Setting the right premium price is central to profitability and growth for insurers. Get an inside look at how carriers transform rates into strategic competitive weapons using the latest data and artificial intelligence capabilities.
Why Insurance Pricing Matters
Premiums represent the exchange of risk for cash between carrier and consumer. More than half of customers rank best price as the #1 purchase factor based on Accenture research.
Meanwhile we know only the top quintile of insurers manage to achieve outsized profits according to McKinsey analysis. The remaining 80% of carriers cluster around the cost of capital.
Why such extreme spread in financial performance? Suboptimal pricing equals lost profits from misjudging risk exposure. But strong data-based pricing conveys competitive advantages like:
- Risk Pool Optimization – Match premiums precisely to projected loss levels
- Market Share Wins – Low rates capture customers from high-priced rivals
- Loss Ratio Management – Balance premium volume with claims payouts
Getting pricing right is no longer just about covering costs plus fair profit. Innovative carriers now unleash pricing as a strategic weapon to defeat competitors.
The Evolution of Pricing Science
Insurance pricing has evolved rapidly from primitive segmentation approaches as data and analytics maturity accelerated.
The Actuarial Era
Traditional underwriting categorized policy applicants into broad classes by attributes like age and geography. Premiums were based on average losses plus expenses and profit margin across groups.
This generalized approach failed to reflect many modern real-world complexities. Individual differences within classes weren‘t accounted for.
The Age of Big Data
Today insurance pricing leverages exponential data growth in areas like:
- Granular policyholder data and behaviours
- Sophisticated fraud intelligence
- IoT smart home and auto telemetry
- Hyperlocal weather and risk modelling
Advanced machine learning algorithms combine this data deluge to estimate risks and losses on an individualized basis. This more accurate risk-based pricing minimizes subsidies between applicants, and ensures premiums match projected claims.
Key Pillars of Modern Insurance Pricing
Many inputs shape optimal pricing strategy:
Precise Risk Assessment
The starting point for rate decisions is predictive analytics to estimate future claim levels based on historical loss data.
Accurately quantifying risk exposure directly impacts pricing power. Insurtechs tout black box machine learning for automated underwriting. However most large insurers prefer more interpretable models like SHAP that reveal how variables directly influence risk predictions.
This transparency ensures fairness and guards against inadvertent model bias. Still, algorithms assess individual risk levels far better than old group averaging approaches.
Key model options include:
- Generalized Linear Models (GLM) – Extension of linear regression for non-normal data
- Regression Trees – Make yes/no splits by variables
- Random Forest – Ensemble of de-correlated trees avoids overfitting
- Neural Networks – Model complex nonlinear variable relationships
No single best approach exists. Hybrid ensembles combine multiple models to improve accuracy.
Expense Efficiency
Operations and technology costs eat into any carrier‘s revenue base. Major expense levers include:
| Expense Category | Cost Management Tactics |
|---|---|
| Claims | Fraud analytics, automated adjudication |
| Customer Service | Chatbots, multi-channel engagement |
| IT Infrastructure | Cloud platforms, process automation |
| Sales and Marketing | Targeted digital advertising |
Keeping expenses low allows charging reduced premiums to win market share while remaining profitable.
Competitive Response
Rate decisions happen in the context of a dynamic rival landscape. Leaders constantly track competitors using data services for visibility into:
- Pricing trends
- New product introductions
- Marketing campaign response
Real-time intelligence combined with advanced analytics empowers agile reactions to ever-shifting market conditions:
- Rapidly match competitor price drops
- Identify disruption opportunities against complacent incumbents
- Launch optimized micro-targeted promotions
Balance competing priorities of growth vs profitability based on competitive intelligence.
Cutting Edge Pricing Science
Pricing innovation continues advancing rapidly as insurtech data science matures. We highlight key developments:
Dynamic Risk Monitoring
Insurtechs are introducing flexible premiums that adjust based on changing policyholder risk profiles thanks to data streams from:
- Auto telematics
- Smart home sensors
- IoT health wearables
This allows continuous underwriting aligned to usage patterns and emerging loss indicators.
Adoption remains low currently given implementation costs. But as connected sensors proliferate across society, flexible premiums will unlock pricing advantage.
Parametric Modeling
Rather than actual losses, parametric coverage pays based on measurable triggers like rainfall levels or wind speeds. Enabled by weather data services, rates derive directly from probability distributions of events hitting parameter thresholds.
Fast automated payouts after catastrophes provide consumer appeal. However basis risk where triggers fail to match actual experience could constrain large-scale adoption.
The Climate Change Wildcard
Climate change drives pricing uncertainty as once-stable actuarial models break down. Coastal loss trends diverge sharply from inland areas. Whole counties become uninsurable risks regardless of adaptation investments.
In response, insurers refine geography-specific indicators and partner more closely with meteorological data providers. Still, extreme "black swan" natural catastrophes threaten unexpected volatility.
Core Infrastructure for Advanced Analytics
Extracting pricing insight from big data requires high performance infrastructure very different from traditional insurance IT stacks.
| Legacy Systems | Modern Cloud Platforms |
|---|---|
| Fixed rigid architecture | Flexible scalability to meet surges |
| Waterfall software development | Agile sprints accelerate use case testing |
| Isolated siloed data | Unified data lake enables end-to-end analytics |
Instead of enterprise software or on-premise servers, the cloud now delivers easily configurable managed services purpose-built for data science.
Combining model factory assembly lines with horizontally scaled compute/storage improves development velocity. This allows moving models rapidly from notebooks to large-scale production deployment.
Key Barriers Constraining Pricing Innovation
While insurtech analytics unlocks new pricing capabilities, overcoming lingering data-driven transformation barriers remains key.
Regulatory Rule Complexity
Insurers face extensive restrictions around usage of certain data signals and risk factors to avoid unfair discrimination. Keeping black box models compliant through explainability adds overhead during development.
But regulators also balance innovation against protection. Recent U.S. Treasury reports endorse data transparency over excessive AI prescriptiveness to encourage progress.
Legacy Technology Debt
Mainframe systems still prominent at large insurers can‘t ingest or process the enormous data volumes behind modern pricing science. This forces patchwork integration solutions delaying analytics velocity.
Architectural legacy debt takes years to unravel via ongoing renewal programs towards cloud-centricity.
Organizational Silos
Sophisticated modeling requires seamless data sharing and coordination between pricing, underwriting, claims, and product teams. But rigid hierarchies coupled with departmental data ownership attitudes often stall collaboration.
Only leadership commitment towards tearing down walls to nurture a data-driven culture unleashes full benefits.
Seizing the Pricing High Ground
Pricing sits at the crossroads of growth, profitability, and competitiveness for every insurance carrier. While agile insurtech analytics teams embrace state-of-the-art techniques, many incumbents remain anchored to legacy approaches losing relevance.
Turning pricing into a strategic weapon requires both technology modernization and talent savvy with translating data into decisions. Teams skilled in balancing modeling sophistication and business needs are scarce.
The choice for carriers boils down to disrupt or be disrupted. Insurtech attackers stand ready to unleash cutting edge price optimization poised to unravel mainstream markets. Incumbents must re-orient around cloud platforms, automated modelling, and continuous competitive response to defend their customer bases.
Companies that transform pricing capabilities fastest will define the future of insurance leadership.
