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The Busara Blog

The cost and accessibility of digital credit

Factors influencing the cost of digital credit in Kenya, Nigeria and India.

Illustration by Lynette Gow

When that short-term emergency loan to cover your dentist’s fees is quite literally a click away, the last thing on your mind is the steep Annual Percentage Rate (APR), or interest rate, charged on the loan. When you take out a traditional loan, say through a bank, a longer repayment period means smaller monthly payments. In essence, a bank loan appears cheaper. On the contrary, digital credit repayment periods are short, and often require the full amount to be paid in one installment. These factors, in addition to the applied APR, make digital credit quite expensive. Traditional lenders are also able to extend large amounts of credit to fewer borrowers after a rigorous credit check, whereas digital lenders can only offer small value loans to a larger number of borrowers with limited credit checks. The difference in loan amounts and repayment schedules between traditional lenders and digital credit providers has arguably the biggest influence on the cost of digital loans.

The price of credit is also influenced by the type of credit provider, and their source of capital. For example, providers who rely on debt investment are often driven to price their loans higher so as to get quicker returns to repay their investors. Providers like banks, who don’t rely on external investment, can afford higher risk and come in at a lower price point, similar to purpose-driven lenders such as SACCOs, who source funding from more impact-oriented investors with longer-term outlooks on profit margins.

Although high interest rates are often attributed to high default rates on repayment, our study found that, in fact, fixed interest rates based on market risk, and personalized interest rates based on default probability are the more likely culprits.

Consumers not only bear the burden to repay time-bound debt through high APRs, but they are also more likely to experience harsh debt collection practices if they default on their loans. Further, investor pressure may force lenders to keep to the high-end and already profitable market, rather than exploring new models that are appropriate for currently higher-risk, lower-income borrowers.

Despite the expense of repaying digital loans, it is surprising that borrowers do not consider this particularly concerning when making borrowing decisions. Attributes such as convenience, loan limits, ease of use, among other factors are prioritized. It may therefore be limiting to place too much weight on cost as a factor when evaluating the impact of digital credit on its users. Additionally, consumers’ low switching behavior creates demand for a particular product, causing little incentive for lenders to reduce their prices. A notable reason for this is that people are not always aware of alternatives, thus remain unable to compare products based on costs or other elements they care about. Finally, consumers are only able to leverage their credit histories with digital credit options, than with traditional loans. Fewer credit checks are performed by the former, compared to the latter.

So, what would be considered a fair middle ground? For borrowers, the overwhelming preference is repayment in installments over lump sums. For one, low-income borrowers often experience irregular and/ or insufficient incomes, thus severely crippling their ability to both service loans and run their households. Even where multiple sources of income exist, proceeds are still insufficient, and largely cover basic expenses. Vulnerable groups, specifically women and youth, have little to no autonomy in financial decision-making, further complicating the loan payment experience.

Automatic repayment is likely to be increasingly favored by digital lenders. Borrowers with digital accounts offer better data trails to aid loan decisions. Some lenders are choosing to go one step further and automatically deduct repayments from digital accounts to further guarantee repayment, although there are some concerns with this approach. With other forms of credit the borrower can default if they have to, which is important for low-income borrowers who face unplanned shocks and emergencies. Automated deductions essentially take away consent at the point of repayment, which can be problematic from a consumer protection perspective. Further, the model poses the risk that some borrowers may choose to transact outside digital ecosystems in order to avoid automatic loan deductions.

Data is a valuable resource for decision-making

Considering digital credit does not require collateral to guarantee loans, heavy reliance on data to support decisions on credit-worthiness is inevitable. Changes to the availability and use of both traditional and alternative data resources are integral to creating positive experiences for end-users, especially regarding credit limits and flexible repayments.

If used properly, data is a game-changer for consumers, credit providers and regulators in transforming digital credit into the impactful solution it was created to be.

What can regulators do to create a better digital credit environment? Find out by watching this video on our YouTube channel that breaks down our recommendations for regulators.

This blog is the second in a four part series on digital credit. Part 1 can be read here. Follow us on social media to get notified as soon as the next blog comes out. If you liked this, head over to our YouTube channel to watch the corresponding Part 2 video on our Digital Credit study.

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Busara is a research and advisory firm dedicated to advancing Behavioral Science in the Global South