InsurTech deals went to distribution-focused startups

The use of AI in distribution and sales is the particular focus of our Q1 report, so it is notable that 54 of the 107 Q1’2024 InsurTech deals went to distribution-focused InsurTechs. These companies raised a collective USD528.22 million in Q1’2024.

The average deal size (USD9.78 million) among this group was nearly equal to the overall average InsurTech deal size.

11 of the deals went to InsurTechs focused on embedded or white-label insurance distribution. This group included the second-largest deal of the quarter — a USD54M Series C deal to ELEMENT, a P&C-focused embedded insurer. 32 of the 54 companies are intermediaries that sell insurance. Just 5 of the 54 distribution-focused InsurTechs are AI-centered.

The use of data in AI

The single biggest issue is the quality of the data — most available data is either poor quality, can lead to biased outcomes, or is just incorrect. For example, approximately 80% of available text on the internet is voluntarily inputted/created, so the potential for error seems high.

The majority of tech investments from (re)insurers were early-stage

Q1’2024 saw 37 tech investments from (re)insurers. 54.1% of these investments were directed toward US-based companies. Early-stage investments comprised the majority (62.2%) of investments.

Investment from accelerators, incubators, business-plan competitions, and economic development entities are excluded.

As such, there are some deals that might constitute a raise in other circumstances that we do not consider in our data.

The numbers and data we do present should be considered a minimum benchmark — e.g. this quarter at least USD912.25M was raised. This is the same consistent set of metrics that we have used since our first publication in April 2017.

Role of AI in the insurance industry

The insurance industry has seen varying degrees of success with technological integration. While some efforts have achieved significant progress, others are ongoing. To date, the global investment in InsurTech totals around $55 billion. The value of this investment varies depending on perspective.

Ignoring technological advancements is not a feasible option. Despite the costs, maintaining industry relevance is crucial, and embracing technology through InsurTech is essential, not optional.

The industry continues to derive value from technology investments, and it is expected that a pivotal moment will eventually validate these efforts.

The 2023 InsurTech reports note a shift from uncontrolled growth to a new phase focusing on profitability, which should further support the industry’s technological transformation.

As analytics have always maintained, technology in isolation (from a concrete use case) is effectively meaningless.

It is a way to do something — whether to speed a process up, or reduce manual entry of something else, the fact remains that technology is a how, not a what. If we were to ask Jeff Bezos about Amazon, he would probably tell us it’s a fast, cheap, convenient way to get goods to your doorstep — not that it is cloud-based, and powered by tech.

AI is challenging this view of technology

Artificial Intelligence is challenging this view of technology. Existing commercial AIs come to life when they have been trained on a data set, or a data model, and as a result they generally have a business focus embedded into the nuts and bolts of the technology itself.

This form of AI can never really exist “in isolation” — it is born with a targeted function.

2023 felt like a pivotal year, in which AI began to be taken seriously among mainstream business audiences for the first time — not just because of its growing prevalence, but also because of the tangible results it was producing.

This has included specific use-cases in traditional (re)insurance processes — claims fraud detection, for example.

The data challenge for (re)insurers

All forms of artificial intelligence (AI) inherently carry biases from their creators, a challenge that extends even to advanced self-aware or theory of mind AIs.

This issue is particularly pronounced in self-learning AI systems, where the training data chosen by creators or users amplifies existing biases. It is crucial to scrutinize the data used to train these algorithms and their models diligently.

Without such scrutiny, AI models may become increasingly narrow, merely echoing societal biases — a challenge recognized by major players like Google.

In February 2024, Google’s CEO, Sundar Pichai, acknowledged that their AI product, Gemini, had demonstrated bias and offended users.

The expansion of AI tools raises significant ethical and regulatory issues, particularly in the insurance sector. Although it is not our role to dictate AI regulations, many insurance bodies are beginning to restrict AI’s use until there is a clearer understanding of the data training these models.

Types of AI output

Some insurers have even opted out of using AI for this reason. InsurTech companies that use AI stand to greatly assist the industry in navigating these complexities.

The term “AI” is often used loosely, leading to confusion and challenges in technology adoption and company differentiation within the industry.

…………..

FULL Report — https://beinsure.com/global-insurtech-report-role-ai/