INSHUR is a leading digital InsurTech providing data-driven auto insurance for mobility, rideshare, and delivery drivers. Operating across the US, UK, and EU, its platform enables fast, flexible coverage at scale. INSHUR also manages claims in-house and, following its 2023 acquisition of American Business Insurance, now serves drivers in all 50 US states.

This blog, written by Adrian Bridgwater – Senior Contributor, explores the evolution of InsurTech, examines the challenges that have shaped its reputation, and highlights how companies like INSHUR are redefining the future of insurance through innovation, experience, and scalable technology.

Tech is rich in blends. The use of portmanteaus and lexical blend word-splintering is as prolific in tech as it is in show business – think Brangelina and other celebrity fusions. In the tech world, we have terminology like DevOps (developers + operations teams as a unified single entity), one of the most well-known examples.

Beyond all the “Ops” extensions (FinOps, AIOps, SecOps, etc.), there are industry-specific blends where we simply add “Tech” onto a shortened business discipline. That’s how we get:

MarTech (marketing technology)
FinTech (financials)
GovTech (government, obviously)
Even AutoTech, which could refer to automotive manufacturing – although that one might also apply to automation more generally.
And then, of course, there’s InsurTech – for the insurance industry.

How InsurTech Developed
Chris Gray, Chief Technology Officer at Inshur, an on-demand embedded insurance services company, explains why many InsurTech organizations have developed a bad name over the years.

According to Gray, for too long, InsurTechs have proclaimed technological innovations like AI-powered automatic claims payments, often promising cash to claimants in under five seconds. While these promises grabbed headlines, they also led to a sharp increase in loss ratios — a key metric that has made the wider insurance industry anxious about working with them.

NOTE: As defined by Investopedia, a loss ratio represents the ratio of losses to premiums earned. It includes paid insurance claims and adjustment expenses, calculated as:

(Insurance claims paid + adjustment expenses) / total earned premiums

Tech Without Insurance Is Just… Tech
“The issue is that InsurTechs are failing to understand the ‘insurance’ element, which is leading to policy pricing inaccuracies,” said Gray. “This is resulting in a mass exodus from reinsurance partners and, although the technology may be working wonderfully, without insurance capacity to pay out on claims, InsurTechs only have a swanky tech platform to offer.”
In the niche that Inshur operates in – commercial auto insurance for on-demand drivers in major cities – capacity issues make this a particularly challenging environment. But Inshur claims a strategic advantage: the company has access to over 40 years of loss ratio data for fleet, taxi, and delivery drivers, which helps it understand the unique demands of on-demand driver coverage. They’re now working to develop new rideshare and courier insurance products.

The Future Is On-Demand
“The future is on-demand. The way we access services like taxis – and how we purchase groceries and pizzas – has changed forever,” said Gray.
He argues that incumbent insurers must adapt by embedding insurance products into platforms used by drivers. If not, more nimble players with complementary technologies will step in to meet the growing demand.

The global on-demand economy has indeed created the most profound economic shift in four decades. According to PwC research, it’s expected to surpass $335 billion USD by 2025.

Out With the Old Breed
Inshur’s team believes the “old breed” of InsurTechs have burned too many bridges by focusing on growth at all costs. They’ve used AI-first pricing and claims handling to lure customers and capacity partners, while overlooking the fact that insurance is a financial discipline – one that requires specialist knowledge, historical data, and regulatory compliance.

In this industry, there’s a vast amount of data that must be handled with care – from personally identifiable information (PII) to health data (in claims), and financial records.

AI-Augmented, Not AI-Replaced
“Because of these sensitivities, we’ve focused on technology, data models, and claims handling to build a platform that is viable – not just for insurance, but for platform partners and drivers,” said Gray, speaking to press and analysts in London this September.
The Inshur platform uses AI and machine learning as an augmented assistant, not a full replacement for insurance expertise. It helps with:

• ID verification
• Fraud detection
• Claims triage and assistance
• The platform embeds insurance into apps so that it’s easily accessible for drivers.

“We listen to our insurance team and use our technology to benefit their workflow. For example, our claims department needed help with the volume of incoming claims and prioritization.

So, we built an AI assistant that summarizes each claim, categorizes it (e.g. vehicle damage, personal injury), and prioritizes it for the handler – based on proprietary factors like communication history and party involvement.”
The result: AI complements the team’s daily tasks, making them more effective, not redundant.

Built to Scale Globally
Inshur reminds us that scalability is crucial. A platform in this space should work globally, while still meeting local regulations and policies – whether that’s across all 50 U.S. states or international markets.

How InsurTech Actually Works
On-demand commercial auto insurance requires a complex mix of data, including:

• Location, weather, vehicle type
• How and where the vehicle is used
• Miles and hours driven
• Driver history, insurance claims
• Telemetry data (e.g. speed, safety)
• App activity used by on-demand drivers
• A good InsurTech platform uses this data to support underwriters, ensuring fair policies and reducing bias.

“Let’s take the highly regulated U.S. market,” Gray explained. “Many insurance products operate in the ‘admitted’ space, meaning state regulators must approve your pricing. This makes subjective or AI-based pricing nearly impossible.”
To address this, Inshur uses machine learning to refine data models before live deployment. For example, using Google BigQuery and AutoML, they:

• Identify pricing factors (like seasonal patterns, driver behavior, environmental data)
• Detect fraud trends and claims surges
• Feed this insight to actuarial teams for human review
• This approach removes bias and keeps decision-making grounded in experience.

Inferred Location Data & Embedded Insurance
“Gone are the days where customers fill out 100 questions to get a quote,” said Gray.
By working with embedded partners like Amazon and Uber, Inshur can collect unique, context-rich data. For example:

Amazon provides info about a driver’s block bookings and shifts.
This enables pricing based on inferred location, driving behavior, and actual usage.
Combined with claims data and customer input, this supports accurate and fair coverage.

Explainable AI, Human Decisions
Even with so much digitization and automation, Inshur emphasizes a human-first approach.

Their AI engine makes recommendations, not decisions. Trained claims handlers always make the final call. To ensure fairness, the company also uses Google Explainable AI frameworks, helping understand and validate how decisions are reached.

That’s some comforting validation for the future of InsurTech – especially as we move toward fully digital and automated services, while keeping humans at the center of key decisions.

Conclusion: InsurTech Is Assured
InsurTech isn’t going anywhere. The shift to on-demand, in-app insurance services is real and validated by companies like Inshur — who are redefining what assurance means in the modern era.

The future isn’t just about flashy AI claims – it’s about building sustainable, scalable, and human-centered platforms that deliver real value in an increasingly on-demand world.