1. ICT and Cash Management and Forecasting
Mark Silins
Moscow
April 2017

2. Ideally, the IFMIS should be the Primary Source of data for Forecasting
Full integration of all possible cashflows into the IFMIS –
Daily Bank Reconciliation – increases reliability regarding the correctness of the cashflow transactions
Commitment Control – recording of contracts when they are legally enforceable – additional useful information on future cashflows
Due Date – terms of trade for the government – provides absolute certainty regarding contractual payments over a given period

3. Commitment Controls
By just capturing commitments for the top 5-10% of payments by value, you record 90% of the required cashflows future cashflows
Including indicative dates at the time commitments are recorded would obviously be a useful addition - including projects with multiple payment points

4. Due Date
Ideally, forecasting should be three months in advance to allow issuance of securities - but this is a capacity which can be developed over time – in the shorter term developing a due date concept can also be useful
Once goods and services are received you recognize the accounts payable. The government due date is a set period after this, typically 30 days.
You pay on the 30th day, not before and not after
This ensures a constant stock of 30 days of all upcoming contractual payments.
The IFMIS has absolutely perfect information on all contractual cash outflows for the next 30 days - these are the cashflows most difficult to forecast

5. The Use of Models in Liquidity Forecasting
Annual forecasting for the underlying cash balance and government liquidity forecasting are different and generally require different approaches
Few countries use complex econometrics models for government liquidity forecasting – simple historical data analysis generally works best, supplemented by the gathering of regular information from stakeholders
You can not just build a model where you plug numbers in for government liquidity forecasting- this Ignores the variability common in short-term cashflows
The problem is that the shorter the period the more volatile the flows – most intricate models fail to provide a useful analytical framework. Sometimes sophisticated tools hinder rather than help our work
Thus developing a daily cashflow historical dataset in excel or a database to help analyze future flows is likely to be the most effectively model
Who has a short term government liquidity model? What is each country using at present?

6. Building a Simple Model is Often the Best Solution

7. Linking down-from and up-to monthly forecasts is also key

8. Shared Folders for Regular Updating by Stakeholders is a Useful Strategy
This is sometimes referred to as a “Community of Practice”
It allows common files to be shared and updated within a specific group – for example the Cashflow Operations Committee
Inputs would be required from key revenue collectors at least weekly to update specific trends, issues and anomalies. Deadlines would be set within the committee rules to allow participants adequate time to review the new datasets and tables prior to the weekly meeting – excel templates for regular submissions could be available
At the weekly meetings the underlying assumptions and proposed changes would be discussed and consensus reached by the group
Daily data could also be shared where additional inputs are required where variations to forecasts exceed certain threshold percentages

9. Monitoring the Model is the key
It is a forecast, and given its short-term nature, volatility is normal
So monitoring each day and week is critical. Also examining whether short-term trends are becoming long term structural changes is important
Ensuring forecasting errors are monitored and investigated is particularly important in any forecast
This is not rocket science, but it is interesting how effective regular analysis can be in developing skills to predict future trends and anomalies