For a predictive analytics project in the field of cash flow forecasting (or for any project relying on time series analysis):
The first step, which is undoubtedly the most complex, consists of listing all the necessary data sources (both endogenous and exogenous).
This may be the TRMS (cash and risk management system), the ERP (integrated management software package), the information system of the “financial consolidation” function, statements of accounts, etc., for endogenous data. And from external sources for exogenous data.
Once this data has been identified and consolidated in a data warehouse, it is necessary to clean it (add missing data, remove redundant and aberrant data)…, then to homogenize it.
These data must be subject to visualization processes, allowing them to be apprehended in a concrete way (visualization according to the main explanatory variables composing them), before an advanced analysis phase (in terms of seasonality, correlations, etc.).
The modeling step can then start.
Just as there is no single transformation methodology, there is no single modeling technique. Some techniques may be more or less effective depending on the data. The proposed solution must be able to suggest the most efficient model based on the profile of the data analyzed.
Finally, the models based on self-learning techniques will evolve according to new data feeding the database.
Exceptional events, such as COVID-19, or a one-time external growth operation cannot be anticipated through a model. The intervention of the business expert is decisive at this level to correct these limits (when possible).
It is also essential to have “back testing” and “out of sample” analysis processes (analysis of errors based on a retraining process carried out on real data) so as not to introduce bias into the model.
In all cases, the experience of the treasurer and his critical eye on the results provided by the models are essential to detect possible flaws in the modelling.