A New Approach: Saving-led Digital Finance

[mouakter13]

Part the answer may come from a new generation of “savings-led” digital finance apps that use the savings capacity of clients as a proxy for their ability to repay loans. Unlike “instant credit” apps, which can lead to over-indebtedness, these savings-led solutions start by helping clients build good saving habits. From the lender’s perspective, the primary benefit of this approach is that the clients themselves demonstrate how much surplus they have by how much they save and when. Instead of digital lenders estimating what clients can afford, each client’s track record of savings generates this information straightaway. A second clear italy whatsapp number data benefit of the savings-led approach is that it encourages borrowers to build healthy saving habits, which inevitably builds financial resilience.

Here at DreamStart Labs we offer a group-based savings app called DreamSave that makes it easy for clients in emerging markets to build savings, access loans and achieve financial goals. DreamSave enables members of existing informal savings groups to manage and track their financial activities digitally, and we have now started to link these groups to formal financial service providers. With the permission of clients, we collect data generated by these member-to-member transactions, including internal lending, and by interactions between the group and external lenders. By statistically analysing this information, we generate data-driven credit scores that incorporate predictive relationships between a wide variety of factors, including the ratio of loans-to-internal savings and a member’s repayment performance.

As one might expect, our analysis shows that the higher the loan-to-savings ratio, the higher the probability of loan default. Our threshold analysis for groups in rural Africa, which looks at this variable in isolation, estimates that a loan-to-savings ratio of 1.35:1 best separates borrowers who repay loans on time from those who do not. In other words, group members who borrow $1.35 or less from the group per every $1 they’ve contributed in savings are less likely to pay late or default on their loans.

Another key finding is that the amount saved by a client is typically more important as a predictive indicator than the frequency of savings. Savings success, measured as the amount saved divided by the group maximum, is highly significant in predicting potential default, whereas savings frequency is far less relevant statistically. This finding is evident across different country data sets.

For group-based savings apps, our research suggests that other savings-led transactions are also statistically significant in predicting member loan repayment performance. These transactions include a member’s contributions to the group’s social fund (a separate savings pool for emergency use by members which does not have to be repaid), and fines levied by her group (for transgressions ranging from non-attendance at meetings, to non-contribution to savings and non-payment of loans). Given that these transaction types are entirely outside the scope of credit-led solutions, this is further evidence of the power of savings-led digital finance to measure a borrower’s capacity to repay.