Improving Combined Ratio with AI for Insurance Claims
How Claims-Processing AI Helps Insurers Reduce Leakage to Improve Combined Ratio
An insurer's margins depend on their combined ratio. It's the clearest measure of your core insurance operation's performance.
Carriers know there are only two paths to increase profitability: increase premiums or reduce expenses & losses.
The former is becoming less viable, given regulatory constraints, slowing growth, and customer attrition.
However, AI for insurance claims has opened up a sizable opportunity to reduce both losses and expenses. Its diverse applications help prevent billions lost to fraud and improve operational efficiency.
In this article, we'll explore:
- What combined ratio is and how it's calculated;
- Why traditional approaches to improving combined ratio face limits;
- How AI reduces both loss ratio and expense ratio; and
- Why deterministic AI matters for sustainably improving combined ratio.
What Is Combined Ratio in Insurance
Combined ratio measures an insurer's underwriting profitability by comparing total expenses to earned premiums.
The combined ratio insurance formula is:
((Incurred Losses + Operating Expenses) ÷ Earned Premiums) × 100
A combined ratio below 100% indicates underwriting profit. A ratio above 100% means the insurer pays out more in claims and expenses than it collects in premiums, relying on investment income to remain profitable overall.
What Is a Good Combined Ratio in Insurance?
Generally, anything below 100% is profitable, with ratios in the low-to-mid 90s considered strong performance.
Industry benchmarks vary by line of business, but combined ratios consistently above 100% are a red flag. They signal operational problems that require immediate attention.
The combined ratio has two components:
- The loss ratio measures the total claims paid out, relative to premiums.
- The expense ratio tracks operational costs, including claims processing, underwriting, and administration.
Both components can be improved, but require significantly different strategies.
Improving loss ratios requires better risk selection, fraud detection, and claims management.
And improving expense ratios requires more operational efficiency and better processes.
Why Improving Combined Ratio Through Premium Increases Has Limits
Carriers facing poor combined ratios in insurance often respond by raising premiums. But this approach is increasingly unsustainable.
According to the Insurance Information Institute:
- Aggregate net premium growth across all lines reached just 5.9% recently, the lowest in over a decade.
- While overall profitability has been high, general liability continues to struggle; it recently experienced the highest quarterly direct incurred loss ratio in at least 25 years.
We can attribute the slower premium growth to several factors, but a few stand out.
1. Regulatory Constraints
Unlike most other industries, insurers can't freely change their prices. The cost of premiums, when premiums can be increased, and when changes come into effect, are heavily regulated.
2. Customer Attrition
Deloitte research shows that for many insurers, customer churn is increasing due to inflation-driven policy-rate hikes. Rising unemployment poses another threat.
3. Market Competition
In lines of business where multiple carriers compete aggressively, premium increases drive customers to lower-cost alternatives. Once you lose your policyholders, it's much more difficult to get them back.
The Bottom Line: Increasing Premiums Is Risky
The clear alternative is reducing the numerator in the combined ratio insurance formula. AI for claims processing helps cut both incurred losses and operating expenses through more efficient, accurate claims operations.
How AI Improves Combined Ratio By Reducing Loss Ratios
Fraud is the largest preventable component of incurred losses. The Coalition Against Insurance Fraud estimates fraud costs at least $308.6 billion in losses each year.
Here's how AI can change that.
1. Document Analysis Prevents Leakage at the Root
Claims leakage often stems from missed details—sometimes buried deep, sometimes just poorly documented. As case workloads grow larger and more complex, claims teams need more support to accurately process all documents.
Claims processing AI helps capture minute details from both image and text, whether handwritten notes or digital charts.
OwlVision extracts data from this wider range of sources with industry-best precision, thanks to a custom-curated LLM in document-processing technology that outperforms traditional Optical Character Recognition (OCR), ensuring your team has key information to reduce claims losses or prevent overpayments.
2. Detecting Discrepancies Helps Identify Fraud
Fraudulent claims rely on carriers missing contradictions across multiple documents:
- The claimant's statement conflicts with medical findings.
- Employment records don't align with disability claims.
- Treatment patterns seem inconsistent with documented injuries.
AI fraud detection for insurance automatically flags claims that warrant investigation well before payments are authorized. This layer of systematic analysis ensures consistent scrutiny regardless of adjuster workload or experience level.
According to Deloitte research, insurers that integrate AI and advanced analytics could save 20–40% on fraud costs. These savings directly lower your loss ratio and improve the insurance combined ratio.
3. External Data Finds Undisclosed Activities
In soft fraud cases, where a legitimate claim is being exaggerated, claimants omit information that would disqualify them. These can range from undisclosed income sources and assets to physical activities inconsistent with claimed disabilities.
Information from external sources is also critical to identifying organized fraud connections. Hard fraud can increase combined ratio significantly because, in many cases, a carrier is repeatedly targeted by the same group.
OwlEnrich accesses a vast network of public and proprietary sources to enrich claims data. It offers automatic, real-time data retrieval that can capture:
- Public personal information
- Relations, affiliates, and employers
- Online posts, mentions, and social activities
- Legal and financial records
- Relevant news and media information
- Assets, from businesses to websites
External intelligence gives your investigators a major advantage, surfacing discrepancies that can't be found in internal documents alone.
How AI Improves Combined Ratio By Reducing Expense Ratio
Your expense ratio is the second component of the combined ratio. AI reduces your operational expenses by increasing speed, accuracy, and employee satisfaction.
1. Automated Document Processing Eliminates Manual Work
Manual document review consumes significant adjuster time, creating bottlenecks that slow the entire claims process.
Insurance document automation handles the work automatically, freeing adjusters to focus on judgment-based tasks. When documents are processed instantly rather than over days, the cost per claim is lower as claims move through workflows faster.
Additionally, when adjusters, investigators, and legal teams feel engaged in more meaningful work, it improves team morale and job satisfaction.
2. Conversational Research Makes Investigation Easier
Conversational research helps your investigators find what they're looking for significantly faster. For example, instead of reading through dense medical histories to find treatment dates, investigators ask questions and receive answers with exact source references.
Reducing investigation time frees up resources, lowering your expense ratio.
3. AI Analysis Helps Standardize Your Workflow
Manual processes are naturally inconsistent. Different adjusters will apply different standards. Even predictive AI tools can vary in how they analyze risks—we can never be sure because of their "black box" nature.
However, standardized, deterministic AI ensures that every claim receives the same rigorous review, and every analysis has an explanation. The combined ratio metric improves as quality becomes consistent.
Why Deterministic AI Matters for Sustainable Combined Ratio Improvement
Many AI solutions for insurance claims promise increased efficiency, but introduce new risks through compliance violations and bias-related litigation.
Going Deeper: Predictive AI vs Deterministic AI
Regulations like SB26-189 in Colorado require insurers using AI to provide transparency and disclosure about decisions to avoid discrimination and bias when flagging risks.
Predictive AI creates compliance exposure, since it uses existing datasets and historical patterns to make decisions. These patterns can reflect past biases.
Ethical AI in insurance prevents these risks through deterministic AI analysis. It evaluates each claim individually, based on documented facts. Every insight traces back to specific documents.
These measures ensure your claims operations remain completely transparent and compliant.
Implement Human-in-the-Loop AI
Technology structures information and presents insights. Humans must make the final determinations based on judgment and regulatory knowledge.
This division of labor protects your organization by ensuring decisions remain defensible. Human feedback improves AI performance, and human oversight helps you adapt during changes.
Explainable AI Improves Combined Ratio
Decision-rationale with (explainable AI for insurance](https://owl.co/resources/explainable-ai-in-insurance] is critical for improving combined ratio sustainably. With it, claims teams cna make informed decisions from Ai insights, thanks to the AI explaining itself.
Short-term expense reductions that create compliance violations are never worth the risk. They ultimately worsen financial performance, leading to penalties, litigation costs, and reputational damage.
Claims Intelligence for Combined Ratio Improvement
Insurance leaders need to be proactive about their combined ratio. Premium growth has consistently slowed down, and unstable economic conditions can suddenly threaten revenue.
Insurance AI solutions offer an opportunity to reduce incurred losses and operational expenses, improving combined ratio long-term.
But carriers should be careful when adopting these solutions. Organizations need a comprehensive approach that can combine powerful AI, explainable outputs, and human expertise.
Claims Intelligence explains this approach through its three principles of AI:
- Accountable AI: Explainable systems with audit trails that prevent compliance violations.
- Effective AI: Accurate, fast insights that reduce both loss ratio and expense ratio.
- Ethical AI: Fairer outcomes based on documented facts rather than predictions.
Industry leaders who achieve a good combined ratio don't adopt AI solutions for the sake of it. They choose ones that empower human expertise, rather than replace it.
(Book a demo)[https://owl.co/book-demo] to see how the Claims Intelligence toolkit improves combined ratio accountably, effectively, and ethically.