The financial sector is witnessing a significant shift in how banks and microfinance banks (MFBs) assess credit risk, driven by evolving regulatory frameworks and economic conditions.
This was the key focus of a breakfast meeting hosted by Metropol in Nairobi, where industry experts and financial leaders gathered to discuss the effectiveness of Expected Credit Loss (ECL) models in relation to interest rate determination.
For a while, banks have relied on judgmental adjustments to modelled ECL outputs to reflect current macroeconomic conditions, especially where historical data used in model development fails to capture evolving risks.
ECL is primarily implemented under the International Financial Reporting Standards (IFRS) 9, which provides a forward-looking approach to estimating potential future losses on loans or credit exposures.
According to PwC, this method takes into account borrower creditworthiness, economic conditions, and future forecasts, making it a more effective tool compared to traditional “incurred loss” models.
During his presentation at the event, Dr. Samuel Tiriongo, Director of Research and Policy at the Kenya Bankers Association (KBA), emphasized the ongoing transformation of the credit market through the adoption of risk-based pricing (RBP).
He said milestones have been achieved in the regulatory framework, which have enabled financial institutions to move away from collateral-based credit decisions toward a more risk-sensitive approach.
Dr. Tiriongo pinned out key elements of risk-based credit pricing, including:
- Credit Risk Assessment: Evaluating borrower creditworthiness through credit scores and prevailing economic conditions.
- Fair and Competitive Pricing: Aligning loan costs with risk levels to prevent overpricing or underpricing.
- Transparency & Disclosure: Providing customers with clear loan terms, pricing structures, and risk explanations.
- Regulatory Compliance: Ensuring alignment with banking sector regulations and ethical lending practices.
To further strengthen credit risk assessment, Dr. Tiriongo proposed key recommendations:
Also Read: What it Means as Kenyan Banks Adopt Risk Based Pricing Model
- Credit Risk Database: Establishing a centralized risk data system for improved ex-ante risk modeling of enterprises.
- Alternative Data Exploration: Incorporating mobile payments, utility bills, and behavioral data for better credit profiling.
- AI in Risk-Based Pricing: Leveraging artificial intelligence to enhance risk predictability in lending.
- Policy Dialogues: Addressing factors that contribute to high credit costs beyond lenders’ control.
The Role of IFRS 9 in Financial Asset Classification
Metropol Group Managing Director, Sam Omukoko, delved into the specifics of IFRS 9, which dictates how financial assets and liabilities should be classified and measured.
He explained that assets without significant deterioration are recognized with a 12-month expected credit loss, whereas assets that have undergone substantial deterioration are recognized with lifetime expected credit losses.
Omukoko said implementing IFRS 9 enables banks to assess credit risk more effectively while ensuring financial stability in the sector.
Access to Finance: The Cost of Credit in the Market
The discussion also shed light on access to finance, a critical issue for businesses and individuals seeking credit.
Elizabeth Nkukuu, Chief Executive of Financial Inclusion, said that while mobile lenders have bridged the financing gap where mainstream lending institutions failed, their rates remain costly for most borrowers, averaging 7% per month.
She worries that this, even though better placed for financial access, a lot needs to be done to create more affordable credit solutions to enhance financial inclusion.
The Metropol Banks and MFBs Breakfast Meeting provided a platform for industry leaders to engage in meaningful discussions on improving credit risk assessment through ECL models and risk-based pricing.
Metropol Credit Reference Bureau Chief Executive Gideon Kipyakwai signed off the forum saying that as financial institutions continue to refine their methodologies, the adoption of alternative data, artificial intelligence, and enhanced policy frameworks will be key to fostering a more efficient and transparent credit market.
