Non Banks using Information Advantages to Finance trade credit

Networks contain valuable data on the performance history of buyers and their suppliers. Today, most of the financing is done on one event, an approved invoice. Tomorrow, financing could be done based on many events – purchase order, materials ordered, factory about to ship, etc. Funds could be released at certain triggers. Far-fetched? Maybe.

But that is where we are moving with many B2B and Supplier Networks that are using the data contained in their network to enable funders to lend money.

By using performance history data along with visibility and underwriting models, the market for “information advantaged” finance is about to boom. Will it be successful? Does big data lead to better underwriting decisions? We will soon find out in the coming year.

As I said in a prior post, networks have transactional data that is more predictive than any other external trade credit information or any other credit related underwriting information.  What’s critical is to ensure the data is transactional down to a deep level.

The challenge is to build intelligent underwriting models that can take this data and make credit decisions. There are many questions to ask. Here are a few:

  • How to use the data contained in supplier networks to make a finance decision?
  • What data is required (historical data on trading relationship)?
  • Does the data extend across business cycles?
  • Does it include dispute and dispute resolution?
  • Can you actually track an invoice to a payment?
  • What risk mitigation techniques are required? e.g., trade credit insurance, cash dominion control of accounts, etc.

Another challenge is the technology to do the securitization. This is not trivial by any means.

In a zero (or very low) short term interest rate world, interest by funders in these models are very high. Putting the above together is complicated and very hard. Finding the parties to do the algorithms to analyze the data, accountants to test the models, building underwriting models off the data, developing the right structures to invest in these flows and finding the right investors is a delicate dance. But the size of the prize is certainly big, as you can offer buyers and suppliers in supply chains rates that accommodate the risk of the transaction, backed up by historical data.

Should be fun!

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