AI in Lending and What Developed Countries Can Learn From Emerging Markets

Small and medium-sized enterprises (SMEs) represent the backbone of economies worldwide, especially in emerging market economies, where they account for nearly 40% of the GDP. However, an approximately $5 trillion credit gap persists, according to the World Bank, which inhibits SME growth and limits innovation, employment, and economic resilience.

Artificial intelligence and other technologies are beginning to close this gap. In regions where traditional banks have underserved small businesses, AI in lending is redefining the landscape for SMEs, and emerging economies are building the future.

Emerging markets are creating scalable financial ecosystems through AI in lending and other technological solutions. Their early successes provide the wealthy world with a blueprint for building more inclusive, effective financial systems — one algorithm at a time.

From automating credit checks to designing tailored loan products utilizing alternative data, AI in lending is empowering lenders to think past outdated underwriting models centered on credit scores and collateral. The move has led to quicker loan approvals, better risk mitigation, and ultimately broader access to capital. 

Several thought leaders and economists recently met in New York City to share perspectives on how artificial intelligence is impacting financial services and other industries. We’ll dive into those insights and discuss the role of AI in emerging markets and how AI in lending is changing access to capital for businesses.

How One Hub Is Building Its Own Innovation Engine

One compelling example of how emerging markets are building future-ready financial ecosystems is the United Arab Emirates (UAE). Its approach combines infrastructure investment, policy innovation, and global partnerships — creating a blueprint developed nations would do well to follow.

Jawahar Almheiri, Head of Economic Policy and Affairs at the UAE Embassy in Washington, explains how the country has actively shaped rather than adapted to AI trends. citing that the UAE “appointed its first AI minister in 2017, “reflecting “its ambition to lead the next economic revolution.”

Key to the UAE’s AI strategy is its ingenuity to foster innovation at scale. Projects like its “cloud computing champion,” G42, Falcon’s open-source language model, and Med42 healthcare AI, exemplify the nation’s capacity to compete globally in both general and specialized AI applications. 

The UAE is also making massive investments in global AI infrastructure to the tune of $100 billion through partnerships with MGX, Blackrock, Microsoft, and Global Infrastructure Partners.

Furthermore, the country has fostered a supportive regulatory environment for fintech and AI companies, including full foreign ownership and tax incentives, making Dubai and Abu Dhabi regional hubs for capital and technological innovation. 

With an ecosystem that allows Fintechs to freely test out AI solutions, “AI-powered financial innovation can move from concept to market faster than anywhere in the region, ”Almheiri said. “But we offer something even more valuable: a testing ground for the future.”

For banks and lenders, the UAE’s strategy shows how policy design, infrastructure investment, and openness to global links can accelerate the adoption of new lending models.

Convergence: The Most Powerful Economic Trend of Our Time

Professor Jeffrey Sachs, renowned economist and Director of the Center for Sustainable Development at Columbia University, believes the use of AI could lead to more inclusive access to financial products and more inclusion overall for emerging markets. 

“AI is going to be yet another, but rather a decisive tool for progress because it is a general-purpose technology…it permeates every single sector of the economy,” Sachs said.

Sachs’ notion of convergence — a trend where poorer countries are growing faster than wealthier ones — can be seen in action in countries like India. In just over a decade, India has transitioned from a cash-heavy economy to near-universal digital payments, providing a clear example of how emerging markets are leapfrogging traditional banking systems.

This transition in India isn’t just about convenience; it’s about enabling access to financial services for millions of small and medium-sized enterprises (SMEs) that previously lacked credit histories. AI is playing a central role in helping these businesses gain access to the capital they need to grow.

The lesson banks can learn from this is to rethink their roles in an increasingly digital financial environment. Traditional banking models based on physical infrastructure and centralized systems are becoming obsolete. Credit evaluations, risk assessments, customer onboarding, and payments are shifting toward decentralized, real-time, AI-driven platforms. 

Leveraging AI in lending also lowers the cost of servicing smaller or riskier clients and increases financial inclusion for business owners who’ve been previously underserved with traditional credit scoring models.

AI in Lending and the SME Financing Revolution

Rohit Arora, Co-Founder and CEO of Biz2X and Biz2Credit, points out that small and medium-sized enterprises (SMEs) are the “engine of economic growth,” particularly in emerging markets. Yet for decades, they’ve encountered a persistent barrier with regard to credit access. 

Traditional banks historically have spent lots of time and money on teams to assess credit and perform risk assessments, which haven’t always served them well, especially with businesses that have thin credit files or weak financial documentation.

This is where AI shines. AI in digital lending analyzes alternative data to assess creditworthiness with unprecedented speed and accuracy. These models reduce the cost of underwriting dramatically, enabling financial institutions to profitably serve SMEs that would otherwise be excluded.

This kind of inclusion wasn’t even possible in many parts of the world until recently. Now, thanks to digital platforms, small and regional banks can serve more SMEs with AI systems that were once only accessible to larger financial institutions. And they do so without increasing their risk profiles.

Why Traditional Models Fail in Underserved Markets

In much of the world, formal financial systems don’t reach far enough. Many small enterprises and individuals lack access to credit because they don’t have standard credit files, formal accounts, or ideal documentation. This is particularly true in rural areas or in economies where much activity is informal. 

That has meant a big gap in capital access. Potential borrowers who are reliable may be excluded simply because they lack conventional records. And that is a loss both for them and for potential lenders: missed opportunity, inefficient capital use, and an economy that slows in growth.

AI technology changes that. AI-powered tools look beyond conventional records. They analyze alternative signals, like payment trends, transaction data, ecommerce activity, and online behavior, to build a profile of creditworthiness. 

In practice, that means many businesses previously invisible to formal finance can be included in credit systems. For banks, AI in lending can expand loan portfolios, help gain new customer segments, and build long-term loyalty.

How Does AI in Digital Lending Work?

AI-powered lending is changing the small business process and transforming the way banks handle applications, assess risk, and determine loan approvals. Digital platforms streamline loan application workflows, from underwriting to funding. As it does so, AI technology helps reduce costs and accelerate decision-making.

• Alternative data uses credit scoring and risk assessment models to make quicker, more accurate lending decisions .
• Natural language processing automates document review to help avoid manual delays.
• Gen AI is being used to better serve borrowers with real-time answers, resulting in better customer experiences.
• AI tools optimize interest rates for risk and market indicators to provide fair pricing to SMEs.

AI systems help banks in meeting regulatory compliance by monitoring evolving rules and providing transparent audit trails. With strong digital infrastructure and solid partnerships, small and regional banks (and banks in emerging markets) are developing advanced platforms.

Banks can make better credit decisions using AI in lending, tap into new customer bases, and increase their loan books without increasing risk or operational costs. More importantly, AI in digital lending opens doors for small businesses previously denied access to capital.

Conclusion: From Using Digital Tools For AI in Lending to Leading the Charge

The shift happening in the lending industry isn't just about smarter algorithms or digital platforms. It signals a bigger transformation in how credit is assessed, how capital flows, and how financial opportunities become accessible to those who’ve been excluded in the past. 

As examples from emerging markets like the UAE and India have shown, these changes are already taking root — and developed countries can model their strategies for building more inclusive and resilient financial systems.

Banks mustn’t view emerging technologies as distant trends. AI innovations will continue to be the future. Thoughtful adoption of AI in lending, local adaptation, risk control, and expert partnerships is essential to stay competitive and relevant and earn a fair market share as digital platforms become more commonplace.

This is a moment for your bank to lead. AI in lending can make it happen. 

FAQs

1. How is AI in lending being used in the loan application process?

AI in lending leverages real-time data and machine learning algorithms to make an informed decision about borrower creditworthiness and the risk of them defaulting on the loan. AI-driven platforms can also generate loan terms that are appropriate for the borrower’s financial and credit profile while staying within a bank’s own risk guidelines.

2. What are some emerging trends seen in conjunction with AI in lending?

Three of the biggest AI in lending trends at the moment are personalization through Gen AI, increased autonomy through agentic customer tools, and embedded finance.

3. What can developed countries learn from emerging markets in AI adoption, particularly where AI in lending is concerned?

Developed countries should move more quickly in implementing newer technologies within their workflows. Emerging markets have shown that innovation sandboxes and openness to digital transformation accelerate financial access and efficiency.

4. How does AI in lending assist with closing the capital gap for small business owners?

Using AI in lending removes the human bias that has long plagued the industry. Instead, it bridges those financial gaps and increases funding access by accessing alternative data sources and AI algorithms to assess a borrower. The result is often more inclusion and capital in the hands of small businesses that drive the economy.

5. How does AI in digital lending reduce costs for lenders? 

AI in lending automates processes that used to be carried out manually, including underwriting, credit assessment, and documentation. Automation lowers operational costs and enables lenders to scale services to a broader base of borrowers without compromising accuracy or increasing default rates.