NextBillion - A WDI Publication

Digital finance has been a game-changer for the underbanked across Africa, with the ongoing development of mobile money platforms and other fintech innovations aimed at extending financial access to traditionally excluded, often rural communities. These technologies have enabled financial service providers to reach smallholder farmers, market traders, gig workers and other customer segments with credit facilities, bank accounts and other products. But many African communities and customers remain excluded, even as these approaches have become commonplace across the continent’s digital finance sector.

AI-powered lending tools could help close these remaining gaps in financial inclusion, promising enhanced efficiency, greater speed in completing transactions and the ability to scale to reach a wider range of underbanked people. However, these tools also introduce multiple risks which, if overlooked, can undermine trust, exacerbate inequality — or even exclude the very populations they are designed to serve.

EMERGING OPPORTUNITIES IN AI-DRIVEN DIGITAL FINANCE IN AFRICA

Due to the underdevelopment and infrastructure gaps that most African countries experience, along with the ongoing gap between the banked and the underbanked populations, the emergence of AI-driven digital finance presents many opportunities.

AI systems’ ability to analyze creditworthiness without the traditional documentation, such as manual loan applications and credit check forms, allows lenders to reach remote and rural populations that were previously excluded by major financial institutions, whose geographical presence is usually located in cities and urban regions.

Additionally, the machine learning models that AI systems depend on ensure that individuals are given more accurate credit scores based on their incomes and savings, and allow lenders to more effectively personalize financial products, such as insurance designed to address a customer’s specific risks, or credit with appropriate loan limits and repayment plans.

AI can also make it safer for providers to serve these customer segments. Compared to the traditional methods, where humans are exclusively responsible for fraud detection and prevention, AI can identify suspicious activity faster, thereby protecting both borrowers and lenders alike.

African governments could also leverage the aggregated insights from AI lending platforms. This would help them in making informed data-driven policies and macro-economic decisions, like determining when to offer subsidies and strengthen social safety nets, ultimately improving government policies affecting both the financial sector and regular citizens.

UNDERSTANDING THE INVISIBLE RISKS OF AI IN DIGITAL LENDING

For financial institutions that aim to deliver these benefits, the correct adoption of the technology depends largely on data. Analyzing data patterns from mobile money transactions, airtime purchases, utility payments and social network activity can enable AI algorithms to estimate creditworthiness, predict repayment behaviors and flag abnormal transactions that could result in fraud. However, when AI systems are not properly configured, they can result in some invisible risks.

One such risk is algorithmic bias, which refers to systematic and repeated errors that create unfair outcomes. For instance, if an individual or a group is privileged over another in the AI algorithm — not because of a lack of objective creditworthiness, but due to irrelevant or discriminatory factors introduced by the AI data or configuration — this can lead to inaccurate and unjust results for other individuals or segments within that same population. Therefore, if these datasets or algorithms place undue emphasis on the structural inequalities that exist between communities, the AI models may systematically undervalue the creditworthiness of the same populations that are already excluded.

Data privacy is another risk, encompassing issues like unauthorized access (through cyberattacks or poor data safety protocols), profiling and surveillance (by entities ranging from tech platforms and social media to government agencies), compliance issues with regulatory bodies, lack of ethical governance by financial institutions, and failure to safeguard user identities in digital ecosystems. For AI data privacy to be adequate, financial service providers will need to invest in robust data governance and maintain a strong commitment to “ethical AI” — i.e., practices that ensure that artificial intelligence is used in ways that are responsible, fair, transparent, and aligned with human values and legal standards. The main aim of ethical AI is to make sure that these technologies do not harm individuals or society, and that they respect people’s rights and dignity. Maintaining ethical AI practices is particularly important in the area of data privacy, since AI systems gather and use more customer data at a faster pace than previous systems did, requiring developers to adjust these systems continually, to remain one step ahead of attackers’ tactics.

A third risk comes into play when AI systems are not properly configured for digital banking, and their loan models unintentionally penalize individuals for lacking digital footprints, pushing them further beyond the reach of formal financial systems. AI assumes that more data = better loan candidates. As a result, first-time or infrequent borrowers may become trapped in a self-reinforcing loop, in which their lack of a digital credit history prevents them from accessing the loans that could allow them to develop that credit history. This dynamic has to be factored into the AI systems’ configuration to avoid unfair exclusion.

Additionally, there’s the risk of AI model instability, which — in the absence of proper monitoring — may create systems where changes in user behaviors or economic conditions can produce unintended outcomes. For example, if an AI credit scoring model was trained only for high-spending periods, it may incorrectly interpret the reduced spending that occurs during recessions or off-peak agricultural or travel seasons, which may result in reliable borrowers being denied access to credit or seeing their credit ratings downgraded. A similar scenario could happen during an inflation crisis, when higher prices cause people to spend more on basic goods, reducing their savings. When AI systems are not monitored and adjusted to these shifting realities, new spending and savings behaviors may be flagged as problematic, and eligible borrowers could lose access to funds precisely when they need them most

And lastly, there’s the risk of AI being used by bad actors: For instance, when an AI lending system detects fraud, rival companies and malicious attackers can also use the same AI to manipulate the system. Imagine if, as part of a lender’s AI system, a loan app is designed to detect fake loan applications. Criminals may test the system by using AI to submit many applications with small variations, to see which ones get approved. Once they determine which one works, they will use the prototype of the accepted fake application to continue to deceive the lender’s AI systems.

AI RISK MANAGEMENT IN PRACTICE

It is digital lenders’ responsibility to anticipate and mitigate these risks by incorporating AI risk management, which must extend to data governance and compliance, bias and transparency, and cybersecurity and risk management, as discussed below.

Data Governance Frameworks: To enable the safe usage of AI in digital financial services, data governance policies, procedures and standards must be established and enforced, ensuring that quality and security are maintained throughout the data lifecycle — from collection, processing and storage, to sharing, usage and archiving. A number of African countries are taking steps to foster appropriate data governance in AI. For instance, standards such as ISO 27001 (a global standard that helps organizations manage and protect information securely through a structured, audited framework) provide guidance for protecting sensitive financial, personal and organizational information, while regulations such as Nigeria’s Data Protection Regulation, Kenya’s Data Protection Act and South Africa’s Protection of Personal Information Act, among others, have been implemented to enforce data protection compliance.

Bias Audits and AI Model Transparency: AI system developers and financial institution representatives must evaluate and examine every AI algorithm for bias or unintended discrimination. It is also essential to provide customers with simple and clear explanations of why certain financial decisions, like the rejection of a loan application, are made. This transparency fosters trust and empowers customers to challenge any inaccurate assessments.

Risk Assessment: One of the benefits of an AI system is its capacity to identify irregular income patterns and other anomalies that might affect a loan applicant’s creditworthiness. However, human oversight is essential to validate AI assessments and ensure context-sensitive decision-making. Therefore, there must always be human intelligence engaged in the continuous monitoring of AI systems — i.e., random assessments conducted independently of AI, to double-check or confirm its findings — to detect changes in borrower behavior, fraud patterns or systemic vulnerabilities.

COLLABORATIVE RISK MANAGEMENT FOR AI

However, it’s important to recognize that AI risk management is not solely a technical problem; it is a collaborative one. Financial institutions, regulators, fintech startups and civil society must work together to ensure that AI-driven financial systems support inclusive growth.

While fintechs must embed risk management, bias detection and ethical AI practices in their systems, regulators must issue guidance on algorithmic fairness, transparency and consumer protection, helping to ensure a balance between innovation and reasonable safeguards.

Investors and donors must also play a role in incentivizing responsible AI adoption by funding platforms that prioritize appropriate governance and inclusion. Additionally, industry networks and leaders must facilitate knowledge-sharing through platforms that promote best practices in AI risk management and compliance across markets.

But although a few African countries — like Nigeria, Kenya, South Africa and Rwanda — are advancing digital finance regulations, there are still significant gaps between different African jurisdictions. Some countries lack specific guidance for AI-driven credit scoring and digital lending algorithms. Many lack clear policies on digital financial inclusion, and some have no strict regulations to oversee fintechs. Consequently, some fintechs are not adhering to the expected compliance requirements — a problem that’s not limited to AI — and borrowers may remain exposed to unacceptable levels of risk.

LOOKING AHEAD

AI-driven digital finance holds enormous potential to advance financial inclusion across Africa. However, this promise requires stakeholders across the financial sector to recognize and mitigate invisible threats by establishing and adhering to governance frameworks, ensuring ethical AI adoption. These measures can allow lenders to unlock the benefits of AI adoption, while ensuring that underbanked populations are not left unprotected.

Just as it did with mobile finance, Africa has the opportunity to lead the world in building AI-driven financial systems that are inclusive, resilient and responsible. But for that to happen, the continent must not simply introduce AI technology into its existing lending systems, but rather build a new foundation for these systems based on trust, transparency and accountability.

Nathaniel Adeniyi Akande is an information technology security analyst.

Photo credit: peerapong boriboon