A new AI-led approach to credit risk and retailer financing
By Anais Masetti - 16 July 2020
It can be a real struggle to find the funds to grow in visibility and size for a retail business, especially in the age of giant online marketplaces of the likes of Amazon and Alibaba.
Small merchants are more vulnerable to volatile demand and to disruptions in the supply chain, making their lack of liquidity and predictability undesirable to most banks and incumbent lenders. Indeed, most traditional lenders are yet to adapt their credit rating and lending practices to the needs of new digital merchants.
Thankfully fintech startups are starting to recognise the untapped potential of artificial technology (AI) models applied to the lending space. Here we look at one particularly promising newcomer, placed among the top of over 3000 rated European startups.
Tackling the lack of financing options for e-commerce retailers
The global retail market is huge and still growing. E-commerce is expected to represent 17.5% of retail sales in 2021 and the share of e-commerce sales done through marketplaces is predicted to reach 67% in 2022. For 80% of these merchants, their biggest concern is working capital for their growth.
Meanwhile banks and other traditional financing providers remain reticent to lend to young merchants with volatile activity. On top of this, their digital customer experience often leaves to be desired and their one-size-fits-all products are sometimes unsuitable for small retail businesses.
These eye-opening facts were shared at a recent conference by Nikolaus Hilgenfeldt, the co-founder and CEO of fintech startup Myos. A startup that aims to bring some much-needed disruption to the lending market.
A new approach to credit decision-making
Founded in 2017 and based in Berlin, Myos has built a product-based working capital financing platform for merchants.
With Myos, merchants can receive liquidity up to seven digits without having to provide any guarantees. It also offers flexible repayment plans rather than traditional fixed annuities. Its innovative approach centres credit decisions on the products, used as collaterals, rather than on the company’s fundamentals and credit history.
Using machine learning, it evaluates credit risk based on the sales potential of trading products. Moreover, its proprietary risk scoring data model analyses product, market and merchant data such as pricing and volatility to define financial risk.
Myos’ key business growth drivers
Rated by Early Metrics in October 2019, Myos placed among the top 10% of startups with strong growth potential. In fact, it has already raised €10 million last year in a funding round that saw the participation of Mountain Partners, Berlin Technologie Holding, Avala Capital and renowned business angels.
One of the key strengths of the startup lies in the founders’ extensive experiences in the payment and financing ecosystem within corporates and startups. The rating also highlighted that it has demonstrated an ability to quickly progress in its technical roadmap and generate commercial conversion just as fast, thanks to an efficient company structure.
Although it currently faces little direct competition from the startup ecosystem, Myos might have to compete with well-established financing providers that are addressing similar targets, such as banks offering venture debt. That is why its partnerships with Deutsche Handelsbank and Raising Bank are particularly important, granting the startup the strategic support to hold its ground against competitors.
Finally, with the uncertainty brought about by the coronavirus pandemic and the increase in retailers going digital, it’s safe to assume that the market for merchant growth lending will continue to represent an interesting opportunity for fintech startups and traditional lenders alike. Myos is a great example of practical AI applications that can tackle a real market need and positively disrupt a slow-moving sector. We expect incumbents to be increasingly open to adopting or investing in these types of solutions and technologies in the short to mid-term.