AI-Driven Distribution Is Changing How Fintechs Reach India's Underserved

India's financial inclusion story remains incomplete despite years of digital transformation.

According to the Reserve Bank of India's Payment Systems Report, nearly 65% of credit card penetration is concentrated in Tier I cities, leaving much of Tier II and Tier III India underserved. Credit products have historically been designed for high-income salaried professionals in metropolitan markets.

Insurance reflects a similar imbalance. Insurance penetration in India stands at 3.7%, according to the Press Information Bureau, compared with the global average of 7%. Rural India, home to nearly 65% of the country's population, accounts for only 2% of life insurance branch presence, according to a report by the Insurance Brokers Association of India (IBAI) and McKinsey & Company.

This is the gap Bengaluru-based fintech WeRize is trying to address. Rather than relying on physical branches or digital marketing, the company uses a network of freelance agents supported by AI to distribute financial products across India's smaller towns and cities.

The opportunity appears to be growing. According to Business Standard, 62% of new health insurance policies originated from Tier II, Tier III, and rural markets in FY26, up from 54% in FY22, indicating that demand outside major cities is steadily increasing.

AI Meets Human Distribution

WeRize has built what it calls a social distribution model for financial services.

"We have a presence in around 5,000 towns and cities with the help of around 20,000 freelancers," co-founder and CEO Vishal Chopra told AIM.

Many of these freelancers already work as LIC agents or in similar advisory roles. Instead of reporting through a traditional sales hierarchy, they are managed through WeRize's AI platform.

The company operates as a three-sided marketplace, connecting consumers in smaller towns with freelance distributors while partnering with banks, NBFCs, insurers, and mutual fund houses that lack the distribution reach to serve these markets directly.

Chopra says the business was built around a segment that both branch-based lenders and digital-first fintechs have largely overlooked.

Profitable Without Branches

WeRize says it has remained operationally profitable for nearly four years since formally launching in 2021, after pandemic-related delays in its initial years.

As of March 2026, the company reported an annualised gross revenue run rate of $65 million and a net revenue run rate of $32 million. It also claims a pre-tax profit run rate exceeding $7 million and a return on equity exceeding 20%. The company expects profits to grow two to three times over the next year.

Chopra attributes this to the company's asset-light operating model. "We don't have any digital marketing costs or any branch costs. We function like an antithesis to many companies in the digital payment ecosystem."

He contrasts WeRize's approach with that of large payment companies that have struggled to monetise digital payment users through lending or insurance products.

The company's target audience is what Chopra describes as India's aspirational middle class, representing roughly 20% of the population and contributing nearly 30% of GDP. Despite their income levels, many remain excluded from products such as credit cards, unsecured loans, home loans, and mutual funds.

"It's not a demand problem. It's a supply problem," he says.

Building an AI Layer for Financial Inclusion

Co-founder and Chief AI Officer Himanshu Gupta says WeRize's AI capabilities are built on a proprietary dataset collected over nearly seven years.

The company says it has accumulated more than 20 billion data points spanning 4.5 million households across 5,000 towns and cities.

"This includes banking transaction histories, credit bureau records, and third-party mobile and location data, all collected with customer consent under Reserve Bank of India guidelines," Gupta told AIM.

The data feeds machine learning models that evaluate income stability, repayment behaviour, and financial stress to create financial profiles for customers who would otherwise be classified as "thin-file" borrowers by traditional lenders.

Gupta describes large language models as the foundation of the company's customer-facing AI agents.

"The LLM is the holy grail of AI."

Rather than deploying a single AI assistant, WeRize has built specialised AI agents that perform narrowly defined tasks such as customer support, verification, database queries, and product recommendations.

"One AI agent only queries the database. Another performs PAN verification," Gupta says, comparing them to digital employees with clearly assigned responsibilities.

Humans Still Close the Sale

Despite its investment in AI, WeRize does not believe automation alone can replace human relationships in financial services.

Gupta argues that while AI can identify the right products and automate back-office workflows, conversion still depends on trust.

"The freelancers carry the trust built through personal relationships. The technology is built to support rather than replace them. These are highly customised AI agents doing very specific tasks."

The company also sees its multilingual call recordings as a growing competitive advantage.

"Initially, the structured data was our IP. Now these conversations are becoming our IP," Gupta says.

The recorded interactions, spanning eight to nine regional dialects, are increasingly being used to improve the company's AI systems.

For customers with limited or no formal credit history, WeRize supplements conventional bureau data with consent-based banking and household information.

The company's AI models analyse income stability, spending behaviour, savings patterns, employment history, and transaction data to build alternative risk profiles, allowing partner financial institutions to underwrite customers who might otherwise be excluded.

Asked about the Reserve Bank of India's proposed framework governing the use of AI in financial services, Chopra said the objective is to ensure that automated lending systems do not introduce discriminatory biases based on factors such as caste or religion.

He believes regulatory oversight will become increasingly important as AI assumes a larger role in underwriting and customer interactions.

Looking Ahead

Chopra says WeRize currently has no domestic competitor operating at a comparable scale using a similar AI-enabled social distribution model.

Both founders say their experience growing up in smaller Indian cities shaped the company's mission.

"We help our customers rise, we help our freelancers rise, and then we help India rise," Chopra says, explaining the origin of the company's name.

The company is targeting an initial public offering in late 2028 and has begun working with external advisers to strengthen its public profile ahead of a listing.

The founders acknowledge that scaling AI across India's linguistic diversity remains one of the company's biggest technical challenges. Supporting more than 20 regional languages with large language models remains expensive, but they see it as a temporary constraint rather than a long-term barrier to growth.