Many commentators have raised concerns about the interest rates charged for digital credit. And, given that the entire process is automated and dependent on computer algorithms rather than expensive human intervention and analysis, this seems reasonable.
On the face of it, it is strange that the interest rates charged for digital credit should be closer to those common in the informal sector than those charged for other formal sector loans. So what is going on?
There are three key drivers of the high-interest rates: 1. The small size of loans; 2. The cost of data analytics; and 3. The risk premium priced in.
Small Loans: We all know that, broadly-speaking, it costs the same amount of money to make a $10 or a $10,000 loan. Digital credit loans, absent the personal relationship, start by lending small amounts (typically $10-20) to gauge repayment behaviours and base future lending decisions (largely) on the basis of these. The interest on these minimal amounts is often inadequate to cover even the variable costs associated with making a digital loan (SMSs or data charges etc.).
Data Analytics: Digital credit providers not only need to invest significant amounts upfront to build their platforms and algorithms, but also on an on-going basis to keep refining them as they learn through the behaviour of their customers. One large provider tells us that they are spending $200-300,000 per month on analysts to maintain and develop their system.
Risk Premium: MicroSave’s recent analysis of a credit reference bureau’s data has highlighted the extraordinarily high default rates amongst digital credit borrowers in Kenya, where the best data is available. We can safely assume that this is a pervasive problem. Inevitably, providers of digital credit have to price these losses into the interest rates charged for loans. This means that all borrowers (whether they repay on a timely basis or not) have to pay the risk premium for those that default.
While providers of digital credit will always struggle with the mathematics and economics of small loans and the cost of data analytics, there is clear opportunity to reduce the level of defaults and thus the risk premium that has to be charged … and perhaps that smart algorithms alone will not be enough to do so.
CGAP’s Greg Chen highlights six early errors made by digital credit pilots and deployments. Several of these contribute to the high levels of default.
Offering credit without a strong remote identification system. When you can’t verify customer identity, offering remote services is difficult, especially at scale.
Poor targeting, where credit offerings attract a high-risk applicant pool.
Cumbersome loan application processes so that only those higher-risk borrowers, who are unable to secure credit from other sources, apply.
Poor product design, which does not adequately recognize and reward those that do repay regularly and on time.
An excessive focus on credit scoring but the absence of a sound collections strategy.
Credit scoring models were too conservative and did not allow credit to be extended to more than a small fraction of applicants.
Addressing 1. – 5. could allow providers of digital credit to improve targeting, increase loyalty and reduce both risk and default … thus increasing the profitability of providers of digital credit.
Mobile network operators (MNOs) can also reduce targeting risk by completing initial credit screening through lending airtime credit. Airtime has marginal costs for an MNO and thus represents a much lower risk than e-value credit. Thus this approach could allow MNOs to test borrower’s credit behavior at much lower cost before opening a window to borrowing e-value.
Loan application processes: Many SMS-based and USSD-based digital credit systems make it almost too easy to access credit, thus potentially encouraging frivolous applications for credit. This may need management through behavioral nudges – for example, to encourage the potential borrower to view the terms and conditions or to reaffirm the need for the loan after a nominal “cooling off” period. In contrast, most app-based systems require the user to go through many screens (and, in some cases, what are seen as invasive requests for data and photographs) before they are given their loan. These systems need a thorough review to ensure that each step in the process is optimized, really adds value and does not put off high potential borrowers.
Poor product design: Currently, few of the digital credit products available reward those that consistently repay on a timely basis – except by offering larger loans. As borrowers demonstrate their credit-worthiness it would make sense to reduce the risk premium (and thus the interest rate) that they have to pay for each successive loan. This approach might be further reinforced and optimally communicated by creating a tiered status system (similar to those for airline miles) so that borrowers can aspire to move up the tiers and thus qualify for lower interest rates, larger loans, variable repayment periods and other benefits. Additional product innovation might include 1. Loans with a tenure of a day for market traders who are currently having to use loans repayable over weeks or a month to finance their business cycles, which run from early morning to afternoon; 2. Goal-based savings/loan products with an appropriate financial planning tool embedded in the app or USSD interface; 3. Longer-term loans for those with an excellent credit record who want to borrow for their business – once again these need to reflect their business cycles.
Absence of a sound collections strategy: At present most digital credit providers use SMS to encourage repayment, but otherwise have little interaction with their borrowers. Only a few are using call centers to talk to borrowers struggling to repay. The important human touch is missing, and thus digital credit loans are last on the list to repay amongst households with multiple loans outstanding. For larger loans, it may also be valuable to involve agents in both loan origination and repayment/delinquency management.
Readers will note that none of the above refers to using “big data” – in a way that has been so successfully done in the developed world (for example by Lending Club in the US). This is because the vast majority of low-income people in the developing world do not leave adequately deep “digital footprints” to reliably inform credit decisions. This will change over time, but for now, the most effective (and commonly used) indicators of creditworthiness lie in credit history and behavior, and (to a lesser extent) top-up and call/SMS behavior. It may be that for larger loans app-based providers of digital credit may also want to use psychometric indicators to assess willingness to pay. However, this would be dependent on reducing the typical screening questionnaire from 200-300 down to 40-50 questions without losing predictive capability – quite a challenge.
There is a clear need to reduce the risk premium for borrowers of digital credit. While this may be difficult (but by no means impossible) to do for the first couple of loan cycles, it should be eminently feasible for later loan cycles once the borrower has established credit history and wants to borrow larger amounts. Doing so should incentivize timely repayment and increase borrower loyalty … and thus profitability of the providers of digital credit