Why Tala backs regulation of digital lending industry

Ivan Mbowa, the regional general manager for Tala. FILE PHOTO | NMG

What you need to know:

  • The Central Bank of Kenya (CBK) in April locked credit-only mobile lenders out of credit reference bureaus (CRBs) database, denying them easy access to borrowing history of clients and barring them from blacklisting defaulters.
  • The digital lenders have been accused of misusing the CRB mechanism by erroneously listing borrowers as defaulters and breaching their privacy.

The Central Bank of Kenya (CBK) in April locked credit-only mobile lenders out of credit reference bureaus (CRBs) database, denying them easy access to borrowing history of clients and barring them from blacklisting defaulters.

The digital lenders have been accused of misusing the CRB mechanism by erroneously listing borrowers as defaulters and breaching their privacy.

Ivan Mbowa, the regional general manager for Tala, a leading credit-only mobile app since 2014, spoke to the Business Daily about their lending standards and developments since Covid-19 shocks struck in March.

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HOW HAVE THE COVID-19 SHOCKS AFFECTED YOUR BUSINESS?

It has been a very challenging time for the industry as a whole. We have seen an increase in average default rates during the lockdown as our customers have been affected by the slowdown in business and also layoffs. However, we are now beginning to see signs of recovery from our resilient customers who are adapting to this Covid economy. As we see signs of recovery we also see our customers adapting to the pandemic. We will continue our lending to more and more returning customers.

WHAT HAVE YOU DONE TO CUSHION THE BUSINESS FROM THE ECONOMIC FALLOUT ARISING FROM THE PANDEMIC?

Because we are very keen on responsible borrowing we have taken a very cautious approach to only provide credit to those customers who we think are able to repay in this Covid-19 environment. We are wary about burdening customers with debt in this period of economic uncertainty.

HOW DO YOU TELL A BORROWER IS RESPONSIBLE?

We actually look at thousands of data points. All the data we track is important for our decision making. What the machine-learning technology does is to determine the weight of the individual data points. So once the user gives us those permissions, the type of data we use depends on how a user handles his own application.

SPECFICALLY, WHAT INFORMS LENDING DECISIONS?

Things like what type of phone, what year is the operating system and some of the apps on the phone. For example, someone who’s using a lot of apps and data can probably afford associated data charges by telcos, and this gives us a sense of their financial capacity as opposed to someone who doesn’t have apps. Also if a user reads our terms and conditions, it may give us a better understanding of their sense of responsibility or decision making around credit and use of credit. We also look at how customers have repaid their prior loans, and all that gives us a (sense) to predict if we can responsibly lend to this customer.

HOW HAS THE LOSS OF ACCESS TO THE CRB DATABASE AFFECTED YOUR LENDING DECISIONS?

We have not relied on the CRB database to make determinations on who to lend to, but that data is very useful as a deterrent for those who may want to default. We are providing unsecured lending, and so the risk of being listed negatively used to be a good deterrence for default. It’s not so much on how you encourage someone to repay you.

DIGITAL LENDERS HAVE GENERALLY BEEN ACCUSED OF ABUSING THE CRB SYSTEM BY LISTING DEFAULTS AS LITTLE AS SH100 AND SOMETIMES ERRONEOUSLY BLACKLISTING OTHERS.

What we require is a code of conduct because we recognise that we are different digital lenders, and we don’t behave the same way. So we need regulation that enforces, as a bare minimum, terms of purchase.

The State has prohibited parties from contributing information to the credit reference bureaus, and this can lead to an issue where you have hundreds of lenders providing credit, but they don’t know how indebted the customer is. As a society we are worried about Kenyans taking on too much debt.

We need legislation that could enable digital lenders rejoin the CRB and contribute not only negative but also positive information. Many of us forget that credit reference bureaus should identify who is a good borrower, and not just those who defaulted.

THERE ARE SOME LENDERS WHO GO TO THE EXTENT OF CONTACTING FAMILY, FRIENDS AND EVEN EMPLOYERS OF DEFAULTERS.

Yes, we have had cases where some people are contacting phone box and doing all kinds of stuff. There have been unethical behaviours. We think that a regulatory framework should drive and enforce rules and help us ensure fairness, transparency and honesty in respect to treating the customers even during debt collection.

ARE YOU, AS A FLEDGLING INDUSTRY, READY FOR A ROBUST REGULATORY SYSTEM?

Regulation is not just on pricing. We think that in order to maintain sort of a vibrant digital lending industry that advances economic opportunities over the long term, we will need regulation that not only increases consumer protection on things like pricing, but also drive and improve our services.

So as Tala, along with Digital Lenders Association of Kenya, we actually support the development of such regulatory framework to enhance a competitive landscape and increase customer’s choice.

HOW DO YOU JUSTIFY THE COST OF MOBILE LOANS WITH MONTHLY RATES GENERALLY IN DOUBLE-DIGIT FIGURES AND TOPPING 100 PERCENT WHEN ANNUALISED?

We believe that our pricing is fair. It’s an actual representation of the risk involved in providing unsecured or no-collateral loans. It may seem relatively high, but it’s not an interest rate. Whenever people start to say that 15 percent is like X percent per annum, they are making unfair comparison because we are not like banks who are providing this for 12 months.

DON’T YOU THINK THERE IS NEED FOR REGULATION OF MOBILE LOAN CHARGES?

There have been many attempts to try and resolve pricing. We are in favour of maybe a facilitative risk-based credit model which are powered by machine learning technologies.

It’s unfair to charge every customer the same rate because different customers present different risks. Different customers also have different intended use of the funds. Somebody borrowing for consumption should have a different rate from one borrowing to put in business. Instead of pushing everyone to charge the same rate, it will be better for us to try and put in place risk-based pricing.

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