There is a staggering $4.9 trillion financing gap for micro and small enterprises (MSEs) in emerging markets and developing economies (EMDEs). As discussed in our earlier blog post, digital technologies are enabling new business models that are starting to disrupt the traditional MSE lending value chain in ways that could increase MSEs‘ access to credit. While there are consumer protection dangers in some digital credit models, credit can also be harnessed for good. As part of CGAP’s research into MSE finance, we’ve identified several new business models that are emerging thanks to these new capabilities. Below are four models that stand out based on their ability to solve the credit needs of MSEs and to reach scale.
1. Digital merchant cash advance: Unsecured credit
The growing use of digital sales and transaction tools by MSEs has laid the foundation for a simple yet powerful model in plugging the credit gap. When lenders integrate their systems with these tools, they gain visibility into cash-flow records that can be used for credit assessments. They also allow for automatic deductions, reducing the risks associated with defaults while permitting businesses and lenders to set up dynamic repayment schedules based on sales volumes. This gives borrowers more flexibility than do traditional monthly repayment schedules.
Fintechs using this model reported nonperforming loan ratios as low as 3 percent in a recent CGAP study. A wide range of players have adopted it, including PayPal Working Capital, Kopo-Kopo Grow Loan, Amazon Lending, DPO’s Easy Advance loans and Alibaba’s PayLater. Merchant cash advance loans were estimated to be a $272 billion business in 2018 and are expected grow to $728 billion by 2025. The largest growth in lending volume is expected to come from China, where a quarter of businesses already use digital transaction tools.
2. Factoring: Credit secured against invoices
Factoring is a form of receivables- or invoice-based lending traditionally available only to large businesses in highly formal contexts. The growing availability of digital data on the sales and cash flows of small and semi-formal businesses is starting to enable the extension of this business model to broader MSE segments. By bringing down the cost and risk of credit assessment and by making digital repayments easier, digital invoicing https://paydayloansohio.net/cities/yellow-springs/ lets lenders offer this type of credit to small businesses.
Lidya, in Nigeria, is an example. Its clients can receive anywhere from $150 to $150,000 in cash in exchange for giving Lidya their corporate customer invoices at a discounted value, depending on the creditworthiness of the corporate customers.
The current market size for factoring-based credit in EMDEs is estimated to be around $1.5 billion. However, this lending model is expected to grow to a volume of $15.4 billion by 2025, driven primarily by the rapid increase in e-invoicing tools and the introduction of regulations in many countries requiring all businesses to digitally manage and record invoices for tax purposes.
3. Inventory and input financing: Credit secured against inventory or inputs
Digital tools for tracking and monitoring inventory purchases and turnover are enabling lenders to finance inputs and inventory with more appropriate credit terms. This is reducing the risk for lenders and helping borrowers avoid the temptation to use a business loan for other purposes.
For example, Tienda Pago is a lender in Mexico and Peru that provides MSEs with short-term working capital to fund inventory purchases through a mobile platform. Tienda Pago partners with large fast-moving consumer goods distributors that place inventory with small businesses, which help it to acquire customers and collect data for credit scoring. Loans are disbursed not in cash but in inventory. MSEs place orders and Tienda Pago pays the distributors directly. The MSEs then repay Tienda Pago digitally as they generate sales.
The potential size of this opportunity is estimated at $460 billion and . Apart from merchant education and acquisition, this model requires upfront investment in digital systems for ordering and tracking inventory, a distribution system for delivering products and the ability to geo-locate MSEs.
4. Platform-based lending: Unsecured and secured credit
Platform or marketplace models enabling the efficient matching of large numbers of lenders and borrowers may be one of the biggest disruptions in MSE financing. These platforms allow the holders of capital to lend to MSEs while avoiding the high costs of customer acquisition, assessment and servicing. Importantly, they can also unlock new sources of capital, since lenders can be large numbers of regular people (as with peer-to-peer lending), moderate numbers of individual investors or small numbers of institutional investors.
Afluenta, a popular online platform in Latin America, lets MSEs upload their company details online. It then cross-references this information against a broad range of data sources to generate a credit score. Afluenta publishes these scores and the amounts companies are requesting for the consideration of prospective lenders. Funds are disbursed and repaid digitally, which minimizes cost. No single lender is allowed to provide more than 5 percent of a given MSE loan, which spreads out the risk.
The volume of lending on is estimated to be around $43 billion. However, this type of lending is experiencing rapid growth in both developed and emerging markets, with estimated volume expected to grow to $207 billion by 2025.
These four models all demonstrate how technology and business model innovation is making it viable and profitable to finance MSEs in EMDEs. These lean digital models can make business possible where legacy bank approaches cannot. However, incumbent banks have cheap and ample capital, which fintechs sorely need to reach scale. Solving the $4.9 trillion MSE financing gap is likely to require unusual partnerships that combine the best of both worlds, deploying vast bank balance sheets through the digital disruptions that fintechs bring.