What you need
Data sources
- Claims management system — Historical claims with final payout amounts, claim type, severity, region, and resolution details
- Policy administration system — Policy limits and deductible amounts for reserve cap calculations
- General ledger — Current reserve postings for reconciliation
Knowledge spaces
- Reserving guidelines — Upload your actuarial reserving methodology, including adjustment factors and minimum reserve rules
- Claims benchmarking data — Industry benchmark payout data by claim type, if available
| Component | Name | Definition |
|---|---|---|
| Object | Claim | Maps to the claims management system. Represents a claim with type, severity, region, and current reserve amount |
| Object | Historical Payout | Maps to closed claims with final payout data. Used as the basis for comparable analysis |
| Metric | Estimated Reserve | Statistical estimate of expected payout based on comparable historical claims |
| Metric | Average Payout | Mean final payout for closed claims matching a given type, severity, and region |
| Dimension | Severity | Classifies claims by loss magnitude: minor, moderate, significant, severe, catastrophic |
| Dimension | Region | Geographic area where the loss occurred, which affects cost benchmarks |
Agent setup
Create the agent
Go to Agent Space > New agent.
| Field | Value |
|---|---|
| Name | Reserve Estimation Agent |
| Role | Claims reserve analyst |
| Goal | Provide data-driven reserve estimates for new claims based on historical comparables |
Set the description
You estimate claim reserves using historical data. When asked to estimate a reserve, always start by identifying the claim type, severity, and region, then pull comparable closed claims. Present the reserve as a range (low, expected, high) with the number of comparable claims used. Explain which adjustment factors you applied and why. Never present a single point estimate without a confidence range. If fewer than 10 comparable claims are available, flag this as low confidence and recommend manual review. Use precise dollar amounts and always note the policy limit as a cap.
Scope data access
Grant access to:
- Claims management system data source (current and historical claims)
- Policy administration system data source (policy limits and deductibles)
- Reserving guidelines knowledge space
- Claim and Historical Payout objects in the semantic layer
Add skills
Calculate claim reserve estimate
Calculate claim reserve estimate
Trigger: User requests a reserve estimate or a new claim is classified
- Identify the claim type, severity, and region from the intake summary or user request.
- Pull historical closed claims matching the same type, severity, and region from the last 3 years.
- Calculate the median and mean final payout for the comparable set.
- Adjust for claim-specific factors: documentation quality, liability clarity, injury severity, and claimant history.
- Apply the adjustment factors from the reserving guidelines knowledge space.
- Cap the estimate at the policy limit minus the deductible.
- Present the reserve estimate as a range: low (25th percentile), expected (median), and high (75th percentile) with the number of comparable claims used.
Compare reserve to actuals
Compare reserve to actuals
Trigger: User asks how accurate past reserves were or wants to validate an estimate
- Pull closed claims from the specified period with their initial reserve and final payout.
- Calculate the reserve accuracy ratio: final payout divided by initial reserve.
- Group by claim type and severity to show where reserves tend to be most and least accurate.
- Identify systematic biases (consistent over- or under-reserving) by category.
- Present a summary table with claim type, average initial reserve, average final payout, accuracy ratio, and bias direction.
Automation
Playbook: Reserve estimate on new claim
Set the trigger
Set the playbook to trigger when a new claim is classified and assigned in the claims management system (after the intake pipeline completes).
Build the workflow
The playbook pulls comparable claims, runs a statistical reserve calculation, and posts the estimate to the claim record.
- Query step — Pull the new claim’s type, severity, region, and policy details.
- Query step — Pull historical closed claims matching the same type, severity, and region from the last 3 years.
- Python code step — Calculate the reserve estimate: compute the median, 25th percentile, and 75th percentile of historical payouts. Apply adjustment factors for documentation quality and liability clarity. Cap at the policy limit.
- Condition step — If fewer than 10 comparable claims were found, flag the estimate as low confidence and add a note for manual review.
- Action step — Post the reserve estimate (low, expected, high) to the claim record in the claims management system.
The Python code step uses a Python code block to compute percentile-based reserve estimates and apply adjustment multipliers from your reserving guidelines. You can customize the adjustment factors, the comparable claim window (default 3 years), and the confidence threshold (default 10 comparable claims).
Configure delivery
Post the reserve estimate directly to the claim record. Send a notification to the assigned adjuster with the estimate range and confidence level. For low-confidence estimates, also notify the reserving supervisor.
What’s next
Fraud pattern detection
Score claims against known fraud indicators and flag suspicious submissions.
All insurance use cases
See the full list.