Artificial Intelligence (AI) is used widely by many industries, including the financial sector which has become one of the biggest players in the area. No surprise – AI can do remarkable things, from compiling and providing massive datasets in seconds, to quickly analysing data, and suggesting decisions on loan eligibility, and even the personalization of financial offers in alignment with actual needs. At the same time, as global debt levels reach historic highs, developing countries face increasing barriers to accessing fair and affordable financing due to their higher debt risk scores. That’s why the use of AI and alternative data in credit risk assessment is now being seen as an innovative solution. However, these opportunities are accompanied by a series of challenges. While AI can improve financial inclusion, there are risks of reinforcing disparities for vulnerable and marginalized groups, including women and minorities. Read the article below to learn more about the pros and cons of using AI to assess debt risks in developing countries.
Key Takeaways:
- According to one of the World Bank’s latest reports (2024), “The Use of Alternative Data in Credit Risk Assessment: Opportunities, Risks, and Challenges”, the integration of alternative data has emerged as a significant advance in credit scoring practices.
- Experts outline the positive side of using AI to assess debt risks in developing countries as it reduces human bias by minimizing subjective judgments to provide credit ratings, and also uses up-to-date databases that are collected in a timely manner from diverse sources to quickly analyse and send results.
- With regard to its limitations, when algorithms are trained on biased or incomplete datasets, they tend to reinforce systemic inequalities. Limited data availability in low-income countries, combined with opaque governance models, can also raise accountability concerns.
- To ensure that AI becomes a tool for inclusion rather than exclusion, policymakers must prioritize transparency, strengthen governance over algorithmic systems, and invest in local data and skills.
DevelopmentAid: What are the pros and cons of using artificial intelligence to assess debt risks in developing countries?
“While the incorporation of AI into debt risk assessment promises innovation, it also brings a slew of serious issues that may ultimately reinforce the imbalances it seeks to solve. A major worry is the opacity and lack of explanation in AI models. This can weaken accountability and transparency, especially in sovereign environments where borrowing decisions have long-term social and economic ramifications. Without explainable techniques, underdeveloped countries may have few options for challenging or understanding the negative credit ratings determined by AI systems. Data inequality is also a source of concern. Many developing economies face high-quality and frequent gaps in economic, financial, and demographic data. AI algorithms that are predominantly trained on high-quality information from affluent economies run the danger of distorting or underestimating the creditworthiness of developing countries. When algorithmic projections are based on insufficient or obsolete data, the ensuing assessments may unfairly represent these countries as risky, thereby discouraging investors and increasing borrowing rates. Furthermore, bias that is entrenched in data and design can exacerbate systemic imbalances. If the algorithms are designed with Western-centric assumptions about fiscal prudence, governance, or institutional performance, they may punish alternative development strategies adopted by the Global South. Such algorithmic bias has the potential to standardize a particular worldview based on advanced-economy experiences while marginalizing economic variety and context-specific reality. Another significant disadvantage is the technological dependence that results from outsourcing risk assessment to AI systems owned or operated by multinational banks or private analytics firms in industrialized countries. This exacerbates existing control imbalances in financial narratives, allowing external actors to determine a poor country’s credit profile using opaque means. Finally, ethical and sovereignty concerns arise when automated systems exert influence over fiscal policy, debt restructuring, or capital access without democratic review. The air of objectivity that surrounds AI risks masking power inequities and undermining local agency. In conclusion, while AI may promise precision, uncritical adoption may entrench global financial hierarchies under the illusion of digital neutrality.”
“AI offers a powerful opportunity to transform how risk is assessed in emerging economies. By processing vast and unconventional data sources from satellite images to digital payment records, AI can generate more comprehensive and evidence-based risk models. This innovation challenges long-standing biases and outdated perceptions that often lead to the undervaluation of developing markets. With a clearer view of economic performance and growth potential, AI can help to unlock fairer credit ratings, lower borrowing costs, and attract private investment. However, this promise comes with real challenges. Disparities in data infrastructure and technical expertise could leave low-income countries further behind. To ensure that AI becomes a tool for inclusion rather than exclusion, policymakers must prioritize transparency, strengthen governance over algorithmic systems, and invest in local data and skills. When designed and deployed responsibly, AI can serve as a catalyst for more equitable and sustainable economic development.”
“Potential Benefits: AI can analyze the vast alternative datasets – satellite imagery, mobile payments, trade flows – that traditional ratings miss, potentially revealing that some developing countries are less risky than perceived. This could lower borrowing costs and attract private investment. AI could also reduce human bias in credit ratings and provide real-time monitoring instead of outdated quarterly assessments. Significant Concerns: Developing countries often lack the data infrastructure that AI requires, creating a ‘data divide’ that reinforces inequality. Opaque algorithms prevent countries from understanding or challenging their ratings, undermining sovereignty. Models trained on Western economic data may encode assumptions inappropriate for developing contexts, while historical biases in training data can be amplified rather than eliminated. Real-time AI monitoring could also increase volatility, potentially triggering self-fulfilling financial crises. The Bottom Line: AI’s impact depends entirely on governance. Without transparency, local participation in system design, investment in data infrastructure, and international standards preventing discrimination, AI risks entrenching the very inequalities it promises to solve rather than creating fairer access to finance.”
“As I know from my own consulting experience supporting various development finance institutions globally, AI can greatly enhance debt-risk assessment by processing vast, multidimensional datasets far more quickly than traditional analytical models. For developing countries, this can improve transparency, flag early warning signs, and help lenders to differentiate between high- and low-risk borrowers with greater precision. In theory, that should translate into the fairer pricing of sovereign and project loans and a gradual reduction in perceived ‘frontier market’ risk premiums. Yet AI is only as fair as the data it learns from. When algorithms are trained on biased or incomplete datasets (especially those reflecting Western market conditions), they tend to reinforce systemic inequalities rather than correct these. Limited data availability in low-income countries, combined with opaque governance models, can also raise accountability concerns. Without transparency and human oversight, AI risk scoring could end up substituting one form of bias for another. Used responsibly, however, AI offers a powerful complement, albeit certainly not a replacement, for sound judgment and contextual financial analysis in development finance.”
“Using AI to assess debt risk in developing countries could present both pros and cons. On the positive side, AI reduces human bias by minimizing subjective judgments to provide credit rating, and it also uses up-to-date databases that are collected in a timely manner from diverse sources to analyze and send results quickly. However, there are also limitations. AI cannot ensure the accuracy nor reliability of data inputs when interpreting and assessing. It assesses purely on economic databases, and it also cannot predict/analyze economic situations, political circumstances, short- and long-term policies, and combine all these factors before rating the credit risk, but all of which strongly influence credit ratings. Moreover, AI-generated results still require experts to determine whether it is reasonable, while the algorithms are often complex and difficult to interpret. Finally, underdeveloped infrastructure and cybersecurity in developing countries may also be a barrier to accessing and evaluating credit risk, potentially leading to lower credit scores despite favorable economic and policy conditions.”
“As developing countries face mounting debt burdens, AI is emerging as a tool for rethinking how credit risk is assessed. Multilateral lenders such as the World Bank and the African Development Bank are increasingly turning to AI-driven analytics to strengthen decision-making and attract private investment to emerging markets. AI’s promise lies in its ability to process vast datasets on macroeconomic trends and fiscal indicators, offering a faster and more objective lens on creditworthiness. This approach can help uncover growth potential in developing countries like Zambia and Kenya – markets that have often been misjudged under traditional risk models – and could contribute to lowering their borrowing costs. Yet the challenges are equally profound. Many AI systems rely on datasets that are rooted in Western economic structures, overlooking the informal economies, policy volatility, and social dynamics that shape risk in developing contexts. These blind spots risk reinforcing rather than correcting bias. For AI to truly advance debt sustainability, its use must emphasize transparency, inclusivity, and contextual understanding. Integrating local data and expertise will ensure that AI serves as a bridge toward equitable finance rather than a barrier that has been shaped by algorithmic assumptions.”
See also: The impact of artificial intelligence on the environment | Experts’ Opinions
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