Will the future of microfinance be powered by generative AI?

In the era of digital transformation, financial institutions of all sizes are embracing cutting-edge technologies to stay competitive and offer better services to their customers. Microfinance institutions (MFIs), a global phenomenon of non-banking financial services companies, began with a simple experiment in Bangladesh by a Banker—Muhammad Yunus—by lending $27 to a group 42 women to make bamboo products. The business eventually scaled to help bring the women out of poverty. Yunus went on to win a Nobel Peace Prize and sparked the microfinance movement.

Today, this sector is expected to grow by 14% each year. MFIs’ consistent performance is attributed to their ability to build close relationships with their clients and their use of group lending models.

This unique financial services segment, catering to the financial needs of underserved segments of the population, now faces stiff competition from large cooperative and private sector banks as they make inroads to the semi-urban sector. MFIs have been laggards in adoption of automation and other advanced technologies, leaving them with more ground to cover.

MFIs broadly pursue three main goals:

1. Remain profitable while delivering social impact

2. Prevent risk while trying to achieve financially inclusion

3. Lower the cost of operations while providing quality service

To meet these goals, they must explore new technologies to match or exceed the quality of service offered by big banks and institutions. Generative AI is a subset of AI that focuses on creating content—such as text, images, or even financial models—based on patterns and data it has learned.

In this blog, we’ll explore the possibilities of generative AI within MFIs, and how this technology can help them achieve their key objectives.

Objective 1 – Profitability

GenAI offers several opportunities to enhance profitability in MFIs while delivering social impact. One such area is personalized financial planning for the underserved. Approximately 65% of the microfinance clients are in rural areas, hence MFIs have a huge responsibility to impact the lives of people who have limited access to formal banking. By analyzing financial data and the goals of this demographic, AI can generate custom packages, savings plans, and debt repayment schedules that are more inclusive and tailored to the needs of underbanked groups.

GenAI can also aid in better targeting of the rural and semi-urban markets. GenAI data analysis capabilities can help identify high-potential demographics and improve regional forecasts related to underbanked populations. For MFIs, following the 80-20 rule—allocating resources to the 20% of channels that contribute 80% of revenue—is crucial to maximize growth and increase social impact. For example, women represented 56% of microfinance borrowers globally in 2023. This poses a huge opportunity for MFIs to craft their products and campaigns around.

Objective 2 – Risk prevention

The total global loan portfolio of MFIs is estimated to be over $183 billion, growing year over year (YoY)—the median portfolio at risk remained stable 4.6%. One of the biggest challenges in the semi-urban and rural markets is the high default rate on loans. GenAI can reduce this risk and improve credit scoring process by analyzing historical data on customer creditworthiness, spending habits, and financial history. This enables MFIs to reduce the risk of default borrowings and thus offer loans to more underserved groups.

GenAI can be valuable in building robust fraud detection systems for MFIs. These companies often face data breaches and fraud due to the low-cost applications and have a high-risk user segment. As per reports, there were over 173 million microfinance borrowers worldwide in 2022, "marking an average increase of 5% growth."

As MFIs continue to expand their services, particularly in developing regions, they can use GenAI with fraud detection systems to analyze transaction data in real time, identifying anomalies and suspicious activities. Additionally, GenAI models can simulate various fraud scenarios, including synthetic data and unknown patterns, giving it an edge over traditional techniques.

Objective 3 – Lower operational costs

MFIs often struggle with high operational costs due to reliance on more field operations (which is labor intensive) and inability of their target audience to adopt digital services. Combining GenAI and automation enables MFIs to automate back-office functions such as loan processing, document verification, and compliance checks, significantly reducing operational costs and processing times. GenAI can effectively process and understand unstructured documents, thus reducing the manual cost of processing.

GenAI can also help MFIs fully explore the potential of vernacular language customer service to reduce workload on human agents. With advancements in large language models (LLMs), the vernacular languages spoken in rural areas can be more effectively catered to, enhancing the customer experience. GenAI-driven chatbots can communicate in local languages and provide 24/7 customer support.

GenAI can also generate local language tutorials, tips, and guides on how to use mobile banking services for processes such as managing loans, opening accounts, etc. to promote self-service options.

Challenges and considerations

While GenAI offers numerous possibilities, there are important considerations. External LLMs hold great promise, but adoption has been slow due to challenges related to security, data retention, and the risk of AI “hallucinations.” An enterprise-grade, AI-powered automation platform like the UiPath Business Automation Platform™ can provide a secure gateway to these LLMs. The UiPath Platform does this via the UiPath AI Trust Layer, context grounding, and retrieval-augmented generation (RAG) capabilities.

MFIs also need to address these challenges when implementing GenAI:

1. Data privacy: handling customer data with care is crucial to maintaining trust. When using Gen AI tools, financial institutions must implement strong data security, and privacy measures.

2. Implementation costs: developing and implementing GenAI systems can be costly. MFIs must carefully assess the ROI and ensure that the benefits outweigh the expenses.

3. Ethical concerns: AI decision-making processes should be transparent and unbiased. MFIs need to ensure that their AI systems don’t discriminate against any population segments or individuals.

4. Regulatory compliance: MFIs must remain compliant when using GenAI to make decisions or to communicate with customers. Here, proper monitoring and oversight play a key role.

Conclusion

GenAI holds great promise for MFIs. As the sector grows, using GenAI and automation will have a direct impact on bringing more people into the formal financial system through its vast cognitive capabilities.

From personalized services and enhanced risk assessment to operational efficiency and fraud prevention, GenAI can become a key growth enabler.

However, to fully harness the power of GenAI, MFIs must navigate challenges related to data privacy, costs, ethics, and compliance. MFIs will need thoughtful strategies and responsible policies by organizations at the Board and C-suite levels.

Want to learn more? The UiPath AI Summit is now available to watch on demand.