From price comparison to the logic behind prices: How a new generation of AI-based market intelligence can transform the insurance industry

The insurance industry is currently investing billions in digitalisation, artificial intelligence and automation. Modern pricing systems optimise existing tariffs, comparison platforms create market transparency, and AI systems help customers who are looking for information.

Despite these advances, however, one key question remains largely unresolved: How can one understand competitors’ pricing logic and market behaviour without having to access their internal data or systems?

Competitors’ prices and product logic influence margins, growth, market share and customer

loyalty on a daily basis. At the same time, they remain largely a black box for insurers, reinsurers, platforms and intermediaries.

Comparison tools show prices. However, they do not reveal why these prices are set. They do not reveal the rules, risk models, product decisions or strategic considerations behind them. They do not reveal how competitors react to market changes, how rates change over time, or how individual providers assess different customer groups.

It is precisely here that a new category of market infrastructure could currently emerge.

Why existing systems are reaching their limits

Most of the solutions available today can be broadly divided into four categories.

Comparison systems

Comparison tools provide transparency regarding current prices and products. They answer the question: “Which provider is currently cheaper?” However, they do not answer: “Why is this provider cheaper?”

Data partnerships and interface providers

Many market participants have access to APIs from insurers or aggregate market data from partnerships. This data is valuable. However, it usually only allows for the analysis of historical or aggregated market information. The underlying logic of individual providers generally remains hidden.

Benchmarking, analysis and rating solutions provide transparency regarding market positions and differences in performance. However, the actual pricing logic behind the prices remains largely invisible.

Dynamic pricing platforms

Modern pricing platforms are among the most powerful tools in the industry. They help insurers optimise their own tariffs and incorporate market data into decision-making processes.

However, the competitive landscape remains largely observation-based. The underlying mechanisms of other market participants are not reconstructed.

Generative AI and Large Language Models

Generative AI is currently one of the most impressive technologies in existence. In the coming years, large language models will be increasingly integrated into the insurance industry – as digital assistants, advisers, intermediaries or AI agents.

However, what language models do not automatically possess is independent market intelligence. A language model does not, by itself, know how a particular insurer assesses risks, what pricing logic underlies a tariff, or how competitors will react to market changes. An LLM requires knowledge. The real challenge, therefore, lies in generating this knowledge in the first place. This is precisely where the difference between language models and market models lies. LLMs can utilise market intelligence. However, they cannot replace it.

From market observation to market twins

A new generation of technologies is therefore taking a different approach. Instead of merely

collecting prices or connecting to interfaces, market models are being built that reconstruct the pricing logic and behaviour underlying observable prices.

The central idea is: Don’t observe the market – model the market.

This gives rise to what are known as market twins. Much like digital twins in industry, they aim to replicate real-world systems as accurately as possible. The difference is that it is not machines that are modelled, but markets.

This opens up new possibilities:

• Real-time predictions for millions of possible risk profiles
• Simulation of price changes
• Analysis of potential competitive reactions
• Product development based on real market mechanisms
• AI applications with a genuine understanding of the market

As a result, insurance markets are becoming machine-readable on a large scale for the first time.

The Bloomberg Question for Insurance

In the capital markets, Bloomberg fundamentally transformed trading when market information became structured, searchable, available in real time and machine-readable. A similar

development may now also become relevant for insurance markets. Not because insurance products are becoming simpler, but because new technologies are increasingly capable of modelling their complexity.

The crucial question is: What happens when market logic becomes as readily available as market prices are today?

The implications would extend far beyond traditional comparison websites or chatbot interfaces. A new operational layer of market intelligence is emerging, supporting pricing, product development, sales and AI applications equally.

New models for success in the insurance sector

The most immediate benefit of centralised market intelligence lies in pricing and product

development. Once the logic behind competitors’ prices becomes clear, insurers can identify more quickly:

• where they are over- or under-pricing,
• which market segments appear attractive,
• how competitors assess certain risks,
• and what impact product changes might

Instead of static price comparisons, a dynamic understanding of the market emerges. This opens up potential for faster product development, shorter time-to-market cycles, better margins and more informed strategic decisions.

AI agents with market insight

A second area of application concerns AI agents. Today, many AI systems answer questions based on general information. With underlying market intelligence, significantly deeper

interactions could emerge in future. For example: ‘I only drive at the weekend. Which offers suit my profile?’ or ‘Why is offer A more expensive than offer B?’

The AI would not merely describe products. It could explain market mechanisms in a way that is easy to understand. This gives rise to a new form of digital advice.

Further applications

The underlying market intelligence remains the same. Only the application changes.

Possible areas of application range from high-speed metasearch systems for insurance, through competitive reaction simulators, to sales support for field staff and brokers, product generators or market sandboxes for other sectors such as banking, energy or leasing.

Common objections

Can’t an LLM simply handle this?

No. Language models can utilise and explain existing knowledge. However, the actual market intelligence must first be generated. Market models and LLMs therefore complement rather than compete with one another.

Don’t you need insurers’ APIs for this?

Not necessarily. New modelling approaches are deliberately designed to understand market mechanisms independently of direct interfaces. This is precisely where a significant step forward in innovation lies.

Can’t you develop such an infrastructure yourself?

In principle, yes. In practice, however, this involves considerable development risk. Market

models of this kind require years of development work, extensive system and market validation, and significant investment. Consequently, such infrastructure assets rarely become available externally at all.

Conclusion

The next stage of development in the insurance industry is unlikely to lie in better price comparisons or better chatbots. Rather, it will lie in systems that understand the pricing logic and the behaviour behind the prices.

This approach gives rise to a new form of infrastructure for insurance markets – one that can support pricing, products, sales and AI applications in equal measure.

For insurers, reinsurers, broker pools, tech and software companies, banks, consultants and other market participants, this raises an increasingly strategic question: Who will control the market intelligence layer underlying AI-powered insurance systems in the future?

The answer to this question is likely to shape the industry more significantly in the coming years than the question of the next chatbot.

About finsago

finsago develops AI-based market intelligence infrastructure for insurance markets. Its technology reconstructs the pricing logic and behavior behind competitors’ prices and can be deployed as a white-box solution for strategic partners.
Learn more: finsago.com