Savings-Led Digital Finance: The Dawn of a New Era?
Posted: Thu Feb 13, 2025 3:31 am
The booming digital lending market surpassed $300 billion in 2020, with significant growth coming from new smartphone users in emerging markets. Every day, millions of clients from Kenya to the Philippines use innovative fintech apps like Tala and Branch to access “instant loans” from their mobile phones. These digital lending apps analyse data extracted from the phones of prospective clients to make data-driven lending decisions without the need for loan officers or brick-and-mortar banks. For aspiring entrepreneurs in the developing world, these digital solutions can provide essential working capital that might otherwise be impossible to obtain from traditional lenders.
Amidst all the success stories, however, are a growing number italy whatsapp number data of concerns about the risky practices some digital lenders use to serve vulnerable clients in developing markets. A 2021 Center for Financial Inclusion blog by Maria Gabriela Coloma Ponce de Leon underscored this challenge, noting that “the primary problem is that digital lenders do not measure the capacity of someone to repay, but their willingness to repay, and these are drastically different concepts.”
In this single sentence, the author focuses our attention on the critical question in digital finance today: How can digital lenders accurately measure the capacity of a client to repay in a low-cost, rapid and ethical manner?
One approach might be to establish the various income-generating roles a client undertakes and assign an earnings estimate to each role using some kind of earnings matrix. Government statistics could theoretically provide some of this information, but many countries do not publish useful earnings data, especially for the informal economy. In developed countries, data from credit bureaus is used to attach a confidence level to a borrower’s stated level of income, but these services are typically not available for most customers in frontier markets.
If estimates based on published data sources aren’t the answer, digital lenders could try to assess income levels directly. Unfortunately, the earnings of different clients vary greatly even within the same roles, either due to time spent, local variations or different levels of entrepreneurship. At best, this might help lenders weed out intentionally misleading loan applications, but collecting this information would create a highly discontinuous and “friction-full” client experience, which also has costs.
Even if digital lenders could generate an accurate earnings estimate, this approach requires another essential piece to the puzzle – understanding a client’s spending patterns. While some clues can be gained from analysing smartphone data, spending patterns are even more variable and harder to predict than income estimates. Add the possibility of compound inaccuracies in this approach, and the question needs to be asked: Is there a better way to assess ability to repay that is economical, universal and reliable?
Amidst all the success stories, however, are a growing number italy whatsapp number data of concerns about the risky practices some digital lenders use to serve vulnerable clients in developing markets. A 2021 Center for Financial Inclusion blog by Maria Gabriela Coloma Ponce de Leon underscored this challenge, noting that “the primary problem is that digital lenders do not measure the capacity of someone to repay, but their willingness to repay, and these are drastically different concepts.”
In this single sentence, the author focuses our attention on the critical question in digital finance today: How can digital lenders accurately measure the capacity of a client to repay in a low-cost, rapid and ethical manner?
One approach might be to establish the various income-generating roles a client undertakes and assign an earnings estimate to each role using some kind of earnings matrix. Government statistics could theoretically provide some of this information, but many countries do not publish useful earnings data, especially for the informal economy. In developed countries, data from credit bureaus is used to attach a confidence level to a borrower’s stated level of income, but these services are typically not available for most customers in frontier markets.
If estimates based on published data sources aren’t the answer, digital lenders could try to assess income levels directly. Unfortunately, the earnings of different clients vary greatly even within the same roles, either due to time spent, local variations or different levels of entrepreneurship. At best, this might help lenders weed out intentionally misleading loan applications, but collecting this information would create a highly discontinuous and “friction-full” client experience, which also has costs.
Even if digital lenders could generate an accurate earnings estimate, this approach requires another essential piece to the puzzle – understanding a client’s spending patterns. While some clues can be gained from analysing smartphone data, spending patterns are even more variable and harder to predict than income estimates. Add the possibility of compound inaccuracies in this approach, and the question needs to be asked: Is there a better way to assess ability to repay that is economical, universal and reliable?