Using analytics in liquidity management helps Treasury prepare for an uncertain future

Using analytics in liquidity management helps Treasury

The risk of liquidity shortages increases in times of economic uncertainty.

Börries Többens

Partner, Financial Services, Finance and Treasury Management

KPMG AG Wirtschaftsprüfungsgesellschaft

Treasury Analytics

Treasury can identify actions necessary with targeted stress testing in liquidity management and scenario analyses.

Stress testing is mainly used in the financial industry. For corporate liquidity, this means that the impacts and changes in risk factors are simulated to forecast the resulting liquidity.

In practice, many companies are finding this difficult (especially in view of the available data and the preparation of such analyses). For instance, oftentimes the necessary data is not available with sufficient accuracy or granularity. In order to perform the analysis, an ad-hoc Excel solution is developed that does not facilitate a continuous and dynamic input of analysis parameters. So that the analysis model functions, four steps have to be defined, coordinated and operationalized:

  1. A transparent planning of the cashflows is the foundation of a solid liquidity forecast.
  2. A scenario catalog that is mapped to the business targets and the corporate environment where all of the important parameters are defined.
  3. Automated data input that has been tested for its quality in order to plan and analyze the individual scenarios.
  4. A reporting that has the granularity appropriate for the audience as well as a standardized and automated reporting process embedded in a reporting solution (e.g. BI).

Transparent and centralized liquidity planning serves as a basis for analyses

Reliable and transparent cashflow plans, available liquid funds and funding instruments (for instance, credit lines) are required for a valid stress tests. Beyond that, a driver analysis of the planned cashflow is necessary to enable a conclusion regarding endogenous and exogenous cashflow dependencies. In this case, it is therefore not sufficient to look at planned cashflows in terms of allocation, i.e. in the form of lists of proposed payments or other cash positions. 

Rather, cashflow plans should be categorized according to the funds used, for instance, differentiating between payments to suppliers and payments for investments. This is the only way one can allocate individual drivers to the relevant category, allowing for a detailed change in parameters during the scenario analyses. Consistently centralizing the planning allows for comparability over time and enables not only forecasting but also better planning using the insights gained from the backtesting.

Mapping the scenario catalog to the company’s business targets and environment

The starting point for a scenario analysis is a compilation of specific drivers and risk factors in order to define the following stress tests. This could include market price developments of essential commodities, the development of sales or revenue components or the availability of credit lines. The best possible way to determine relationships and correlations between drivers and the relevant cashflow positions is with historic amounts. Scenarios are then defined that look at the possible interplay of endogenous and exogenous factors and their impact on the drivers identified. Beyond that, input factors from traditional risk management should also be considered.  For example, these could be:

  • Currencies: development of the most important exchange rates in comparison to commodity prices and the functional currency.
  • Interest rates: development of variable payment commitments arising from let’s say credit commitments or bond issues, as well as cash inflows from investments made.
  • Banks: development of counterparty risk KPIs such as ratings or CDS spreads. For instance, a scenario should simulate the impact of the default of at least one core bank.
  • Customer payments: the analyses should consider time series on the payment behavior and defaults of customers. Changes could have significant implications for a company’s liquidity.
  • Own payments: here, the consequences of own payment conditions to third parties should be examined and how internal cash flows are managed, for instance, to subsidiaries. 

Once it has been established, such a scenario catalog should be used continuously and, if necessary, expanded.

Automated data input saves time and improves the quality of the results

The above-mentioned list of potential risk factors shows how complex the interplay of effects is, namely high or maybe even uncontrollable. Even though complexity should be kept to a minimum, it makes sense to use analytics for the data modelling of relationships.

As this is the basis for sufficiently flexible but still controllable solutions, we would like to into more detail as to how it is done. Ideally, the data necessary for the analyses and stress tests should be aggregated in a dedicated data hub. ETL processes are especially suitable for this. ETL stands for Extract, Transform and Load, the individual phases following each other in automated data processing.

Extraction: data is extracted from internal and external data sources (for example, currencies) and stored in a database to be used for processing.  

Transformation: this step has two sub-steps: initially, data is cleansed, its quality checked and then processed. Then the values and KPIs needed in the reporting, the scenarios and the KPIs are calculated. For instance, data modeling could determine how lower sales will impact procurement and fluctuations on own inventory levels. Finally, the modeling can also simulate the impact on existing covenants, such as net financial debt.

Loading: the last step is the part where the data is allocated to all relevant programs in the company’s IT landscape. Generally, this data is used by BI tools or other software to crunch them further in complex calculations.

Ideally, no manual interventions are necessary in this process once it has been set up. The high degree of automation will significantly reduce the analysis efforts. 

Reporting with the help of Business Insights meets the expectations of the audience, making for effective business decisions

Modern BI reporting tools are handy for mapping the relationships and correlations of the different drivers and cashflow positions identified earlier. To simulate events and developments, the drivers in the input mask can be adjusted in order to analyze their impact on the planned cashflow. These should consider the available liquid funds as well as the credit lines in order to get a holistic picture of the overall liquidity situation.

What is of particular advantage is a function that allows commenting and distinguishing versions in order to be able to track changes in the reporting. This also allows a critical appraisal by a reviewer and documents the decision criteria. Data granularity can be ramped up considerably with the so-called drill-down functionalities. Thanks to the drill-downs it is no longer necessary to go back and forth, as recipients can pull up this information themselves. Thanks to the centrally available and detailed data, decision paths within the financial department and operational units will not only become more efficient but also of a higher quality.


Mapping the scenario analyses during stress testing with the help of analytics enables the continuous identification of impacts of changing risk factors. It also allows the observation of anticipated developments once these come to pass. This then allows an early recognition of cashflow developments. In turn, actively managing cashflow may make for a more flexible reaction to expected situations. 

Automated analyses are becoming more and more significant in companies of all sizes. Of course, predictive analyses do not protect against all risks, but they help make it easier to manage a company at all levels of responsibility. Starting out with liquidity targets and risk aversions, the complexity of the analysis possibilities should be evaluated carefully. Our maxim is: keep it as complex as necessary, as simple as possible.

Automation also enables persons that would otherwise be engaged with analyzing to focus on tasks that add value. It is easier to achieve important KPIs, such as the reduction of cost for external financing or more efficient investments of funds this way. But above all, the possible in-depth analysis allow treasurers to draw Management’s attention to future developments in the liquidity buffer and to start countermeasures in good time.

Source: KPMG Corporate Treasuy News, Edition 105, October 2020

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