Skip to main content

A unique suite of Machine Learning (ML) algorithms to battle cashflow problems in SMEs

Periodic Reporting for period 1 - Zero1 (A unique suite of Machine Learning (ML) algorithms to battle cashflow problems in SMEs)

Reporting period: 2019-08-01 to 2020-01-31

The issue of slow or late payments from buyers can spell disaster for many SME suppliers. Payment terms generally range from 30 to 90 days, with up to 40% of these invoices paid late, creating a serious cashflow problem.As a result, SMEs need access to short-term financing which is both affordable and quick enough to keep up with incoming orders.Yet banks are reluctant to loan to SMEs and their credit scoring methods are often unfairly harsh on them. Alternative online financing exists for SMEs, but many of these lenders charge elevated fees or don’t get funding to suppliers when it is most needed: at the start of production.Our solution: Zero1 is a unique suite of Machine Learning (ML) algorithms accessed through an easy-to-use web- and cloud-based platform. It is designed to finally solve the SME funding gap, allowing supplier SMEs to get funding as soon as they receive an order from their buyer, at an affordable interest rate.Our main objective is to validate Zero1 Algorithms, proving that its results (forecast of the Purchase Order behaviour) are statistically reliable and the results of implementing Zero1 Algorithms have value for Suppliers, Financiers and Buyers. We have worked with all the parties involve in order to validate all the aspects, fine tuning our solution with the market and a pilot with customers. The results of the Phase I project have been very successful, where all the parties have noted a clear benefit.:-Suppliers: Access to better finance, both earlier (on average several days before) and cheaper (on average several price p.a. less).-Buyers: A Supply Chain that is more reliable and efficient, with an additional benefit of begin able to monetize their data by obtaining a fee for its use. -Financiers: A new product offering to their Corporate clients (which Corporates are demanding), access to more lending transactions (increasing the volume of their Supply Chain Finance Program) which are of higher quality (linked with the larger buyer’s risk) and with better risk assessment (based on the behaviour of each order), thereby obtaining a relevant improvement in their net profitability.Thanks to the support obtained by the SME instrument in Phase 1, we are able to conclude that Zero 1 project is feasible and viable, so we expect to get more funds in a near future to continue with the acceleration of it.
During SME instrument phase 1, we have partnered (i) with a large buyer (leading FMCG multinationals), developing a Proof of Concept with a limited scope (Europe), applying Zero1 algorithm in its Supply Chain database, validating the value of Zero1 as high attractive to the MNC as the large buyer, and to its Suppliers (mainly European SMEs); and (ii) with large global Spanish Bank, Private Funds and Credit Rating Agencies, validating the value of Zero1is also highly attractive to the lender/ financier, but with the Bank we also have validated the regulatory requirements and the most efficient structure. We realised that Zero1 is also very attractive as a new bank product, opening a new business line, due to the Bank discussing how its Corporate clients are demanding a product like Zero1. So, during this phase 1 we also have begun the first contact with more Banks.The results of the Phase I project have been very successful, where all the parties have noted a clear benefit:-Suppliers: Access to better finance, both earlier (on average several days before) and cheaper (on average several price p.a. less).-Buyers: A Supply Chain that is more reliable and efficient, with an additional benefit of begin able to monetize their data by obtaining a fee for its use. -Financiers: A new product offering to their Corporate clients (which Corporates are demanding), access to more lending transactions (increasing the volume of their Supply Chain Finance Program) which are of higher quality (linked with the larger buyer’s risk) and with better risk assessment (based on the behaviour of each order), thereby obtaining a relevant improvement in their net profitability.
Beyond State of the art:Zero1 Algorithm has achieved the “minimum viable corpus” (concept of Andreessen Horowitz, leader of US Venture Capital)In the Market there are no other solutions applying AI to the Supply Chain Finance problem which can match our results.Thanks to the survey made to European SME suppliers, we confirmed Zero1 helps agri-food SMEs to increase their financial sustainability.
State of the art