Why are loan defaults rising despite the availability of credit risk tools?

On June 10, 2025, the Monetary Policy Committee (MPC), through its chairman, Central Bank Governor Kamau Thugge, announced that the gross non-performing loans (NPLs) ratio in Kenya’s banking sector had climbed to 17.6 percent in April, up from 17.2 percent in February.

This upward trend in loan defaults, particularly in real estate, personal and household, trade, construction, and manufacturing sectors, has raised alarms across the financial ecosystem.

Despite this deterioration, the Central Bank of Kenya emphasised that the sector remains stable, with strong liquidity and capital buffers, and that banks continue to make adequate provisions.

In the same meeting, the MPC reduced the Central Bank Rate by 75 basis points to 10.00 percent to boost private sector lending amid easing inflation and a stable exchange rate environment.

As someone deeply involved in the credit information space, these developments trouble me. We have robust data systems and legal frameworks in place—yet defaults are rising.

This paradox led me to undertake a research project last year to explore a narrower but revealing aspect of the problem: whether significant differences in credit risk exist across Kenya’s eight former provinces, and how these differences influence Loss Given Default (LGD) among micro, small to medium enterprises.

The findings were compelling. Credit scores and PD values differed significantly by region and enterprise size.

This indicates that two businesses with similar profiles may have very different risk levels depending on where they operate.

In contrast, LGD showed variation across groups but was not statistically significant when controlling for business size, suggesting that loss severity is more closely linked to the scale of the business than to geography.

These findings have clear implications. Lenders must move beyond generic risk assessments and develop customer-centric, data-driven products tailored to regional and sectoral nuances.

Regulators and accountants can also use this insight to fine-tune Expected Credit Loss models under IFRS 9, ensuring more accurate provisioning.

For policymakers, the study raises an important question: could some well-meaning policies, such as blanket debt relief, unintentionally encourage strategic default?

If Kenya is to strengthen financial inclusion and build a more stable credit market, we must move beyond compliance checklists and ask harder questions about borrower behaviour, motivation, and context.

We must try and deal with why banks are increasingly favouring clients with strong track records, collateral, and credit scores, instead of leveraging data and analytics in their decision-making.

Similarly, at the service level, why should they continue to tax customers with bureaucratic lending procedures and extensive risk assessments, when there are convenient and more predictive approaches to risk management?

I hope that data and analytics, coupled with objective scientific research can be embraced more in policy formulation by both government and private sector towards a more resilient and responsible financial ecosystem.

This voice of data magazine partly showcases the regional and customer segments’ related nuances.

We will have deeper conversations with lenders operating in the different regional of Kenya on the regional nuances on customers credit risk profiles, in the upcoming regional credit community workshops.

This will culminate into a national credit market convention later in the year.

The writer is the CEO, Metropol Credit Reference Bureau