Predicting dilution is essential for invoice finance solutions –


Trade Financing Matters welcomes this guest post from Sabeen Ahmed, COO and Chief Credit Officer of Interface Finance Group

Few truisms remain, especially in these times when trust is everything and everything seems to be a product of fake news or Fiat. But when it comes to risk, seller’s data debt financing or buyer’s data debt financing, there is a truth and this truth is that buyer’s data are more reliable. Why? The simple reason is that it helps predict dilution. As new forms of invoice financing become all the rage, there are many things that can go wrong with debt financing. One of the most popular items from Trade Financing Matter is 12 Risks to be managed when financing domestic trade receivables.

Naturally, there are a number of differences between traditional and digital invoice finance services when it comes to integration, processing, underwriting, and financing. Digital invoice finance actors are designed to provide funding in one (sometimes two) online session (s). As such, they don’t really have the capacity to fully address invoice quality and auditable deliverables, which are important parts of underwriting invoice financing and risk mitigation.

In addition, the simple act of extracting invoices from the seller’s accounting systems (i.e. a seller-centric approach like Blue vine and Fund of funds which are integrated with 3 or 4 cloud accounting systems or IFG which is built into 11 and has the ability to instantly pull data from the majority of office accounting systems), can help speed up the process but can’t really tell you if invoices have been approved and scheduled for payment by buyers (accounts receivable).

Obviously, integration with buyers’ accounts payable system, either directly or through third-party platforms, provides the ability to extract 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 many forms including credit notes, non-specific invoice chargebacks, holdbacks, counterclaims, tax issues, judgments, and more.

Some of the more advanced players are trying to solve this problem in several ways. IFG, for example, does this through a fully automated system Digital supply chain finance Fast data-driven service and a dynamic credit limit engine. Provide Data scientists have opted for big data and machine learning.

Obviously, this critical problem is gaining more and more attention and the pace of progress is quite exciting, but so far no single solution has been tested on a large scale in the market.

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