Smarter Claims Triage and Decision Support with AI

In property & casualty (P&C) insurance, every claim is unique. Some are straightforward—a windshield replacement, a minor property repair. Others are complex—liability disputes, catastrophic losses, or suspected fraud. The challenge for insurers is to triage claims efficiently, routing them to the right adjusters and resources, and then to provide decision support that ensures consistent, fair, and cost-effective outcomes.

Historically, claims triage has relied heavily on manual judgment. Adjusters review incoming files, assess severity, and decide where to route them. While experienced professionals can make accurate calls, the process is slow, inconsistent, and subject to human error. Decision support has been limited to static checklists and policy manuals.

Today, artificial intelligence is changing this landscape. By analyzing data at scale, spotting patterns invisible to humans, and providing actionable recommendations, AI can dramatically improve both triage and decision support.

Why Claims Triage Matters

Claims triage is the process of assessing incoming claims to determine:

• Severity: How complex or costly is the claim likely to be?

• Risk: Are there red flags for fraud, coverage disputes, or regulatory scrutiny?

• Routing: Should the claim be handled by fast-track automation, a general adjuster, or a specialist team?

Getting this right has outsized impact. Mis-routed claims mean wasted resources, longer cycle times, higher expenses, and greater customer dissatisfaction. Conversely, effective triage ensures simple claims are settled quickly while complex cases get the expert attention they require.

The Traditional Limitations

1. Manual review is slow and inconsistent. Different adjusters may categorize the same claim differently, leading to inconsistent outcomes.

2. Data is fragmented. Policy details, prior claims history, customer communications, and supporting evidence often sit in silos.

3. Risk signals are easy to miss. Fraud patterns, conflicting details, or missing documentation often surface late in the process.

4. Decision-making depends on individual expertise. Institutional knowledge is not easily shared across teams, leading to variability.

How AI Improves Claims Triage

AI supports triage in several powerful ways:

• Automated Document & Data Ingestion
  Using natural language processing (NLP) and computer vision, AI can instantly extract key fields from PDFs, images, and forms. This allows claims to be assessed at the point of entry, rather than waiting for manual review.

• Risk Scoring
  AI models can analyze incoming claims against historical data, flagging anomalies such as inconsistent injury reports, duplicate claims, or suspicious billing patterns. High-risk claims are routed to special investigation units (SIU), while low-risk claims flow through fast-track.

• Severity Prediction
  Machine learning models trained on prior outcomes can predict the likely cost and complexity of a claim. For example, property claims with certain characteristics (geography, weather event, property type) may be flagged as likely to exceed a threshold and routed accordingly.

• Dynamic Routing
  Based on severity and risk scores, AI automatically routes claims to the appropriate queue: automation for simple claims, standard adjusters for moderate complexity, and specialists or legal for high-complexity cases.

Decision Support: Beyond Triage

Once a claim is in the right hands, AI continues to provide decision support to adjusters:

1. Policy Interpretation
   AI tools trained on policy language can highlight relevant clauses, exclusions, and endorsements, reducing the time adjusters spend searching through documents.

2. Coverage & Liability Guidance
   By comparing the details of a claim with historical cases, AI can suggest likely outcomes and highlight potential disputes.

3. Next-Best-Action Recommendations
   Decision support models can recommend the most efficient next step—whether to request additional documentation, escalate to SIU, or proceed with settlement.

4. Settlement Benchmarking
   AI can benchmark proposed settlements against past similar cases, helping ensure consistency and reduce overpayment (“leakage”).

5. Regulatory Compliance
   Built-in compliance checks flag missing disclosures, deadlines, or required documentation to keep adjusters aligned with local regulations.

Benefits of AI-Driven Triage and Decision Support

• Lower Loss Adjusting Expenses (LAE): By automating intake and routing, insurers cut manual work and reduce time spent on simple claims.

• Faster Cycle Times: Simple claims can be settled in hours, not weeks, improving customer satisfaction.

• Reduced Leakage: Consistency and benchmarking help eliminate overpayments and errors.

• Early Fraud Detection: AI flags suspicious claims sooner, protecting insurers from unnecessary losses.

• Improved Adjuster Productivity: Adjusters focus on higher-value tasks while AI handles routine review.

• Stronger Compliance: Automated audit trails and checklists reduce regulatory risk.

Evidence from the Market

• Bain & Company estimates generative AI can cut claims handling costs by 20–25% and reduce leakage by 30–50%, largely through improvements in triage and decision support (Bain).

• McKinsey highlights Aviva’s use of 80+ AI models in claims, which improved routing accuracy by 30% and reduced liability assessment time for complex claims by 23 days (McKinsey).

• Capco notes that AI can streamline intake and claims triage, leading to measurable reductions in cycle time and operational costs (Capco).

Implementation Roadmap

1. Data Foundation: Consolidate policy, claims, and customer data into accessible systems.

2. Pilot Models: Start with narrow use cases—e.g., severity prediction or fraud scoring.

3. Human-in-the-Loop Design: Ensure adjusters can review and override AI outputs.

4. Integration with Core Systems: Connect AI outputs directly into claims management software.

5. Compliance & Governance: Build explainability, audit logs, and bias monitoring into the models.

6. Scale Gradually: Expand from pilots to enterprise-wide triage and decision support with continuous monitoring.

Challenges to Address

• Data quality and bias in historical claims data.

• Resistance from staff who may fear automation replacing judgment.

• Regulatory hurdles requiring explainable AI.

• Complex claim exceptions where human expertise remains essential.

Conclusion

AI-driven claims triage and decision support represent one of the most promising applications of artificial intelligence in P&C insurance today. By combining automation, predictive analytics, and intelligent guidance, insurers can cut costs, reduce cycle times, and improve both consistency and compliance.

At Wamy, our focus is building AI teammates that complement adjusters—automating the repetitive, surfacing the risky, and supporting better decisions. The result: smarter triage, stronger decisions, and a more efficient claims operation.