Financial management is the primary area of an ERP. It provides the tools for letting your company perform its work. Money availability is essential for personnel wages, for purchasing items to be sold, for parts you need to assemble into your products.

The journey reaches out to this ERP's section explaining the potential of Machine Learning in providing weighted suggestions supported by collected measurement. It sustains human choice with valued metrics.

Cash flow best fit

Another delicate function in ERP. The sensibility to choose when to anticipate payment requests to customers or postpone payments to suppliers comes from experienced interaction with the counterparts. ML helps here, providing the prospect of possible outcome depending on every payment date change (income or outgoing).

ML calculates the optimised set of changes to reach the most convenient result. When the operator modifies the proposed collection, it recalculates the adjustment in the current group. Optionally, it suggests a new optimisation if the calculated outcome would be much more beneficial.

General Ledger reconciliation

Keeping General Ledger updated should be an activity that goes straight and automatically complying. It needs time for settling up unconventional transactions like specific invoices or supplier bills in the real world.

Machine Learning helps here for the reconciliation suggesting the possible connections in General Ledger. Every adjustment executed by the operator improves the algorithm and the subsequent suggestion has always a better fit with the final acknowledge.

Investment management

It happens that sometimes some good occasions are lost because of a limited budget. Consulting the stored tracks of the lost tenders and their prospected ROI consumes elevated resources and, most of the time, without benefit.

ML here calculates the virtual income provided by the supposed ROI of every offer. It provides the best optimisation by a given budget. It calculates the best fit for every department and provides a forecast of the expected effective ROI.

Conclusions

The ML learns from the operator's choices and also gives in this vital area its "insights". It measures the suggestions by weighted metrics. It brings the facts into evidence as they are without any impact on human feelings. The highlight of purely calculated data provides the optimal tool to the operator for adjusting the choice.