Cash Flow Forecasting Using Machine Learning for a Retail Group
Retail group with almost 4,000 retail points in Europe
Cash flow forecasting using Machine Learning techniques
- Identifying groups of flows with similar rules. The more granular a flow grouping is, the more precise the results. However, higher granularity implies more Machine Learning exercise and time to tune and maintain the models. The original grouping of flows into 56 groups was further aggregated to 10 groups.
- Identifying features and rules to predict the flows, like, for example, day of the week or day of the month
- Selecting the training period, that is the period of historical data used to create the model.
- Investigating data outliers. This consists of either assigning a new feature, excluding it from the model, or creating a separate machine learning model. For example, confronting the COVID-19 and post-COVID-19 period, which required additional reactive tuning.
- Finding an appropriate machine learning algorithm, which provides the lowest error.
- Running the tests and comparing the model results to the manual results, in terms of mean absolute error divided by the mean value of real data where the mean absolute error is the average absolute difference between prediction and real values.