AI for Financial Institutions in emerging markets

AI for Financial Institutions in emerging markets

Artificial intelligence (AI) is now deeply embedded in global financial institutions (FIs), but its role varies between emerging markets and advanced economies, bringing with it a whole new set of risks that Risk Managers and Insurers alike need to be aware of. While banks everywhere use AI to detect fraud, assess creditworthiness and automate operations, the motivations and risk trade-offs differ in important ways.

Inclusion versus optimization

In many emerging markets, AI has become a workaround for structural gaps in financial infrastructure. Large parts of the population remain underbanked, credit histories are thin or non-existent and traditional risk models exclude thousands of potential customers. Machine-learning models trained on alternative data (mobile phone usage, transaction patterns, utility payments) allow lenders to extend credit where conventional approaches would struggle to.

This is most visible in mobile-first financial ecosystems across Africa, Southern Asia, where digital wallets and fintech platforms operate at population scale. Here, AI is less about marginal efficiency gains and more about basic market access. Risk models are designed to say “yes” more often, accepting that this means increased uncertainty in their models.

By contrast, FIs in more established marketplaces already serve mature, data-rich markets. AI is used primarily to refine rather than reinvent risk decisions, such as reducing false positives in fraud detection, automating compliance workflows or stress-testing large portfolios under increasingly complex scenarios. The objective is not expansion, but optimization within tight regulatory boundaries.

A different risk calculus

These differing priorities shape how risk is understood and managed.

In emerging markets, the dominant risks associated with AI adoption tend to be operational and executional. Data quality is often uneven, governance frameworks are still developing and many institutions rely heavily on third-party AI vendors. Models may perform well in practice, but their inner workings are not always fully understood or explainable by the institutions deploying them.

AI systems are often more integrated (for example influencing who gets credit, who is flagged for fraud or who is excluded entirely) while being less tightly governed. Bias, model drift and cyber vulnerabilities can persist longer before detection, particularly where supervisory capacity is limited.

In more established markets, the risk profile looks different. Here, AI introduces more tension around governance, accountability and systemic impact. Regulators increasingly expect banks to demonstrate not just that models work, but that they can be audited, explained and overridden. ‘Human-in-the-loop’ controls, documentation requirements and model validation processes are becoming standard.

As a result, some of the most advanced AI capabilities particularly generative models are deployed cautiously, often in internal or low-risk settings first. The risk is less about whether AI can make a decision and more about whether that decision can withstand regulatory and legal scrutiny.

Regulation as a dividing line

The largest difference can therefore be shown in regulatory maturity.

European institutions operate under an increasingly prescriptive regime, with the EU AI Act and banking supervision frameworks pushing AI firmly into the realm of regulated risk. In this environment, AI adoption is inseparable from model risk management, data governance and operational resilience planning. In the US, they have a less centralized but still risk-intensive approach, relying on supervisory guidance, enforcement actions and existing financial regulations to shape AI behavior. Innovation moves faster, but institutions are expected to manage the consequences.

In many emerging markets, by contrast, regulation is still catching up with the rapid evolution of AI. Central banks and supervisors are experimenting with AI themselves for market surveillance or fraud detection even as they develop rules for private-sector use. This creates space for rapid innovation but also raises the risk of uneven standards and regulatory arbitrage.

A convergence soon?

Over time, these paths are likely to converge. As emerging markets scale their AI-driven financial systems, pressure will grow for stronger governance and systemic oversight. Meanwhile, advanced economies may look to emerging markets for lessons on speed, inclusion and practical deployment.

For now, however, the risk story remains divided. One of the broker’s most valuable roles is interpretation as AI risk is often discussed in abstract terms; or example, bias, explainability and systemic exposure that do not map neatly onto traditional insurance categories.

Willis can translate these concerns into familiar risk frameworks:

• Operational risk (model failure, data errors, automation breakdowns)
• Professional liability (AI-driven advice or decisions causing client harm)
• Cyber and data risk (training data leakage, model manipulation, third-party exposure)
• Regulatory and compliance risk (use of AI that breaches evolving local rules)

A key value Willis brings is clarity. For multinational clients operating across emerging and developed markets, this translation function is critical. The same AI model may carry very different regulatory and reputational risks depending on where it is deployed. Willis can help structure insurance programs that reflect real AI usage. At the same time, we can be clear about the limits of insurance as certain risks remain fundamentally non-transferable, for example:

• reputational damage from opaque or biased decisions
• regulatory sanctions tied to governance failures
• systemic model issues affecting large customer populations

By distinguishing between insurable and non-insurable risk, Willis can help you focus investment where it matters most. Talk to your local Willis broker about how our experience and specialist knowledge can help you navigate, as part of your risk management strategy, the complexities of using AI in your business.