Successful businesses care for extensive cash flow planning. Mature finance teams follow best practices, to review everything on a regular basis to see what actually happened versus what they said would happen. Being aligned with your payment terms for payables and receivables, or planning your cash needs for the next quarter or year is not a minor task.
Many finance teams that we talked to had the data to make it right, but there was some hiccup which hampered end results.
Many of them struggled across these –
Cash flow forecast has traditionally been performed based on experience and intuition, in an excel environment. When a history of counterparties and profit center is available on the business intelligence tool, cashflow prediction can be scientific.
Predicting cash flows is a quantitative estimate of cash inflow and outflow for future periods. For any finance firm, efficient management of accounts receivable is more challenging than accounts payable because accounts receivable are dependent on factors not controlled by the firm. And, in the long run, accounts receivable also impact accounts payable. How quickly your customers pay their bills impacts how quickly you can pay your bills.
Cashflow forecasting is one field where methods and process maturity vary from one finance team to another. Every corporate team is working with methods and tools evolved over time. The majority of current cash flow prediction methods assume that invoices will be paid before or on their due date —� an inaccurate assumption. Robust real cash flow predictions should consider the probabilistic nature of the invoice payment date.
iNatrix has developed a model which takes into account the payment behavior of counterparties. The model analyzes historical behavior of a counterparty on invoices raised and then finds patterns in payment behavior over different invoice parameters. The observed payment behavior can be extrapolated, to predict the expected payment date on a newly raised invoice. The newly devised model is based on a state-of-the-art machine learning algorithm� projective adaptive resonance theory (PART) to classify the expected payment date of an invoice into different pre-determined time periods.
In the next part of this blog, we will be discussing the output of the model, how predictive financial analytics can be beneficial to businesses.
Vinay is a data scientist with far fledged experience in transportation, industrial engineering and B2B marketing. He works with teams to identify the inherent need of the business and how data analytics can help, particularly in maintenance (predictive and preventive).