Predicting dilution – key for invoice finance solutions
Sabeen Ahmed, COO and Chief Credit Officer of The Interface Finance Group discuss about dilution and its impact on receivable financing.
There are few truisms left anymore especially in this era where trust is at a premium and everything appears to be a product of fake or fiat news. But in terms of the risk, funding receivables based on seller’s data or funding receivables based on buyer’s data, there is truth and that truth is buyer data is more reliable. Why? The simple reason is that helps in predicting dilution. In this day where new forms of invoice finance are the rage, there are many things that can go wrong with financing receivables.
Naturally, there are a number of differences between traditional and digital invoice finance services when it comes to onboarding, processing, underwriting and funding. Digital invoice finance players are designed to provide funding during a single (sometimes two) online session(s). As such, they don’t really have the ability to fully address invoice quality and verifiable deliverables, which are important components of invoice finance underwriting and risk mitigation.
In addition, simply pulling invoices from the sellers accounting systems (ie seller centric approach where providers are integrated with many cloud accounting systems and have the ability to instantly pulling data from the majority of desktop accounting systems), may help speed up the process but can’t really tell you if the invoices have been approved and scheduled for payment by the buyers (account debtors).
Obviously, integration with the buyers account payable system either directly or via third party platforms offers the ability to pull a lot of additional information about approved invoices.
But even when invoices have been approved and scheduled for payment, the risk of dilution still exists. Post-confirmed invoice dilution can take a number of forms including credit memos, nonspecific invoice related chargebacks, withholdings, counterclaims, tax issues, judgments and so on.
Some of the more advanced players are trying to address this issue in a number of ways: through a fully automated digital supply chain finance service based on fast data and a dynamic credit limit engine or with big data and machine learning.
Clearly, this critical issue is getting more attention and the rate of progress is quite exciting but, as of yet, no single solution has been tested on a large scale in the market.