The branch in every pocket: How Shriram Life Insurance is engineering “Insurance for All” by 2047

India’s insurance penetration problem has never really been a product problem. It has been a problem of reach, trust, and simplicity — and those are, fundamentally, technology and data architecture problems. For CIOs across financial services, Shriram Life Insurance offers a rare case study: an insurer that has used digital infrastructure not to chase the urban, digitally-native customer, but to bring protection to the ₹5–15 lakh income segment — daily wage earners, small business owners, farmers, and first-time policy buyers — who have historically been locked out of formal financial services.

The result is a business with an average ticket size of roughly ₹30,000, against an industry average of ₹90,000 — proof that the right technology stack can make financial inclusion an operational reality, not just a policy aspiration.

Casparus Kromhout, MD & CEO of Shriram Life Insurance, frames the opportunity plainly: “Insurance for All by 2047 is an ambitious goal, and technology, more than anything else, will be the enabler that gets us there.”

From paperwork to pocket-sized branches

For an industry built on physical paperwork and manual underwriting, Shriram Life’s starting point was deceptively simple: remove friction everywhere it doesn’t add value, and preserve the human touch everywhere it does.

Today, more than 95% of the company’s business is completely paperless, processed through its agent-facing app, Astra. Onboarding, documentation, and KYC — once a multi-hour, multi-branch process — now happens in minutes. As Kromhout puts it, “It’s like giving every agent a branch in their pocket.”

For CIOs, the lesson here is architectural, not cosmetic. Shriram Life didn’t bolt AI onto an analog process — it digitized the operating model first, then layered intelligence on top. “We invested early in building the right digital and data architecture foundation,” Kromhout notes. “AI today sits on top of that foundation and continues to strengthen with every interaction and every data cycle.”

AI as infrastructure, not a feature

Since 2017, Shriram Life has been building analytics capability — starting with lapse-propensity and fraud-propensity models. That early investment has compounded into 19 analytics and machine learning models now running across the business, spanning new business, digital channels, and persistency management.

The measurable outcomes:

Underwriting: AI separates high-risk from low-risk proposals early. Low-risk cases are fast-tracked with minimal manual intervention; high-risk cases are routed for deeper manual review — compressing what used to take days into a significantly faster cycle.

Renewals and persistency: Rather than treating every lapsing customer the same way, predictive models segment customers by behavior and likelihood to respond — some get a WhatsApp payment link, others an AI voice-bot nudge, others a human call. “This allows us to allocate capacity much more intelligently, improve customer experience, and improve persistency outcomes at scale,” Kromhout says.

Servicing: Through the Shri Mitra app and other digital channels, more than 80% of service requests now arrive digitally.

Claims: Claims that require no investigation are settled within 12 hours; overall, roughly 94% of claims are settled within that window.

Fraud detection: Predictive fraud models have measurably reduced leakage.

This is a single data architecture serving acquisition, underwriting, servicing, and retention simultaneously — each model getting smarter as the others generate more interaction data.

The next frontier: GenAI for agent productivity and vernacular reach

Having digitized the back end, Shriram Life is now turning generative AI toward its distribution network. The company is exploring GenAI-driven agent training and vernacular language support — a direct response to serving customers across dozens of regional languages and varying levels of financial literacy.

One example already in production: AI-generated, personalized post-purchase messages in regional languages — including a contextual message attributed to brand ambassador Rahul Dravid — sent to customers after policy purchase. “That, for us, is the evolution from using AI as a tool to building AI as a long-term strategic capability for the organization,” Kromhout says.

Designing for trust, not just for scale

Perhaps the most important insight for CIOs building AI roadmaps in regulated, trust-dependent industries is Kromhout’s repeated emphasis that technology and human relationships are not substitutes — they’re complements, deliberately engineered.

“While we’ve digitized processes, we’ve also used analytics and AI to make customer engagement more personalized,” he explains. “Insurance is ultimately a trust business.”

This shows up most clearly in how the company thinks about its core customer segment. These are often first-time buyers for whom a policy “isn’t a portfolio decision; it’s the only protection their family has,” Kromhout says. Expectations among this segment have shifted dramatically — shaped by UPI, e-commerce, and quick commerce — even as the need for human reassurance at the moment of purchase, and especially at the moment of claim, hasn’t diminished.

“Technology can reach people,” Kromhout says. “But it’s the agent sitting across from a customer, explaining what this policy means for their family, that builds the relationship. And it’s the claim being settled quickly and with dignity that cements the trust.”

He points to a vivid signal of this dynamic at the grassroots level: in a village he visited, only one person initially placed orders online for the entire community — and on a later visit, everyone was doing it themselves. The same diffusion pattern, he believes, is now playing out with insurance trust itself: “When a claim is paid — when a family receives that money at their most vulnerable moment — the entire community notices. And suddenly, ten more families in that area want to buy life insurance.”

What’s next: personalization, sharper underwriting, and open distribution

Looking five years out, Kromhout identifies three forces that will reshape the sector:

Personalization at scale — using maturing data capabilities to deliver the right product, at the right time, through the right channel for each individual customer.

Sharper AI-driven underwriting — faster decisions, more accurate pricing, and the ability to profitably underwrite segments once considered too complex or costly.

Open digital distribution — platforms like Bima Sugam extending reach into markets where traditional agent networks haven’t penetrated.

But Kromhout is careful not to let the technology narrative outrun the trust narrative: “Technology is only as powerful as the trust that sits behind it. The industry’s biggest challenge isn’t technological. It’s awareness and belief.”

The takeaway for technology leaders

Shriram Life’s experience offers a clear blueprint for companies in financial services and insurance: build the data and digital foundation early, let AI compound on top of it across the full customer lifecycle, and resist the temptation to treat human relationships as a process to be automated away.

The technology’s job is to remove friction at scale. The human’s job — agent, claims handler, or community word of mouth — is to convert that friction-free experience into trust.

For an industry whose biggest constraint has always been belief rather than bandwidth, that may be the most important architectural decision of all.