Habari za asubuhi,
That’s “good morning” in Swahili
Last week, a troubling news report on digital lending aired
on Kenyan national television and caused #DebtofShame to trend.
The report focused on two things:
- First, on aggressive debt recovery methods used by some lenders, such as harassing guarantors until defaulters paid up,
- And, second, lenders that issued predatory loans.
But the solutions to those 2 problems are either here or already on their way.
The issue of debt shaming can be solved by actually enforcing the existing data protection laws. As for predatory loans, a bill to regulate digital lending products has been hailed
as the standard for the rest of the continent.
The bill is pending approval in the national assembly. But if many Kenyans had their way, it would get approved yesterday.
In their mission statements, Africa’s fintechs often use the line “doing x for the unbanked or underserved”, where x is the financial service they provide. But digital lenders are one of the few fintechs that actually do what they say they do.
Before them, traditional lending was broken and low-income earners without access to financial services had no access to credit. But in the last decade or so, digital lenders have changed that.
These lenders don’t have the same regulatory restrictions as traditional banks, which has allowed them space to innovate.
And one of their biggest innovations is digital credit.
Unlike traditional banks, digital credit makes use of alternative data to determine creditworthiness. So, whereas a bank would rely on financial histories to make a credit assessment, digital lenders use digital data points to make a similar assessment.
This approach reduces the barrier to borrowing for people without financial histories.
The bad and the ugly
According to a new study
on the “promises and pitfalls” of digital lending in Kenya, digital credit is solving a long-standing problem but creating new long-term ones.
The study found that while digital credit has democratized access when compared to traditional credit, it also has considerably higher default and blacklisting rates.
And it is this high rate of blacklisting, not even the debt shaming, that could cause real issues for defaulters:
“Digital borrowing could even lead to insurmountable hurdles to full credit access rather than constitute a gateway to it.”
That’s why it’s important to see this as a funnel problem, with digital lenders at the top and traditional credit at the bottom.
Because someone that defaults on a KSh 20,000 (< $200) loan and gets blacklisted can be prevented from getting a mortgage in the future, even if:
- They were a poor fit for the loan, to begin with
- They’ve become a good fit for a mortgage over time
Digital lenders must, therefore, improve the quality of their credit assessments to make lending not only accessible but also safe in the long run. A by-product of this will be a reduction in reports of debt shaming.
Ivan Mbowa, MD of Tala, a digital lender, said
“The art of loan collection actually begins with who you choose to lend to in the first place.”
One of Tala’s solutions is to run over 250
data points through its machine learning algorithm. I’m curious to see if this has translated to lower default and blacklisting rates than its competitors.
For now, the proposed bill continues to allow
digital lenders, like Tala, to self-regulate and optimize their algorithms rather than forcing them to use traditional data for credit assessments. Will they use the breathing space wisely?