The insurance industry is getting a makeover, and it’s not just trading beige for bold, but reinventing its entire playbook. Artificial intelligence is muscling in on underwriting, swapping dusty ledgers for algorithms that predict risk with eerie precision.
Vikas Acharya, CEO of London-based InsurTech ChainThat Limited, has witnessed this upheaval first-hand.
Speaking to FinTech Global‘s Harry Slade as part of an exclusive interview, Acharya dissects the rise of AI underwriters, commenting on their transformative potential, while casting an eye on their potential pitfalls.
“Underwriting is no longer just about analysing historical loss trends — it’s about projecting forward using live data and predictive signals,” he explains.
This shift is about getting ahead of the curve, snuffing out trouble before it begins, as opposed to merely tweaking premiums.
“It’s not only about looking back at loss trends; it’s about identifying signals and patterns that help prevent losses in the first place,” he adds, sounding like a man who’s glimpsed the future.
A deluge of data
Behind these AI oracles is a deluge of data, far exceeding the usual claims-and-policy grind.
“The most effective models go beyond internal policy and claims data. They pull from external and alternative data sources — behavioural insights, IoT device data, environmental trends, connected devices, even location and credit proxies,” Acharya explains, his list spilling out like a tech manifesto.
Your smartwatch might snitch on your driving habits, or a weather sensor could flag a flood risk before you file a claim.
This data flood, he argues, is reshaping how insurers think, letting them act faster and smarter.
Human interaction
But Acharya’s quick to ground the hype, stressing that AI’s not a solo act. “We’re seeing a hybrid model emerge — and rightly so,” he says.
“Underwriting automation is most valuable when it removes friction in low-touch cases, but underwriters still play a vital role in nuanced or high-value risks, after all insurance is still a relationship business.”
Despite his earlier championing of AI, he insists humans are not obsolete in the chain. In all cases, they’re essential for the big, messy cases where instinct trumps code. “In complex or high-touch lines, underwriting remains a craft, we just give underwriters sharper tools.”
Archarya’s also blunt about the need for reskilling, not layoffs. “The focus needs to shift from fears of AI ‘replacing humans’ to the reality of ‘augmenting human capability.”
“To do this effectively, insurers must invest in robust change management, including education, reskilling, and upskilling,” he says.
Challenging the industry to improve, he remarks, “Technology adoption can only succeed if people are brought along for the journey.”
On top of this, Regulators are also sharpening their pencils, especially in the US, where states like Colorado are rolling out AI-specific rules for 2026.
“Regulatory change is happening unevenly — with states like Colorado introducing AI-specific guidelines that take effect in 2026, and others likely to follow,” Acharya noted in an AM Best TV interview.
“In the absence of a federal standard, insurers must prepare for a fragmented and evolving regulatory environment, which requires systems that are flexible, transparent, and auditable,” he says.
Navigating this patchwork, he warns, demands tech that’s as open as it is clever.
Real-world gains
Acharya points to real-world wins to show AI’s impact. Insurers are using predictive models to automate decisions, cutting quote times sharply.
“We work with insurers and MGAs who use BPAs to streamline underwriting using external data, allowing them to automate decisioning and reduce quote turnaround times dramatically,” he says.
One case sticks firmly in his memory, “For example, we are able to automate parts of the underwriting workflow based on the predictive nature of loss for a given risk unit that is being insured,” he notes.
His advice for riding this wave? Don’t dive in blind. “Start small, design for big,” Acharya states, as he calls for judiciousness amidst a swell of intrigue.
It’s about testing, learning, and scaling smart, and “the insurers who win are the ones who fail fast, measure ROI early, and scale incrementally. That’s where the real agility lies,” Acharya adds, his pragmatism cutting through tech’s usual bluster.
As our call fades, Acharya is clear on his ambition for the future, the rise of AI underwriting isn’t about machines taking over in an attempt at producing some rather predictable fictitious dystopian tale, it’s about making insurance sharper, fairer, and ready for what’s next. And he’s betting the industry’s ready to rise with it.
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