5 Ways InsurTech Can Personalize Insurance Products

So the way we personalize is less about building more products and more about recommending fewer, better ones.

At Eprezto we are a fully digital car insurance broker in Panama, so personalization happens at the moment someone compares quotes. The instinct in this business is to show as many carriers as possible. We went the other way. We cut the carriers we display from eight to between four and five, and that did two things. One, it turned us into more of a recommender than an aggregator. A customer gets a short list that actually fits, instead of a wall of options they have to decode. Two, it cut our overhead, because every carrier connects differently, with its own API quirks and its own maintenance.

The second piece is the vehicle inspection. Full coverage used to require a physical inspection, and that step blocked a lot of online sales. Now the customer takes photos with their phone, AI checks for damage and determines insurability, and a process that took days takes minutes. That is personalization too, because the decision is based on that specific car, not a generic rule.

On feedback, the clearest signal for us is not a survey, it is behavior. After we replaced the physical inspection, the heavy abandonment we used to see at that step dropped, and people decide faster when they are choosing from a shortlist.

The honest note is that we did not trust the AI on day one. We rolled it out incrementally and checked its assessments against real outcomes before relying on it.

So if you want to personalize, start by removing choices and steps, not adding them.

Insurance has evolved due to the introduction of real-time API integration and data-driven analysis, moving from a stagnant product to a dynamic service tailored for individual risk profiles. The most innovative way I have seen InsurTech achieving personalization is through automation replacing manual underwriting surveys. In our work on developing insurance platforms, we are concerned with establishing technical bridges between outdated core systems and external data systems. The underlying technology enables the platform to rely on telematics, IoT, and behavioral finance data when determining the premium instead of demographic characteristics.

This new trend shifts the focus of personalizing insurance from marketing to resolving risk issues. As the systems are implemented in practice, the feedback received from end-users is concentrated around two notions: speed and fairness. People increasingly do not like to provide the information that they are sure is already available. By applying APIs for pre-filling applications and personalizing insurance coverage within seconds, we succeed in removing problems that cause high levels of drop-off. Most importantly, when consumers recognize that their positive behavior (e.g., safe driving, timely maintenance of their property) leads to lower premiums, their confidence in the company increases.

The challenge is to make sure that personalization will not disrupt financial discipline or lead to compliance violations. From the perspective of management, the objective is to make sure that the AI and machine learning models utilized are not merely 'black boxes'. Personalization will be viable only when it reduces the loss ratio along with improving the customer experience.

I use InsurTech-driven claims analytics to tailor funding strategy and benefits to an employer’s specific risk profile. By reviewing two to three years of claims, pharmacy trends, and utilization patterns, we determine whether fully insured, level-funded, or self-funded structures make sense and identify top drivers to address. That data also informs targeted wellness programs, for example focusing on musculoskeletal or preventive care when those signals appear. Clients tell us this data-driven approach brings clarity and accountability, moving leadership from reacting to renewals to actively influencing them, and they value that we measure results quarterly and adjust when progress stalls.

Hi, we are building draudykle.lt - AI insurance aggregator. We used location, age, car value, driving experience and some more metrics to offer best insurance for customer. It's still in test mode, but we received quite good feedback. Usually customers buy cheapest insurance. We try to change this way and offer best for customer.

The most useful personalisation we've done wasn't algorithmic. It was designing products around who the insured person or business actually is. When we designed a travel insurance product for women, we started with how women actually travel and what worries them, rather than adapting a generic policy. That shaped the cover itself, and it also shaped the policy wording. Designing for a specific persona changes the language you use, from the policy wording to claims.
What's fascinating is that you don't really know how a product will be received until real people start using it. Customers bought it, asked questions and made claims in ways we hadn't expected, and we had to adjust quickly. That is where insurtechs have a real advantage over large incumbents: we can hear that feedback and change the product in weeks, not years.

In specialist lines, personalisation isn't about the end customer adjusting a policy online. It's about building a flexible product at the policy wording level. It means finding the real needs within a niche and building a product narrow enough to fit that risk properly, rather than stretching a broad product to cover it.
The hardest problem in insurtech has always been distribution, and that hasn't changed. A well-designed niche product is worthless if it's hard to place. So we design the quoting and referral experience as carefully as the policy wording.