Targeted claims handling by category
When it may fit: Categories differ in needs.
Evidence needed: Category-level cost and cycle data.
Trade-off: Needs specialized teams or tools.
Insurance Intelligence Brief
How to Compare Claims Patterns Across Categories
Claims behave differently by product, cause and size. Comparing patterns across categories shows where cost is concentrated and where handling should differ.
1. The business problem
Which kinds of claims are costing us the most, and are they changing?
An average claim is a fiction. Auto glass claims, homeowners water damage, commercial liability and umbrella losses differ in size, frequency, handling needs and trends. Treating them as one population hides where the problems are.
A comparison by category shows where cost is growing and where a different handling approach might help.
2. What the evidence can reveal
A category comparison shows, for each category:
- Calculated Claims cost and its growth.
- Calculated Loss ratio against the premium it relates to.
- Calculated Frequency and severity separately, where available.
- Information gap Claim type, cause of loss, size band and handling route, which the demo does not include.
Large changes in a few categories usually explain most of a total change.
3. Investigate the problem
The synthetic insurer is analyzed by product. Calculated Personal Auto claims cost rose 23.6% to $25.1M against premium growth of 9.0%, moving its loss ratio from 65.5% to 74.3%. Homeowners claims cost rose 14.3%, Small Commercial 18.3% and Specialty & Umbrella 37.9%.
Calculated Auto contributed the largest share of the increase in dollars. Claims cost rose about $4.8 million in Personal Auto ($25.1 million against $20.3 million), $2.2 million in Homeowners, $1.5 million in Small Commercial and $1.1 million in Specialty & Umbrella, for a total of about $9.6 million. Auto alone is about half of the increase.
Hypothesis Personal Auto may reflect rising repair costs and more claims, which would be consistent with the general rise in frequency and severity. Information gap The demo does not split by cause of loss, size or handling route. A first split would be by claim size band, to see whether a few large claims or many small ones drive each product’s increase.
4. Explore the interactive experience
Investigate it yourself.
Insurance Portfolio Explorer: a portfolio dashboard with loss, expense and combined ratios, a scenario simulator for rate, volume, frequency and severity, evidence-linked answers from APIS, and a sample brief, all on synthetic data.
The experience below is built on a synthetic insurer, “Demo Mutual Insurance.” It uses no policyholder or claims-handling data of any kind, and it has no cycle-time or workflow records, so it is used to show the financial side of claims. Every figure is illustrative.
- In the product table, sort by Claims incurred, then select each line to see how much it rose. Which product has the largest increase in dollars?
- Select each product and compare the growth in its claims incurred with the growth in its earned premium.
- Ask APIS which products warrant a closer review.
5. Evaluate possible solutions
Category differences point to targeted actions.
6. What leadership should do next
Questions to answer
- Which categories drive the cost increase?
- Is it frequency or severity?
- What differs in handling?
- What can be changed?
Data to gather
- Claims by category, cause and size
- Premium by category
- Handling routes
- Vendor costs
Steps to take
- Rank categories by increase.
- Dig into the top two.
- Test a handling change.
- Track.
7. Engage Two Pillars
Investigate Performance Across Your Insurance Business
Two Pillars can help carriers, agencies and brokerages investigate portfolio, claims operations, agency economics and process improvement using your own data. This demo uses no policyholder data. A good first step is a claims category analysis that shows where cost growth is concentrated.
Sources and data notes
- The organization, its figures, and every number in this article and its interactive experience are synthetic and illustrative. They are not real data, a benchmark, or a forecast.
- Reviewed October 2026. This article describes an approach to investigation. It does not replace review by qualified professionals.