Artificial intelligence is rapidly redefining what market intelligence looks like for property and casualty (P&C) insurance pricing teams. Historically, the information needed to understand competitor behaviour and regulatory trends was buried in lengthy filings, fragmented PDFs and inconsistent terminology. Extracting useful insights required significant manual effort and often took months.
Today, advances in AI are helping insurers turn those scattered data points into structured insights that can be analysed and used almost instantly.
With these advancements in mind, Akur8 has put together a guide highlighting five practical AI-driven use cases demonstrating how insurers can harness market intelligence more effectively.
Turning regulatory filings into structured market intelligence
One of the most immediate advantages of AI for pricing teams is the ability to extract structured insights from regulatory filings quickly.
Traditionally, actuaries had to manually review numerous documents from different carriers to understand rating approaches, variable usage or pricing sophistication. AI tools can now automatically filter filings based on specific criteria such as line of business, geographic location, time period and approval status.
Once extracted, this data can be transformed into structured tables showing factors such as rating variables or estimated rating sophistication. Pricing teams can then layer additional business metrics, including gross written premium or expense ratios, to visualise market positioning.
This approach dramatically reduces the time required to test hypotheses about the market. Instead of lengthy research cycles, teams can analyse patterns and evaluate pricing complexity within minutes, enabling faster and more informed strategic decisions.
Benchmarking factor curves with greater clarity
Factor curve benchmarking is another area where AI can deliver significant improvements.
In ratemaking, actuaries often face a common challenge: selecting a final rating factor that sits between the current value and the actuarially indicated value. To justify that selection, they must understand how similar factors are applied across the market.
Regulatory filings already contain this information, but extracting it manually can be extremely time-consuming. Terminology variations such as “package discount”, “multi-line discount”, or “companion discount” can further complicate the process.
AI systems can standardise these differences and extract the relevant factor values, curves and associated business rules. The results are presented in structured comparisons, often linked directly back to the supporting documents for transparency.
With this context, pricing teams gain a clearer picture of how discounts are applied across the market, whether they are flat or variable, multiplicative or peril-specific, and how they differ depending on bundled products.
Assessing third-party data adoption across the market
AI can also help insurers evaluate whether third-party risk scores are widely used and approved by regulators.
External data providers often offer specialised risk models for areas where insurers lack sufficient internal data. However, adopting a vendor score involves regulatory considerations as well as modelling questions.
Pricing teams need to determine which vendors are already being used by competitors, whether those scores have been approved as rating variables and which jurisdictions allow their use.
AI can scan thousands of filings to identify where specific vendor scores appear and summarise how they have been referenced in submissions. It can also highlight whether approvals are established or still under review.
This intelligence allows insurers to avoid adopting variables that could trigger prolonged regulatory scrutiny and instead focus on data sources that are both analytically valuable and regulatorily viable.
Monitoring filing changes with automated alerts
Keeping track of regulatory filings is a constant challenge for insurers. Although the information is publicly available, manually monitoring changes across multiple carriers and jurisdictions is time-consuming.
AI can automate this process by continuously tracking filings and generating alerts when relevant changes occur.
Teams can define the criteria they want to monitor, such as specific carriers, states or topics like telematics adjustments or new policy exclusions. The system then scans filings and sends automated summaries highlighting material changes.
This turns market intelligence into a proactive monitoring system rather than a periodic research exercise, enabling product, pricing and compliance teams to stay ahead of emerging trends.
Using filings as modelling offsets and priors
AI is also enabling actuaries to use industry filings as practical modelling inputs when launching new products or entering unfamiliar markets.
When insurers expand into a new state or segment, they often lack sufficient internal data to build a robust pricing model. In these situations, competitor filings can serve as useful reference points.
AI can parse and standardise the rate structures found in those filings, converting them into usable factor tables and rate components. These market-based benchmarks can then act as modelling offsets, priors or complementary signals alongside internal data.
From there, traditional actuarial methods such as credibility adjustments, offsets and penalised regression help refine the model while avoiding overfitting in early-stage data.
AI amplifies actuarial expertise
Overall, AI is transforming market intelligence from a slow, manual process into a continuous operational capability for pricing teams.
By structuring and monitoring regulatory filings at scale, insurers gain faster benchmarking insights, improved factor selection context, clearer visibility into third-party data usage and real-time alerts for market developments.
Crucially, the technology does not replace actuarial expertise. Instead, it enhances it by delivering structured insights and traceable evidence that support better modelling and more defensible pricing decisions.
Read the full blog from Akur8 here.
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