It's Time to Slow Digital Credit's Growth in East Africa

First-of-its-kind data on millions of loans in East Africa suggest it is time for funders to rethink how they support the development of digital credit markets. The data show that there needs to be a greater emphasis on consumer protection.

In recent years, many in the financial inclusion community have supported digital credit because they see its potential to help unbanked or underbanked customers meet their short-term household or business liquidity needs. Others have cautioned that digital credit may be just a new iteration of consumer credit that could lead to risky credit booms. For years the data didn’t exist to give us a clear picture of market dynamics and risks. But CGAP has now gathered and analyzed phone survey data from over 1,100 digital borrowers from Kenya and 1,000 borrowers from Tanzania. We have also reviewed transactional and demographic data associated with over 20 million digital loans (with an average loan size below $15) disbursed over a 23-month period in Tanzania.

Both the demand- and supply-side data show that transparency and responsible lending issues are contributing to high late-payment and default rates in digital credit . The data suggest a market slowdown and a greater focus on consumer protection would be prudent to avoid a credit bubble and to ensure digital credit markets develop in a way that improves the lives of low-income consumers.

High delinquency and default rates, especially among the poor

Roughly 50 percent of digital borrowers in Kenya and 56 percent in Tanzania report that they have repaid a loan late.  About 12 percent and 31 percent, respectively, say they have defaulted. Additionally, supply-side data of digital credit transactions from Tanzania show that 17 percent of the loans granted in the sample period were in default, and that at the end of the sample period, 85 percent of active loans had not been paid within 90 days. These would be high percentages in any market, but they are more concerning in a market that targets unserved and underserved customers. Indeed, the transactional data show that Tanzania’s poorest and most rural regions have the highest late repayment and default rates.

Who’s at greatest risk of repaying late or defaulting? The survey data from Kenya and Tanzania and provider data from Tanzania show that men and women repay at similar rates, but most people struggling to repay are men simply because most borrowers are men. The transaction data show that borrowers under the age of 25 have higher-than-average default rates even though they take smaller loans.

Interestingly, the transactional data from Tanzania also show that early morning borrowers are the most likely to repay on time. These may be informal traders who stock up in the morning and turn over inventory quickly at high margin, as observed in Kenya.

Borrowers who take out loans after business hours, especially at 1 or 2 a.m., are the most likely to default — likely indicating late-night consumption purposes. These data reveal a worrisome side of digital credit that, at best, may help borrowers to smooth consumption but at a high cost and, at worst, may tempt borrowers with easy-to-access credit that they struggle to repay.

Further, the transaction data show that first-time borrowers are much more likely to default, which may reflect lax credit screening procedures. This can have potentially long-lasting negative repercussions when these borrowers are reported to the credit bureau.

Most borrowers are using digital credit for consumption

Many in the financial inclusion community have looked to digital credit as a means of helping small, often informal, enterprises manage daily cash-flow needs or as a way for households to obtain emergency liquidity for things like medical emergencies. However, our phone surveys in Kenya and Tanzania show that digital loans are most commonly used to cover consumption , including ordinary household needs (about 36 percent in both countries), airtime (15 percent in Kenya, 37 percent in Tanzania) and personal or household goods (10 percent in Kenya, 22 percent in Tanzania). These are discretionary consumption activities, not the business or emergency needs many had hoped digital credit would be used for.

Only about 33 percent of borrowers report using digital credit for business purposes, and less than 10 percent use it for emergencies (though because money is fungible, loans taken for one purpose, such as consumption, could have additional effects, such as freeing up money for a business expense). Wage employees are among the most likely to use digital credit to meet day-to-day household needs, which could indicate a payday loan type of function in which digital credit provides funds while borrowers are waiting for their next paycheck. Given the evidence from other markets of the high consumer risks of payday loans, this should give pause to donors that are funding digital credit.

Further, the phone surveys show that 20 percent of digital borrowers in Kenya and 9 percent in Tanzania report that they have reduced food purchases to repay a loan . Any benefits to consumption smoothing could be counteracted when the borrower reduces consumption to repay.

The survey data also show that 16 percent of digital borrowers in Kenya and 4 percent in Tanzania had to borrow more money to pay off an existing loan. Similarly, the transactional data in Tanzania show high rates of debt cycling, in which persistently late payers go back to a lender for high-cost, short-term loans with high penalty fees that they continue to have difficulty repaying.

Confusing loan terms and conditions are associated with difficulties repaying

Lack of transparency in loan terms and conditions appears to be one factor contributing to these borrowing patterns and high rates of late repayment and default. A significant percentage of digital borrowers in Kenya (19 percent) and Tanzania (27 percent) say they did not fully understand the costs and fees associated with their loans, incurred unexpected fees or had a lender unexpectedly withdraw money from their accounts. Lack of transparency makes it harder for customers to make good borrowing decisions, which in turn affects their ability to repay debts. In the survey, poor transparency was correlated with higher delinquency and default rates (though correlation does not indicate causation).

What does this mean for funders?

Even though digital loans are low value, they may represent a significant share of a poor customer’s income, and repayment struggles may harm consumers. Overall, the use of high-cost, short-term credit primarily for consumption coupled with high rates of late repayments and defaults suggest that funders should take a more cautious approach to the development of digital credit markets — and perhaps stop providing grants or concessional funding terms for this segment of products.

More specifically, the free and subsidized funding currently used to expand digital credit products to unserved and underserved customer segments would be better used helping regulators monitor their markets, identify opportunities and risk and promote responsible market development. One way to do this would be to fund and assist regulators with gathering and analyzing data on digital credit at the customer, provider and market levels. More comprehensive and granular data would help regulators — as well as providers and funders — better assess the opportunities and consumer risks in digital credit.

Improved data gathering need not be cost prohibitive. CGAP’s research in Tanzania shows that affordable phone surveys can provide useful data that are remarkably consistent with provider data. Digital lenders’ transactional and demographic data should be collectable since lenders regularly assess them when calculating and reporting on key performance indicators. However, additional investment may be needed to ensure the consistency, integrity and reliability of the data.

At a market level, it will be important to strengthen credit reporting systems and require information reporting from all sources of credit, including digital lenders, to improve the accuracy of credit assessments. These efforts should consider whether prevailing digital credit screening models are strong enough and whether rules are needed to ensure first-time borrowers are not unfairly listed. This could include rules on reckless lending or suitability requirements for digital lenders.

Donors and investors can play an important role in the next phase of digital credit’s market development. This phase should see greater emphasis on assisting regulators to regularly gather and analyze data and act to address key warning signs that are already emerging around transparency, suitability and responsible lending practices.