AI as a Catalyst for Sustainable Finance in Emerging Markets

AI as a Catalyst for Sustainable Finance in Emerging Markets

How Artificial Intelligence Overcomes Financial Barriers, Optimizes Investments, and Accelerates Sustainable Development

Artificial intelligence (AI) is much more than just a technological innovation—it is a transformational catalyst that is reshaping financial systems in emerging markets. From mobile payments to credit assessment and risk management, AI provides access to financial tools for individuals and businesses that have historically been excluded from traditional financial systems. According to a McKinsey report, AI could increase the GDP of emerging markets by $1.5 to $2 trillion by 2030 by improving financial accessibility and efficiency.

Emerging economies often lack well-developed financial institutions and infrastructure, and bureaucratic processes tend to be slow and inefficient. AI plays a crucial role in addressing these issues by ensuring transparency, accessibility, and efficiency in financial services. Let’s explore six key ways AI is transforming sustainable finance in emerging markets.

1. Improving Financial Accessibility Through AI-Based Lending

Why is financial inaccessibility a problem?

In many emerging markets, access to financial services remains limited. According to the World Bank, over 1.7 billion people worldwide do not have a bank account. The primary reasons for this include:

• Lack of credit history – Most banks rely on traditional credit reports, but in emerging markets, a large portion of the population has never used an official financial instrument, leaving them outside the system.

• Limited financial infrastructure – In some regions, there are not enough bank branches or access to digital financial services.

• Strict borrowing conditions – Even when banking access exists, credit requirements are often too strict, excluding low-income individuals.

These barriers mean that small and medium-sized enterprises (SMEs), which form the backbone of economies in developing countries, lack access to critical financing, stifling their growth.

How does AI change the game?

AI provides an alternative method for assessing creditworthiness that does not rely on traditional credit reports. Instead, it uses alternative data analysis such as:

• Mobile payment history – AI analyzes transaction patterns from mobile payment platforms (such as M-Pesa in Africa) to determine financial habits.

• Behavioral models – AI examines spending habits, payment frequency, and income regularity to offer more accurate credit assessments.

• Social and professional networks – AI evaluates work history and social connections that indicate financial responsibility.

According to a study by the International Finance Corporation (IFC), AI-based lending increases the accuracy of credit assessments by up to 50% and reduces default risks by 30%. This means more people and businesses can access fair financing options.

Example: Tala and Branch

Tala and Branch are two AI-driven platforms that assess creditworthiness in countries such as Kenya, India, and the Philippines. They analyze mobile phone data—including call history, mobile payment frequency, and geolocation—to determine financial reliability. This enables rapid loan approvals that would otherwise be inaccessible through traditional banks.

Why is AI critical in this process?

Financial accessibility is not just a matter of convenience—it drives economic development. When small businesses have access to capital, economies expand, new jobs are created, and poverty decreases. AI offers a fairer, more efficient lending model, democratizing financial systems and integrating more people into the formal economy.

2. Predicting Climate Risks for Sustainable Investments

Why are climate risks so critical?

Emerging markets are the most affected by climate change, as their economies heavily depend on agriculture, fisheries, and extractive industries—sectors highly vulnerable to weather conditions. According to the Germanwatch Global Climate Risk Report, 90% of the countries most susceptible to climate-related disasters are in developing regions.

Major challenges include:

• Unreliable forecasts – Traditional methods of predicting droughts, hurricanes, and floods are often inaccurate, making planning difficult.

• Lack of adaptation strategies – Many businesses and farmers lack tools to mitigate losses from extreme weather events.

• Uncertainty about future risks – Without accurate predictions, directing investments into sustainable projects is challenging.

How does AI help solve these problems?

AI analyzes vast amounts of climate and environmental data—including satellite imagery, historical weather trends, and IoT sensor data—to predict climate change impacts on agriculture, infrastructure, and the economy.

How AI enhances climate risk analysis

• Automated forecasting – AI models process data on precipitation, temperatures, and humidity to predict droughts, floods, and extreme temperatures with up to 80% greater accuracy than traditional methods.

• Optimizing agricultural practices – AI helps farmers decide when to plant crops, manage irrigation, and harvest based on climate forecasts.

• Improving infrastructure planning – AI analyzes climate patterns to help governments and businesses develop infrastructure that is more resilient to extreme conditions.

Example: ClimateAI

ClimateAI develops predictive climate models for farmers and businesses using machine learning to analyze temperature and weather trends. This allows for better water resource management and agricultural optimization.

Why is AI crucial for this process?

Climate change is already having massive economic consequences—global losses from natural disasters exceeded $313 billion in 2022, according to Swiss Re Institute. If investments in sustainable development are not managed effectively, these losses will continue to rise. AI provides critical data that helps businesses and governments plan for a more sustainable future, reducing financial risks and economic collapses.

3. Optimization of Mobile Payments

Mobile payments are a fundamental tool for financial inclusion, especially in emerging markets where traditional banking infrastructure is often limited or entirely absent. In many regions worldwide, smartphone access exceeds access to banking services—according to the World Bank, over 1.7 billion people do not have a bank account, yet more than half of them own a mobile phone. This means mobile payments can play a crucial role in providing access to financial services for populations excluded from traditional banking systems.

Why Are Mobile Payments So Important?

In emerging markets, traditional banking is often inaccessible due to several key reasons:

• Geographical isolation: Rural areas often lack bank branches or have them located too far away.

• High banking fees: Conventional banks frequently impose fees that low-income individuals cannot afford.

• Lack of official documents: In many countries, citizens do not have the necessary documentation to open a bank account.

• Distrust in financial institutions: In some nations, there is low trust in traditional banks due to corruption, inefficiency, or lack of transparency.

All these factors make mobile payments not just an alternative but a necessity for millions of people. They allow users to send and receive money, pay bills, and even save and invest—all without requiring a traditional bank account.

How Does AI Improve the Security, Efficiency, and Accessibility of Mobile Payments?

AI plays a central role in enhancing the security and efficiency of mobile payments by:

• Fraud Prevention: AI algorithms analyze transactions in real time and identify suspicious patterns, blocking potentially fraudulent payments. For example, if a mobile user suddenly starts making large international transfers, the AI system may require additional authentication.

• Transaction Optimization: AI improves the speed and efficiency of mobile payments by predicting network congestion and automatically allocating resources.

• Personalized Financial Advice: AI-powered virtual assistants provide guidance on better expense management and automatically set aside small amounts for savings.

Example: M-Pesa in Africa

One of the most successful examples of AI-driven mobile payments is M-Pesa, launched in Kenya and now widespread across Africa. Through AI analysis of financial behavior, the system provides microloans and automatically identifies the best transaction conditions. As a result, over 70% of Kenya’s adult population uses mobile payments, significantly improving financial literacy and stability.

4. Combating Financial Fraud and Cybersecurity

In emerging markets, the lack of regulatory control and digital security makes the financial system particularly vulnerable to fraud and illegal financial transactions. The reasons for this vary—weak legal frameworks, lack of technological resources, corruption in some countries, and limited institutional capacity to enforce effective financial oversight. Additionally, low financial literacy among a significant portion of the population makes it easier for fraudsters to manipulate consumers through various schemes, including fake investment offers, illegal money transfers, and identity fraud.

The absence of oversight creates an environment where banks and financial institutions struggle to identify risks, while investors and consumers fall victim to financial crimes. Money laundering and corruption also become more prevalent, hindering sustainable economic development and limiting the attraction of international investments. This makes combating financial fraud a key factor in building stable financial systems.

How Does AI Solve the Problem?

AI is a powerful tool in fraud detection because it can analyze vast amounts of transactional data in real time, identifying anomalies that would go unnoticed by traditional systems. Using machine learning algorithms and natural language processing (NLP), AI can flag suspicious transactions based on unusual user behavior, geolocation, transaction timing, and payment types.

For instance, AI-powered systems by MasterCard and Visa detect suspicious transactions by analyzing spending patterns. If AI detects that a card used in an emerging market suddenly makes large international purchases or frequent cash withdrawals from suspicious locations, the system automatically blocks the transaction or requires additional verification.

Example: Trust Stamp

An example of successful AI implementation in emerging markets is the startup Trust Stamp, operating in Africa and Asia. It uses AI for biometric identification and behavioral analysis to prevent fraud in mobile payments. The system creates unique digital “profiles” for each user based on their payment habits, and if someone attempts fraud by impersonating another user, AI detects discrepancies and blocks the transaction attempt.

How Does AI Improve Business?

AI-powered cybersecurity solutions significantly reduce fraud losses while increasing trust in financial institutions. According to a report by Accenture, the implementation of AI-based fraud detection systems has led to a 40% reduction in financial losses in emerging economies. This encourages banks and fintech companies to invest more in security and expand financial access without fearing substantial risks.

Moreover, AI optimizes banks' operational efficiency by reducing false positives in fraud detection by up to 25%, according to PwC data. This means banks can focus on real threats instead of investigating innocent transactions, saving time and resources.

5. Accelerating Sustainable Investments through AI Analysis

Environmental, Social, and Governance (ESG) investments in emerging markets are increasing but still face numerous challenges. The lack of reliable and standardized data to assess the impact of investments on sustainable development makes the decision-making process difficult and often subjective. In many of these economies, corporate accountability is limited, regulatory oversight is weak, and the information provided by companies is often incomplete or misleading. This creates obstacles for investors in identifying truly sustainable companies and projects, reducing capital flow into environmentally and socially responsible initiatives.

AI is a powerful tool to overcome these challenges. Big data processing and machine learning technologies enable automated and objective assessment of ESG indicators across different companies and investment projects. By analyzing satellite imagery, social media, regulatory documents, and news sources, AI can provide an impartial evaluation of companies' environmental and social commitments. For example, if a company claims to have a minimal carbon footprint, AI can verify this by comparing the claim with real satellite images of its factories or industrial zones.

How Does AI Make ESG Investments More Reliable and Transparent?

• Detecting “Greenwashing” – Many companies exaggerate their sustainability initiatives to attract ESG investors. AI uncovers these discrepancies by comparing reported ESG commitments with actual results.

• Automated Data Collection – Instead of relying on voluntarily submitted corporate reports, AI analyzes external and independent sources such as government reports, environmental studies, and corporate ratings.

• Forecasting Long-Term ESG Effects – AI models can predict how well a company will perform in sustainability over time by analyzing its historical ESG performance and industry trends.

Example: Clarity AI

Clarity AI, one of the leading AI-powered ESG platforms, uses machine learning to analyze over 30,000 companies in real time. The platform provides ESG ratings based on objective criteria, allowing investors to filter out companies with high environmental or social risks. This significantly reduces information asymmetry and facilitates sustainable investment decisions.

Why Is AI So Important?

Without reliable ESG data, financial resources do not reach projects that genuinely contribute to sustainable development. AI not only enhances the accuracy of analysis but also increases transparency by directing capital toward the most effective and sustainable investments. According to Bloomberg, global ESG assets are expected to reach $53 trillion by 2025, and AI will be a key tool for managing and distributing these investments.

6. Facilitating Access to Renewable Energy

Renewable energy is crucial for economic development and sustainability in emerging markets. However, electrification remains a major challenge in these regions, with millions of people still lacking reliable access to energy. The reasons for this are numerous:

• Limited infrastructure – The absence of power transmission networks in rural areas hinders energy distribution.

• High installation costs – Traditional energy solutions are expensive to build and maintain.

• Unstable energy demand – Energy consumption varies significantly in some regions, making grid management challenging.

AI is used to analyze and optimize the deployment of renewable energy sources, improving their efficiency and accessibility. By predicting energy needs, managing resources, and optimizing power grids, AI makes renewable energy sources more sustainable and economically viable.

How Does AI Support Renewable Energy Development?

• Forecasting Energy Demand – AI analyzes consumption patterns and climate data to predict energy needs across different regions.

• Optimizing Solar and Wind Farms – AI identifies the most suitable locations for installing renewable energy sources, considering climate conditions and infrastructure.

• Automated Energy Grid Management – AI algorithms balance energy supply and demand in real time, reducing losses and improving efficiency.

Example: Off-Grid Solar in Africa

In many parts of Africa, where traditional electricity grids are underdeveloped, AI-powered solar systems like Bboxx and Zola Electric provide sustainable energy to millions of households. By using AI algorithms to forecast consumption and remotely manage solar panels, these companies have reduced maintenance costs and made energy more accessible.

Why Is AI So Important?

According to the International Energy Agency (IEA), automation and AI can reduce renewable energy costs by up to 15% and increase efficiency by 20%. This means that more households and businesses can transition to renewable sources, reducing their carbon footprint and relying on more affordable and sustainable solutions.

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AI not only facilitates investments in sustainable projects but also makes them more transparent, accessible, and efficient. Emerging markets can leverage these technologies to overcome barriers to financing ESG initiatives, renewable energy, and financial inclusion. AI provides solutions that reduce investor risk, automate the analysis of sustainable projects, and improve financial accessibility for the population.

However, realizing the full potential of these solutions requires greater collaboration between technology companies, financial institutions, and governments. Open data policies, regulatory frameworks that encourage ESG investments, and the development of technological infrastructure in these regions will be key to AI’s success in sustainable finance.

The prospects for AI in emerging markets are enormous. As more advanced algorithms, automated analytics, and intelligent sustainability solutions emerge, opportunities for innovation in sustainable finance will continue to expand. In the coming years, we can expect deeper AI integration in microcredit, resource management, and strategic planning for investments with positive environmental and social impact.

AI is already transforming sustainable finance in emerging markets and is the key to building a fairer, more sustainable, and environmentally responsible global economy. If these technologies continue to develop and be applied purposefully, they will contribute not only to economic growth but also to creating a better world where sustainable investments are accessible to all.