Kenya lenders turn to AI, digital assets to reshape credit market

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Kenya’s financial sector is increasingly using Artificial Intelligence (AI), alternative data and cloud computing to change how credit is assessed and provided. Industry players say data could become a new form of collateral, helping millions of people who currently struggle to access loans.

The shift could especially benefit small and medium-sized enterprises (SMEs), which often find it difficult to get formal credit because they lack traditional collateral such as property and other physical assets.

Speaking during a forum that brought together chief executives from commercial banks, microfinance institutions, digital lenders and Saccos, Spinmobile chief executive officer Victor Kiplagat said financial institutions should use technology more effectively to make better lending decisions, reduce risks and increase access to credit.

He said SMEs such as small traders, boda boda operators and barbers may not own traditional collateral, but their daily transactions generate valuable data. This information can help lenders understand their financial behavior and assess whether they can repay loans.

“Data is the new collateral. Alternative data, AI and big data could be used to develop a clearer picture of a borrower’s financial capacity and character,” Kiplagat said,

According to Kiplagat, technology is already changing how customers interact with financial institutions, allowing them to access loans and other services without necessarily visiting physical branches.

The adoption of digital platforms, he said, has improved the speed and consistency of lending while helping institutions reduce operational costs. These savings could ultimately translate into more affordable loans for their customers.

The discussions also highlighted the growing role of AI in credit scoring and risk management. However, industry leaders cautioned that the technology is only as effective as the quality and quantity of data used to train it.

Kiplagat noted that insufficient data and potential bias in AI models remain some of the key risks financial institutions must address. He said lenders need reliable and diverse datasets to ensure that AI-driven decisions are accurate and fair.

Metropol CRB CEO Gideon Kipyakwai said the use of data-driven credit scoring is already demonstrating its potential to improve lending outcomes. He said digital lending portfolios have recorded significantly lower levels of non-performing loans compared with some traditional lending channels, attributing the difference partly to the use of automated scoring and analysis of multiple data points.

He said traditional credit assessment has often relied heavily on assumptions and limited information, while digital systems analyses a wider range of customer data to determine borrowing capacity and repayment behavior.

Kipyakwai said the real-time analysis of such information enables lenders to make faster and more informed decisions while reducing the risk of lending to customers who may struggle to repay.

The entry of alternative data platforms such as SpinMobile, he added, is providing lenders with additional information that can complement traditional credit bureau data.

The growing use of AI and alternative data comes at a time when financial institutions are under pressure to expand credit access while keeping lending risks under control.

For SMEs, the development could be significant as businesses without substantial physical assets may increasingly be assessed based on their transaction history, cash flows, customer behavior and other digital footprints.

The industry players said the broader adoption of these technologies could also strengthen the competitiveness of Kenyan financial institutions as they seek to expand into other African markets.

They noted that Kenya already has the technology needed to transform financial services and the next step should be to ensure that businesses adapt these tools effectively, and use data responsibly.