How AI is Helping Mobile Loan Apps Avoid Defaulters

How AI is Helping Mobile Loan Apps Avoid Defaulters

Digital credit is rapidly gaining traction in Kenya, with 51 service providers currently approved by the Central Bank of Kenya (CBK). These loan applications, which operate without collateral requirements, face the significant risk of accumulating bad loans, potentially jeopardizing their operations. This risk, paradoxically, contributes to the growing popularity of these credit facilities among borrowers.

The Challenges of Loan Default

Recently, clients have raised concerns regarding the unconventional methods employed by some digital credit providers to secure loan repayments. Practices such as contacting close associates and family members of defaulters have been reported, often leading to embarrassing situations for the borrowers.

The Role of AI in Risk Management

Tala's General Manager, Annastella Mumbi, emphasized the pivotal role of technology in mitigating such risks during her appearance on Spice FM on May 23. She highlighted how artificial intelligence (AI) and machine learning are used to effectively profile customers by analyzing their data before approving or rejecting loan applications.

“When giving credit, you check the client for three things: fraud, ability to repay, and willingness to repay,” Mumbi stated.

The effectiveness of Tala's approach is evident, with an impressive 95% repayment rate on issued loans, contrasting sharply with the 30% default rate reported by the government-administered Hustler Fund last year.

Utilizing Credit Reference Bureau (CRB) Reports

Mumbi further noted the importance of consulting the Credit Reference Bureau (CRB) for insights into customers’ lending patterns. This practice enables digital lenders to make informed decisions, helping to further reduce default rates.

Democratizing Access to Credit

Digital lending apps have played a significant role in democratizing access to credit, allowing many Kenyans to secure quick loans to support their Micro, Small, or Medium-sized Enterprises (MSMEs). With increasing demand for loans, many lenders have set borrowing limits, usually within a repayment window of 60 days. The allure of lower interest rates compared to traditional banks continues to attract a growing customer base.

“We have about a 2 billion credit deficit. Six out of ten borrowers in Kenya borrow from more than one lender. Even at Tala, we are not fully satisfying the needs of one customer,” Mumbi noted.

Default Cases: Economic Pressures at Play

Mumbi attributed a notable number of default cases to inflationary pressures, business layoffs, and unexpected financial shocks. These factors complicate borrowers' abilities to repay their loans, highlighting the fragile economic landscape that many Kenyans navigate.

Consumer Protection Concerns

In response to growing public complaints regarding punitive interest rates, the National Assembly is currently investigating several lenders. There is a rising outcry about the potential violations of consumer protection laws, signaling a need for greater oversight in the digital lending space.

Conclusion

As the digital credit landscape continues to evolve in Kenya, the integration of AI and machine learning in loan management offers a promising solution to reduce defaults. While these technologies enhance risk assessment and improve repayment rates, ongoing attention to consumer protection and economic pressures remains critical to fostering a sustainable lending environment.

For more insights on digital lending in Kenya and its implications, stay tuned for updates on this developing story.