AI in FinTech - Microfinance for Financial Inclusion

The future of FinTech is here!

And it is with AI!

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What is microfinance?

Microfinance is a banking service provided to low-income individuals or groups typically excluded by conventional banking systems; for example, a street vendor who makes less than USD 5 in a developing nation cannot walk into a traditional bank and ask for a small loan of, say, USD 250 to buy a pushcart to sell their wares. Microfinancing, however, could provide small loans and other financial products to this vendor and others excluded from traditional banking systems, helping to escape the cycle of poverty, create jobs, and contribute to economic growth.  

Salient elements of microfinance:

• Small loans:  Loans start from a few hundred to a few thousand dollars.  
• Target market: Low-income individuals and small businesses.  
• Purpose: To help people start or grow businesses and improve livelihoods.  
• Impact: Promotes financial inclusion and reduces poverty.  

Examples of microfinance services:

• Microcredit: Small loans to individuals or groups.  
• Microinsurance: Insurance tailored for low-income individuals.  
• Microsavings: Savings accounts designed for low-income customers.  
• Microenterprise development: Support small businesses through training, consulting, and mentoring.  

AI in Microfinance:

The biggest challenge with microfinance institutions is to reach the typically underserved population. Some of these are low-income people living in remote areas excluded from modern conveniences to the extent that traditional banking may not even be an option. When reaching out to remote communities for microfinancing, assessing creditworthiness and keeping a lid on operational costs is a considerable challenge; this is where AI is emerging as a powerful tool to expand financial inclusion.  

AI for Credit Scoring and Risk Assessment: Where traditional information used for credit assessment, such as collateral, history, and repayment capacity information, are unavailable, AI algorithms analyse data from social media and mobile phone usage. These additional data points create more accurate credit scores, thus expanding access to finance for those excluded from traditional banking.  

AI for Operational Efficiency: AI can automate routine tasks such as loan processing, customer onboarding, and collections, reducing operational costs

Case study: Branch (Branch International | Home)

Branch is a prime example of how AI is revolutionising microfinance. By leveraging advanced algorithms, the company has developed a unique approach to credit assessment that goes beyond traditional methods.

• Smartphone Data Analysis: The Branch collects a vast amount of data from users' smartphones, including call records, app usage, location history, and device information. This data provides valuable insights into a borrower's financial behaviour, such as repayment capacity and creditworthiness assessment.
• Real-time Decision Making: The Branch's AI-powered platform can process loan applications and make credit decisions almost instantly, providing borrowers quick access to funds.
• Risk Management: By continuously monitoring borrower behaviour, the Branch can identify potential risks early on, take proactive measures to mitigate them, and educate the clients to promote financial literacy.

Impact & Implications

Branch's model has disrupted the traditional banking industry by:

• Expanding Financial Inclusion
• Improving Access to Credit
• Promoting Financial Literacy

While Branch's approach has been successful, it's essential to consider the ethical implications of using smartphone data for credit assessment. Protecting user privacy and ensuring fair lending practices are crucial for the long-term sustainability of AI-driven microfinance.

More on ethics here - AI in FinTech - Ethics in Financial Markets

What Next?

Over the next few weeks, let us explore four key areas where AI is making significant strides in promoting financial inclusion.

(1) AI for financial literacy,

(3) and AI in remittances.

(4) AI for Insurance Accessibility

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Did you find the article informative?

If you liked this, you may also find the previous articles in this series interesting. Here are the links:

1. AI in Fintech - Exploring Regulatory Landscape of AI Governance - Framework and Approaches
2. AI in Fintech - The Power of Large Language Models (LLMs)
3. AI in FinTech - Ethics in Financial Markets
4. AI in FinTech - The Future of Human-AI Collab
5. AI in FinTech - Explainable AI (XAI)
6. AI in FinTech - AI Risk Management - Part 1
7. AI in FinTech - AI Risk Management - Part 2
8. AI in FinTech - Building a Robust AI Security Framework
9. AI in FinTech - Continuous Monitoring for AI Risk Management
10. AI in FinTech - Taming the Risks, Seizing the Rewards
11. AI in FinTech - Build a Culture of AI Governance Beyond the Checkbox
12. AI in FinTech - The Future of Security to Protect Data and Systems
13. AI in FinTech - The Impact on Financial Inclusion