A Guide to Using AI for Streamlining Microfinance Documents

Using AI and LLMs to Streamline Microfinance 

Microfinance has supported over 174 million people around the world. If you are part of this sector, you know that your clients come from diverse linguistic and cultural backgrounds. In countries like India, with 22 official languages and many regional dialects, your business must communicate in the local language. 

Every stage of the microfinance workflow involves documents. These include loan applications, disclosures, consent forms, and repayment schedules. To serve your clients well, these documents must be easy to understand and localized for each region. However, most institutions still rely on manual or semi-automated processes to create and translate these materials. This slows down operations and increases the risk of confusion or error.

As of the 2024 financial year, India’s microfinance sector served more than 78 million borrowers through 149 million loan accounts. Due to the scale that microfinance institutions and small financial banks have to handle, multilingual digitized workflows are the only way to scale.

In this blog, we will discuss how open-source language models can simplify these workflows and how to implement scalable AI-powered solutions within your product stack. We will begin with the processes currently in place, and showcase how modern AI can streamline several of the challenges MFIs face.

Current Workflows of Microfinance Systems

In most microfinance institutions (MFIs), document handling is still a largely manual process. You often deal with printed loan applications, KYC (Know Your Customer) forms, repayment plans, and consent documents. Staff members either draft these documents themselves or depend on local translators to prepare versions in regional languages. In many cases, documents are printed, filled out by hand, and then scanned or archived as PDFs.

To serve a multilingual audience, MFIs try to bridge the gap using field staff or local officers. In India alone, you may be dealing with more than 22 official languages, and each borrower may prefer to communicate in a specific regional dialect. Staff often translate and explain loan terms during meetings or through translated handouts. 

In countries across Africa and Southeast Asia, field agents play a similar role. Some institutions have started exploring options like IVR (Interactive Voice Response) and mobile translation tools, but these solutions are still in early stages and not widely adopted.

Challenges in the Existing MFI Workflows

As the microfinance sector expands, institutions find it harder to handle the operational demands of a growing and diverse client base. Below are the major challenges that they face: 

Language Barriers

Most core systems are built in one language, and then localized in 2-3 languages. This creates barriers in digital workflows and paperwork when trying to serve a new region. 

Low Financial Literacy

If borrowers lack financial literacy, they often struggle with money management and repayments. Governments, in many geographic areas, mandate investment in financial literacy for operative financial institutions. This literacy content should be disseminated in the local language of the borrower, either through literacy apps or otherwise.

Manual Data Entry

When the paperwork is in the local language, it becomes challenging for businesses to automate processes. Manual data entry through every step of loan origination slows down workflows and is error-prone. 

Operational Costs

Hiring translators or multilingual staff increases your operational expenses. If translations are inaccurate, there is also a risk of legal issues or miscommunication that could damage your reputation.

Regulatory Compliance

In many regions, laws require localized versions of contracts and disclosures. Keeping all versions updated with regulatory changes adds another layer of complexity and makes version control difficult to manage manually.

Technology Limitations

Most MFIs still lack the tools to generate multilingual content digitally or manage it efficiently. Without systems for language version control or smart templates, updates and scaling become harder over time.

Why It Was Challenging to Automate MFI Workflows In The Past

With so many challenges in managing multilingual workflows, manual documentation, and limited digital tools, it becomes clear that microfinance institutions (MFIs) need better systems. 

However, in the pre-AI era, this was challenging to achieve; technology just did not exist that could have streamlined the workflows that MFIs struggle with. 

MFI Apps in Pre-AI Era

In 2017-18, when we built mobile apps for MFIs like Swadhaar FinAccess (acquired by RBL Bank), MFIN, and others, we localized the apps in the top 5 Indian languages. 

Our approach was cutting-edge for its time, and won our client the Metlife Foundation’s prestigious award. 

We had built: 

• A money management app for borrowers to keep track of their income, expenses, and loan repayment schedule.
• An assistive app for agents to streamline their workflow on the ground.
• A platform for the MFI to track agent and borrower location, send notifications, and share financial literacy content in digital format.
• All apps had ‘voice’ mode, which allowed users to ‘hear’ the content, instead of reading it.

However, here’s what we could not automate, and saw as a gap: 

• All paperwork had to be handled in local languages
• All language content had to be manually created and translated
• The platform required extensive data entry work
• Lack of bidirectional speech-to-text and text-to-speech

Can LLMs and modern AI solve these problems?  

An Overview of Capabilities of LLMs, VLMs, Speech-to-Text and Text-to-Speech Models

In the last couple of years, AI has completely transformed the technology landscape. It is worth looking at some of the key ones and their capabilities. 

Large Language Models (LLMs)

Large language models (LLMs) are advanced machine learning systems built to handle natural language tasks. They can understand context, generate human-like responses, and work across multiple languages. Some popular LLMs include GPT-4o, Claude Sonnet 3.5 or 3.7, Gemini 2.0, Llama 3.2, Mistral, or DeepSeek R1. These models have been released by companies like OpenAI, Anthropic, Google, Meta, Mistral, Alibaba, and others. 

These LLMs are highly capable of handling language tasks, and several of them support 100+ languages. This makes them well suited for helping MFIs create, translate, and personalize documents at scale while reducing errors and saving time.

Vision Language Models (VLMs)

A subset of LLMs are models that can understand visual documents. Models like 4o by OpenAI or Claude Sonnet 3.5 by Anthropic, Gemini 2.0 by Google, Pixtral-12B by Mistral, or Llama 3.2-vision by Meta, can interpret visual documents and derive insights from them (with varying capabilities). 

Below are some examples of visual documents they can handle: 

• OCR and Document Parsing: You can use VLMs to extract structured information from hand-written loan origination documents, invoices, contracts, receipts, and more. 
• Translating Infographics and Pamphlets: You can use VLMs to interpret and translate printed infographics, brochures, or pamphlets. 
• Interpret Graphs and Charts: You can use them to interpret graphs and charts, and derive insights from them. 

Think of them as AI models capable of seeing and reasoning over visual data. 

Speech to Text 

Automatic speech recognition (ASR) or Speech to Text allows one to build apps where the user can speak instead of read and type. In the past, the capabilities of speech recognition systems were limited. 

However, models like Whisper have been trained to recognize multiple languages, and can be used to transcribe speech in real-time. 

Text to Speech

Text-to-speech models have also been advancing, with OpenAI’s Audio API offering powerful capabilities for multiple language voice synthesis. Alternatively, open source models like Bark or SpeechT5 can also be used to provide a natural language interface to users. 

How Do You Integrate an Ensemble of AI Models Into Your Workflow?

AI models need an interface for users to interact with them. For instance, OpenAI allows users to interact with models like 4o, 4.5-preview, o1, using the ChatGPT platform’s chatbot interface. 

However, when integrating AI into business workflows, you leverage these AI models in myriad ways depending on your use case. 

AI APIs

You can use a familiar REST API interface with both platform LLMs or open source LLMs (hosted on your cloud infrastructure), and power your native Android app or webapp with it. This is the most common way that businesses use to integrate AI into their workflows. 

Agentic AI

Agentic AI systems work by interpreting user queries, using LLMs or other AI models to reason over data, and then performing a range of actions. For instance, you can use them to build a chatbot that helps users understand their transactions. Or you can use them to build customer support agents that update a database of support tickets. Modern tools like MCP (Model Context Protocol) enable a developer to create agentic workflows with multiple data sources easily, bypassing the need for API integration entirely. 

You can also use a combination and mix and match models depending on your specific scenario. 

How Superteams.ai Can Help Streamline MFI Workflow 

At Superteams.ai, we have a deep understanding of how the MFI ecosystem works. Our founders have built mobile apps and platforms for the microfinance domain, that has helped MFI businesses streamline and digitize their workflows. 

If you are a small finance bank or microfinance institution, we can help you in several ways: 

• Create AI-native voice-interactive mobile apps that support multiple languages
• Streamline your workflow using a combination of frontend, backend, and AI technologies
• Train domain-specific AI models that help you generate credit scores based on documents submitted by the borrower
• Create web platforms that help you track and manage document workflows or your agent workforce
• Create agentic chatbots that automatically update your existing data based on user queries
• Parse loan documents, invoices, payment receipts, or contracts using vision language models, and streamline data entry 
• Build agentic workflows that break down siloes in your data, and streamline your data updation
• Deploy open source LLMs, vector database on your cloud infrastructure, and build AI assistants for your team

To explore possibilities, schedule a demo, or drop us an email.  

Conclusion 

AI is a transformative technology, and is set to redefine how the next decade unfolds. If your business is still heavily reliant on manual workflows, you should consider exploring AI solutions that can remove bottlenecks and prepare you for the AI future. By doing so, you would be able to serve your customers better, scale faster, and improve productivity, all of which directly translates to revenue and cost savings.

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