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February 08, 2021

Using Data Analytics to Enhance Cash Management

Data Analytics
Posted by Guillermo Tello [1]

Financial management information systems (FMIS) put increasingly vast amounts of financial information at the fingertips of members of state treasuries, enabling them to analyze budget execution trends and to track the accuracy of cash flow projections.

However, these units do not always have enough resources or skills to produce timely and accurate financial information for effective decision making. This is partly due to the lack of complete system integration or interoperability. Cash management units were put to the test during the Global Financial Crisis and the COVID-19 pandemic given the increasing need to produce ad hoc financial reports. In many countries, government asset liability and cash management committees assemble each month to analyze trends in budget execution and cash forecasts. The aim is to minimize the opportunity costs of the large amounts of fiscal support being provided.  

A lot goes on behind the scenes in the preparation of these reports, from gathering many pieces of data from multiple sources to the manual input of this information into Excel spreadsheets. The results of these efforts have been mixed. Many countries are still experiencing a high degree of cash flow volatility as well as relatively large deviations between actual and forecasted numbers, creating both impediments to effective cash management and fiscal costs for the government.[2] Contributing to these mixed results are inaccurate spending plans from agencies, unreliable revenue projections, and the lack of access to timely information.

Business Intelligence (BI) is a tool to obtain high-value information to support the process of making decisions. BI uses processes and technologies to obtain information from different sources that is timely, accurate, and actionable. During the design of a BI system, the gathering of user requirements is critical. As a result, cash management and asset liability management committee members are interviewed to understand what core reports, performance indicators and other information are important to them in undertaking their business processes.

The use of data analytics can help government treasuries improve the accuracy of cash forecasting, which is one of the pillars of modern cash management.[3] Data analytics is no longer an exercise performed in the private sector alone as solutions have been developed with reasonable licensing costs, and many countries have acquired considerable experience and knowledge of the analytical techniques.

An example is the Government of Indonesia, where the Ministry of Finance’s Directorate General of Treasury is planning to take active cash management to the next level by implementing a BI platform. The objectives are three-fold: 1) to increase data digitization, 2) to obtain timely and actionable cash flow information to invest anticipated cash surpluses and borrow to cover anticipated shortfalls, and 3) to apply the use of a dashboard to monitor key performance indicators and improve the predictability of cash flow forecasts. Of course, it will be challenging to consolidate data from different systems so that it can be extracted, transformed and loaded into the BI tool. Some of these challenges are being addressed by Indonesia’s cash management unit teaming up with the information technology team to develop “data marts” that feed data from various operational systems, including the FMIS.[4]

The BI tool will allow governments to quickly spot budget execution trends by agency, quarter, year, and economic classification, as well as trends in revenue collections. Such knowledge can be used to adjust and increase the predictability of monthly, weekly and daily cash flow forecasts. In addition, ad hoc reporting and query capabilities are now possible with front-end tools such as online analytical processing (OLAP), thus allowing a shift from static reports to a multidimensional view of data that can be rearranged spontaneously in response to varying needs.

Other advanced data analytic tools that are becoming widely used include predictive analytics and machine learning which allow users not only to predict trends from historical data, but to make forecasts using calibrated models[5]. Selecting the right people with the necessary experience and analytical skills is, of course, crucial to accurately interpret the information produced by these tools.

The implementation of data analytical tools such as BI should allow government treasuries and their respective cash management units to improve the predictability of cash flow forecasts under both approaches: bottom-up (expenditures) and top-down (revenues). For many countries the tool could prove invaluable in enhancing cash management procedures while making treasuries more resilient during challenging times such as the COVID-19 pandemic.

 

[1] Treasury Management Advisor, U.S. Treasury, Office of Technical Assistance, Advisor to the Ministry of Finance in Indonesia.

[2] “Cash Management – How do Countries Perform Sound Practices”, M. Coskun Cangoz and Leandro Secunho, World Bank Group, 2020.

[3] “Characteristics of good practices in government cash management:  centralization of government cash balances via the implementation of Treasury Single Account, ability to make accurate projections of short-term cash inflows and outflows, use of short-term money markets instruments to manage balances and timing cash flow mismatches, and strong coordination of debt and cash management”. Government Cash Management: Its Interaction with Other Financial Policies, Mike Williams, International Monetary Fund, 2010.

[4] A data mart is a repository of summarized data for reporting and analysis aimed at supporting business processes and decision-making.

[5] Calibration is a technique that evaluates – and aims to improve - the effectiveness of predictive models by making adjustments according to their error distribution, i.e., a comparison of the actual output and the expected output.

Note: The posts on the IMF PFM Blog should not be reported as representing the views of the IMF. The views expressed are those of the authors and do not necessarily represent those of the IMF or IMF policy.

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