, many banks are now considering the use of forward-looking scenario analysis instead of, or in addition to, historical data. 

The problem with using historical observations 

In many European markets, the degree to which customer deposit rates track market rates (repricing) has decreased over the last decade. Repricing first decreased because banks were hesitant to lower rates below zero. And currently we still observe slower repricing when compared to past rising interest cycles, since interest rate hikes were not directly reflected in deposit rates. Therefore, the long period of low and even negative interest rates creates a bias in the historical data available for calibration, making the information less representative. Especially since the historical data does not cover all parts of the economic cycle. On the other hand, the historical data still contains relevant information on client and pricing behavior, such that fully ignoring observed behavior also does not seem sensible.  

Therefore, to overcome these issues, Risk and ALM managers should analyze to what extent the historically repricing behavior is still representative for the coming years and whether it aligns with the banks’ current pricing strategy. Here, it could be beneficial for banks to challenge model forecasts by expectations following from economic rationale. Given the strategic relevance of the topic, and the impact of the portfolio on the total balance sheet, the bank’s senior management is typically highly involved in this process.  

Improving models through forward looking information 

Common sense and understanding deposit model dynamics are an integral part of the modeling process. Best practice deposit modeling includes forming a comprehensive set of possible (interest rate) scenarios for the future. To create a proper representation of all possible future market developments, both downward and upward scenarios should be included. The slope of the interest rate scenarios can be adjusted to reflect gradual changes over time, or sudden steepening or flattening of the curve. Pricing experts should be consulted to determine the expected deposit rate developments over time for each of the interest rate scenarios. Deposit model parameters should be chosen in such a way that its estimations on average provide the best fit for the scenario analysis. 

When going through this process in your organization, be aware that the effects of consulting pricing experts go both ways. Risk and ALM managers will improve deposit models by using forward-looking business opinions and the business’ understanding of the market will improve through model forecasts. 

Trying to define the most suitable calibration approach for your NMD model?

Would you like to know more about the challenges related to the calibration of NMD models based on historical data? Or would you like a comprehensive overview of the relevant considerations when applying forward-looking information in the calibration process?

Read our whitepaper on this topic: 'A comprehensive overview of deposit modelling concepts'

Economic instability, a pandemic, geopolitical turbulence, rising urgency to get to net zero – a continuousstream of demands and disruption have pushed businesses to their limits in recent years. What this has proven without doubt is that treasury can no longer continue to be an invisible part of the finance function. After all, accurate cash flow forecasting, working capital and liquidity management are all critical C-suite issues. So, with the case for a more strategic treasury accepted, CFOs are now looking to their corporate treasurer more than ever for help with building financial resilience and steering the business towards success.

The future form of corporate treasury is evolving at pace to meet the demands, so to bring you up to speed, we discuss in this article five key observations we believe will have the most significant impact on the treasury function in the coming year(s).

1. A sharper focus on productivity and performance

Except for some headcount reductions, treasury has remained fairly protected from the harsh cost-cutting measures of recent years. However, with many OPEX and CAPEX budgets for corporate functions under pressure, corporate treasurers need to be prepared to justify or quantify the added value of their function and demonstrate how treasury technology is contributing to operational efficiencies and cost savings. This requires a sharper focus on improving productivity and enhancing performance.

To deliver maximum performance in 2024, treasury must focus on optimizing structures, processes, and implementation methods. Further digitalization (guided by the blueprint provided by Treasury 4.0) will naturally have an influential role in process optimization and workflow efficiency. But to maintain treasury budgets and escape an endless spiral of cost-cutting programs will take a more holistic approach to improving productivity. This needs to incorporate developments in three factors of production – personnel, capital, and data (in this context, knowledge).

In addition, a stronger emphasis on the contribution of treasury to financial performance is also required. Creating this direct link between treasury output and company financial performance strengthens the function’s position in budget discussions and reinforces its role both in finance transformation processes and throughout the financial supply chain.

2. Treasury resilience, geopolitical risk and glocalization

Elevated levels of geopolitical risk are triggering heightened caution around operational and financial resilience within multinationals. As a result, many corporations are rethinking their geographical footprint and seeking ways to tackle overdependence on certain geographical markets and core suppliers. This has led to the rise of ‘glocalization’ strategies, which typically involve moving away from the traditional approach of offshoring operations to low-cost destinations to a more regional approach that’s closer to the end market.

The rise of glocalization is forcing treasury to recalibrate its target operating model to adopt a more regionalized approach. This typically involves changing from a ‘hub and spoke’ model to multiple hubs. But the impact on treasury is not only structural. Operating in many emerging and frontier markets creates heightened risks around currency restrictions, lack of local funding and the inability to repatriate cash. Geopolitical tensions can also have spillover effects to the financial markets in these countries. This necessitates the application of more financial resilience thinking from treasury.

3. Cash is king, data is queen

Cash flow forecasting remains a top priority for corporate treasurers. This is driving the rise of technology capable of producing more accurate cash flow predictions, faster and more efficiently. Predictive and prescriptive analytics and AI-based forecasting provide more precise and detailed outcomes compared to human forecasting. While interfaces or APIs can be applied to accelerate information gathering, facilitating faster and automated decision-making. But to leverage the benefits of these advanced applications of technology requires robust data foundations. In other words, while technology plays a role in improving the cash flow forecasting process, it relies on an accurate and timely source of real-time data. As such, one can say that cash may still be king, but data is queen.

In addition, a 2023 Zanders survey underscored the critical importance of high-quality data in financial risk management. In particular, the survey highlighted the criticality of accurate exposure data and pointed out the difficulties faced by multinational corporations in consolidating and interpreting information. This stressed the necessity of robust financial risk management through organizational data design, leveraging existing ERP or TMS technology or establishing a data lake for processing unstructured data.

4. The third wave of treasury digitalization

We’ve taken the three waves of digitalization coined by Steve Case (former CEO of US internet giant AOL) and applied them to the treasury function. The first wave was the development of stand-alone treasury and finance solutions, followed by the second wave bringing internal interfaces and external connectivity between treasury systems. The third wave is about how to leverage all the data coming from this connected treasury ecosystem. With generative AI predicted to have an influential role in this third phase, corporate treasurers need to incorporate the opportunities and challenges it poses into their organizations' digital transformation journeys and into discussions and decisions related to other technologies within their companies, such as TMS, ERP, and banking tools.

We also predict the impact and success of this third wave in treasury digitalization will be dependent on having the right regulatory frameworks to support its implementation and operation. The reality is, although we all aspire to work in a digital, connected world, we must be prepared to encounter many analogue frictions – like regulatory requirements for paper-based proof, sometimes in combination with ‘wet’ signatures and stamped documents. This makes the adoption of mandates, such as the MLETR (Model Law on Electronic Transferable Records) a priority.

5. Fragmentation and interoperability of the payment landscape

A side effect of the increasing momentum around digital transformation is fragmentation across the payments ecosystem. This is largely triggered by a rapid acceleration in the use of digital payments in various forms. We’ve now seen successful trials of Central Bank Digital Currency, Distributed Ledger Technology to enable cross border payments, a rise in the use of digital wallets not requiring a bank account, and the application of cross border instant payments. All of these developments lead us to believe that international banking via SWIFT will be challenged in the future and treasurers should prepare for a more fragmented international payment ecosystem that supports a multitude of different payment types. To benefit from this development, interoperability will be crucial.

Conclusion: A turning point for treasury

A succession of black swan events in recent years has exposed a deep need for greater financial resilience. The treasury function plays a vital role in helping their CFO build this. This is accelerating both the scale and pace of transformation across the treasury function, with wide-ranging effects on its role in the C-suite, position in finance, the priorities and structure of the function, and the investment required to support much-needed digitalization.

For more information on the five observations outline here, you can read the extended version of this article.