How AI can reduce defaults and expand financial inclusion in microcredit markets

he study notes that most MFIs rely on standardized interest rate systems that treat borrowers uniformly, regardless of the individual likelihood of repayment. This approach leaves low-risk borrowers paying higher rates than necessary, while high-risk borrowers may receive credit underpricing that weakens institutional resilience.

Microfinance institutions across emerging economies may soon be able to offer fairer and more sustainable lending conditions, thanks to artificial intelligence systems capable of predicting credit risk with unprecedented accuracy. A new academic investigation suggests that machine learning models can transform how microcredit interest rates are calculated, reducing barriers for low-income borrowers while strengthening the financial stability of institutions that serve them. 

The study, titled “Promoting Financial Inclusion by Optimising Financial Interest Rates Based on Artificial Intelligence in Microfinance Institutions,” published in the International Journal of Financial Studies, provides a detailed empirical analysis of how AI-powered risk assessment can reshape the microfinance landscape. The research applies a machine learning framework to thousands of real lending transactions to determine how interest rates can be more accurately aligned with borrower risk. Their findings indicate that AI-enhanced models not only outperform traditional credit evaluation techniques but also create opportunities for more inclusive lending practices in developing regions.

Microfinance institutions struggle with high risk and inflexible pricing models

Microfinance institutions (MFIs) play a crucial role in extending credit to individuals and small businesses excluded from the formal banking sector. However, these institutions often face high default rates, limited access to capital and regulatory obligations that restrict the flexibility of their lending operations. The study notes that most MFIs rely on standardized interest rate systems that treat borrowers uniformly, regardless of the individual likelihood of repayment. This approach leaves low-risk borrowers paying higher rates than necessary, while high-risk borrowers may receive credit underpricing that weakens institutional resilience.

To address this longstanding challenge, the authors analyze a dataset of 4,550 microcredit transactions from a Guatemalan MFI, covering 30 variables that include repayment history, loan characteristics, business performance indicators, demographic information and broader economic factors. Their goal is to determine how well AI can predict the probability of default and how those predictions can be integrated into a Basel III Internal Ratings-Based (IRB) model for setting interest rates.

The IRB model is widely used in banking to align regulatory capital requirements with risk exposure. MFIs, however, rarely implement such systems due to cost, complexity and limited internal data analytics capability. The authors argue that machine learning offers a scalable path for MFIs to adopt IRB-driven pricing without the high barriers that typically accompany advanced financial modelling.

The study tests three methods for determining probability of default: Linear Discriminant Analysis, Logistic Regression and a Multilayer Perceptron (MLP) neural network. The MLP model produces the most accurate predictions, achieving a substantially higher ability to distinguish between risky and reliable borrowers. The neural network also minimizes misclassification costs, meaning it better identifies which borrowers are likely to default and which are not.

These findings lay the groundwork for using AI to address a fundamental tension in microfinance: how to expand lending access without jeopardizing institutional sustainability. By identifying risk levels more precisely, MFIs can offer differentiated interest rates that reward good borrowers, support community development and protect portfolios from excessive exposure.

AI models reveal how fairer pricing can improve access and reduce capital pressure

Once the probability of default is calculated using the MLP model, the researchers apply the IRB framework to convert these probabilities into individualized interest rates. The IRB model calculates expected loss, unexpected loss and required risk capital for each borrower, enabling MFIs to set prices that reflect genuine credit risk rather than broad estimates.

The results demonstrate meaningful differences between traditional standardized interest rates and those generated through AI-based IRB modelling. Under the standardized approach, borrowers pay a fixed rate regardless of their risk profile. In contrast, the AI-IRB model produces a wide distribution of interest rates ranging from 4.32 percent to 48.89 percent. Low-risk borrowers benefit from significantly reduced rates, while high-risk borrowers face pricing that better reflects the cost of capital associated with their credit profile.

The study finds that borrowers with a probability of default below 19.30 percent could access credit at lower rates than those applied by standardized systems, supporting financial inclusion by lowering the economic barrier for clients with strong repayment behavior. This is particularly relevant for women entrepreneurs, informal workers and small businesses that often demonstrate discipline in repayment but lack access to risk-sensitive lending systems.

For MFIs, the benefits are twofold. First, better risk segmentation improves portfolio stability by reducing exposure to unexpected losses. Second, the ability to adjust pricing according to borrower characteristics helps institutions remain competitive in markets where digital lenders and fintech platforms increasingly offer tailored products. The study argues that AI-driven IRB systems reduce reliance on one-size-fits-all pricing and empower MFIs to reward good borrowers without jeopardizing financial health.

Machine learning techniques also highlight key variables that influence repayment behavior. Through SHAP interpretability tools, the authors identify factors that increase default likelihood, such as high debt burdens, previous loan denials and weak business performance, as well as those that reduce it, including long-term client relationships, stable employment and strong asset returns. These insights can support MFIs in refining credit evaluation policies and developing targeted financial literacy programs.

The integration of machine learning into IRB frameworks marks a significant advancement for microfinance, where operational constraints have historically limited the use of sophisticated analytical tools. By demonstrating a practical, data-driven approach, the study shows that MFIs in emerging economies can adopt global risk management standards without excessive resource requirements.

Implications for financial inclusion and the future of AI in microcredit markets

While the results come from a Guatemalan microfinance institution, the methodology is adaptable to other emerging markets. Many low-income countries share structural similarities, informal employment, volatile income streams, limited collateral and restricted financial records, that make credit evaluation challenging. AI-driven pricing systems may help overcome these obstacles by generating accurate risk assessments even in environments with inconsistent or incomplete data.

The study’s implications extend beyond credit pricing. More accurate risk modelling can enhance transparency, build borrower trust and encourage long-term financial engagement. By aligning interest rates with actual risk, MFIs can foster stronger client relationships, reduce delinquency and promote responsible financial behavior.

However, the authors caution against viewing AI as a standalone solution. Successful adoption requires reliable data collection, staff training, internal governance mechanisms and careful monitoring to avoid unintended biases or ethical concerns. Institutions must ensure that algorithms do not reinforce existing inequalities or disadvantage marginalized groups. Proper oversight and regulatory support will be essential as AI systems become more deeply embedded in lending processes.

The study also identifies opportunities for future innovation. Combining machine learning with alternative credit scoring techniques, such as mobile phone usage analysis, digital payment histories or behavioural biometrics, could further improve predictive accuracy in markets where traditional financial records are scarce. Expanding AI use into portfolio risk management, early delinquency detection and personalized borrower advisory services may also reshape the future of microfinance.