How AI Can Close the Financing Gap in Emerging Markets

Access to credit has long been a hurdle for unbanked populations in emerging markets. According to the World Bank, nearly 1.4 billion adults worldwide remain unbanked, with a significant portion residing in developing countries like Mexico. This creates a bottleneck for economic growth. The challenge is notably critical in agribusiness, where traditional banks cannot lend as the industry requires due to insufficient or unreliable data about farmers.

For example, for Mexico’s 15 million agricultural producers, the issue isn’t just about credit availability, credit history, or the financial strength of the farmer, it’s about trust, and understanding the complex situation of their businesses. Farmers often rely on informal lending or government support because traditional banks see them as high-risk borrowers. This perception can be justified because the most important dataset to analyze for most farmers is qualitative data, such as verbal assurances of yield projections or informal records of past productivity. This kind of data cannot easily be quantified or verified by banks.

The Data Gap: From Qualitative to Quantitative

The challenge is transforming qualitative data into quantitative. For instance, instead of assessing a farmer’s creditworthiness based solely on historical repayments, tax declarations, or financial statements, we could integrate data points such as weather forecasts, the farmer’s expertise, how technified the farm is, the farm’s soil health, access to water, or even supply chain strength, among many others variables.

These elements are usually just part of the potential borrower's background without being considered for formal analysis; for banks, they are just anecdotes. However, these data points hold the key to assessing the risk and opportunity with more accuracy. Integrating such diverse variables requires advanced technological tools capable of processing and learning from complex datasets.

Artificial intelligence is transforming the financial sector, enabling lenders to quantify and analyze previously subjective variables. With machine learning models, large volumes of unstructured data, such as satellite imagery, climate statistics, and transaction histories, can be processed rapidly to generate actionable insights.

For agribusiness in Mexico, AI can predict crop yields with higher accuracy using historical weather and soil data, evaluate risk levels by mapping supply chain dynamics and market fluctuations, customize credit products tailored to the specific needs and repayment cycles of farmers, and many other analyses that will allow lenders to not only rely on credit history, tangible collateral, financial statements, and tax declarations.

The speed and precision of AI-driven models allow financial institutions to reduce credit risk, lower costs, and unlock capital for underserved populations. By embracing AI, lenders can shift from exclusionary practices to inclusive growth strategies.

Opportunities for Capital Markets and Success Stories

The adoption of alternative credit scoring models isn’t just a theoretical exercise. Several fintechs in emerging markets are already using data analytics to provide tailored credit solutions to underserved populations who would otherwise remain outside the banking ecosystem. By integrating AI, they assess borrower risk holistically, enabling loans to those who traditionally have been overlooked.

For financial institutions, the message is clear: those who embrace these models early will gain a competitive edge. By tapping into unbanked segments like Mexican agribusiness, banks can expand their portfolios while contributing to broader economic progress.

AI-powered credit scoring is no longer an experiment; it’s a necessity. For agribusiness and other underserved industries in Mexico and beyond, the potential to turn qualitative assumptions into quantifiable realities means better access to capital and more inclusive economic growth. Traditional lenders and capital markets have a unique opportunity to bridge the financing gap, enabling millions of unbanked individuals and industries to thrive.

The future of financing isn’t just about loans; it’s about creating trust through data. The question for banks is no longer “if” they should adopt these models but “how quickly” they can adapt to this paradigm shift.