You're navigating a volatile market. How can you use data analysis to predict cash flow trends?

Well, data is the basis of analysis. It's important to have the right data and an understanding of the data. Every organization has its own way of collecting, recording, consolidating and presenting data. One needs to go into detail of these processes before starting of analyzing the data. A preliminary exercise to check anomalies and consistency is equally important.

It is helpful to first make an assessment of the sources and uses of cash. This begins with ensuring that all of the financial institutions and accounts have been identified. Each will have some limitations on the output of data with respect to formats and fields of data. Plan for time to request access to all platforms and reporting roles for each institution.

Most organisations have a cashflow made up of day to day transactions that are the meat and drink of the organisation but with an added complication of other transactions that only occur quarterly or yearly. For instance a football club will one month of heavy season ticket sales, maybe two months of large transfer spend and depending on the terms, loan repayments say quarterly or maybe annually. Disentangle these unrelated cashflows and forecast them separately. An algorithm like Winters Exponential Smoothing would be useful for day to day transactions. Transfer Spend and transfer income would need a more contractual analysis as would loan repayments. Complete the forecast for each type of spend then consolidate the cashflows.

Trend analysis is one of the techniques used to predict the future cash flows. By doing a proper analysis, one can identify the regular items and establish the basic trends of movements in inflows and outflows. Look out for exceptional items in the data as the same can lead to wrong conclusions and result in erroneous prediction.

One of the keys to successful predictive analytics is using the right model. Last year, I needed to forecast product demand to optimize a company's inventory production and cash flow. I tested both a machine learning approach and the classical ARIMA model, which is the gold standard for time series problems. Although the machine learning approach gave better results, it's crucial to keep stress-testing to see how extreme market conditions or unexpected events might affect the outcomes. As @Prakash Sharma said in his contribution, variables can fluctuate significantly, so ongoing evaluation is essential.

When predicting future cash flows, one has to consider variables which can fluctuate significantly. If the organization is dealing with products or services that do not follow a clear pattern, plan for scenarios of ups and downs in inflows while providing for regular outflow on account of fixed overheads. Always make a provision for contingencies.

For cash flow forecast, data analysis has to be done on a continuous and regular interval basis. The assumptions we make are subject to change. The overall environment as well as the conditions of your customers and suppliers keeps evolving. Periodically, the forecast should be compared with actual numbers to identify the weaknesses in the model like insufficient data, lack of consultation with stakeholders etc.