The AI Governance Kenya Needs For Fair Credit Access
Kenya has never lacked vision. What we need now is regulatory coherence, institutional maturity, and a shared commitment to fairness.
The recent Survey on Artificial Intelligence in the Banking Sector, conducted by the Central Bank of Kenya (CBK), provides an honest and sobering account of the current state of the country’s financial sector amidst the AI revolution. This survey comes at a pivotal moment when Kenya is taking bold steps to position itself for the global digital economy. The launch of the National AI Strategy earlier this year underscores this commitment, as the country aims to harness AI for economic transformation and citizen-centered innovation.
However, as is often the case with rapid technological evolution, policy ambition now confronts institutional readiness. The survey reveals that while 50% of lenders have yet to adopt AI technology, 65% of those who have are using it for credit risk scoring. This is not surprising, given that credit decisions are central to building trust in the financial system, determining who gets access to opportunities and on what terms.
It is imperative that we do not postpone AI governance for a future moment. There needs to be an urgent, deliberate, and collective effort in tackling emerging governance issues.
The Promise and Perils of AI in Credit Assessment
AI is indeed a powerful tool that offers financial institutions a new means of reimagining risk assessment. With AI, financial institutions can transition from rigid credit scoring systems that rely heavily on collaterals, securities, and pay slips to a more dynamic, data-driven approach that reflects real-world behaviors. This shift is supported by the rise of digital footprints, transaction histories, mobile money patterns, and behavioral analytics. Consequently, access to credit can be expanded to previously unbanked and underserved populations, particularly those working in the informal economy, thereby enhancing financial inclusion.
However, this promise carries a sobering caveat: without a well-curated and ethically designed governing framework, AI can become a bad master, entrench exclusion, perpetuate algorithmic bias, and erode transparency. The CBK survey highlights that among the institutions leveraging AI for credit assessment, few have embedded mechanisms for bias detection, explainability, or customer redress. This stark revelation goes beyond mere compliance; it poses a systemic risk.
The Need for Transparency and Accountability
AI systems, particularly in the realm of credit, should not operate in black boxes. When an institution denies a borrower a loan based on the recommendations of an algorithm, it should be able to explain that decision to the borrower. A system that cannot justify its decisions is not only unethical but also legally vulnerable and reputationally dangerous. This obligation is even more critical in a society grappling with inequality and a digital divide.
Thus, the National AI Strategy and CBK must converge. The values of inclusivity, ethics, and human oversight espoused in the AI Strategy need to be embedded in the CBK guidelines on AI adoption within the sector. To ensure meaningful impact, these principles should not only be integrated into sector-specific regulatory frameworks but also anchored on clear supervisory expectations for AI governance in financial services, addressing fairness, data integrity, algorithmic accountability, and proportional human oversight.
Shifting Compliance Functions
As regulators explore avenues to catalyze a broader reform agenda, including regulatory sandboxes for predictive accuracy, fairness, transparency, and unintended harms, the compliance functions within financial institutions must also evolve. It is no longer sufficient to review documentation at the end of the product pipeline. Compliance teams must be involved during the design stage of AI systems, interrogating the data being used, the assumptions being encoded, and the outcomes being optimized. There must be a strategic shift from being rule enforcers to risk translators, shaping the internal ethics and external accountability of institutions.
Conclusion
Ultimately, the future of AI in credit risk will not be determined by technology alone. It will be shaped by how we govern, who we include, and what values we encode into our systems. We must ask ourselves: Will AI be a tool for broadening access to finance or a shield for excluding those on the margins? Will it reduce human bias or merely repackage it into technical language we no longer interrogate?
The CBK’s survey has sparked an important conversation. While the National AI Strategy provides a guiding star, the heavy lifting still lies ahead. Financial institutions must make strategic, not cosmetic, investments in AI governance. Regulators must transition from observation to obligation, establishing enforceable standards and ethical thresholds, while the public must demand transparency, especially when algorithms impact their ability to build livelihoods and dignity through access to credit.
Kenya has never lacked vision. What we need now is regulatory coherence, institutional maturity, and a shared commitment to fairness. Only then can we say we are truly ready for AI, for risk, and for the future of finance.
