Rethinking the Loss Run: From Totals to Insight

Loss runs are foundational tools in insurance, summarizing claim activity and helping underwriters and actuaries assess a policyholder's risk. Yet traditional loss run analysis often falls short. By aggregating all claims, it obscures important trends that vary by claim size, type, and development timeline.

Sub-layer analysis addresses this gap by segmenting losses into distinct layers—revealing nuanced behavior that enables more accurate reserve estimates and better portfolio oversight.

What Is Sub-Layer Analysis in Reserving?

A loss run typically includes:

• Claim dates
• Loss types
• Paid amounts
• Case reserves

Sub-layer analysis groups these claims by meaningful dimensions such as size, coverage type, or injury category. This enables insurers to detect development patterns hidden within aggregate totals.

Three Core Methods to Decompose Loss Runs

1. Chain-Ladder by Claim Segment

The chain-ladder method extrapolates future payments using historical development factors. Applying this method across sub-layers, e.g., small vs. large claims which improves accuracy.

Steps:

• Divide claims into bands (e.g., <$50K, $50K–$250K, >$250K)
• Build individual triangles for each group
• Calculate separate development factors
• Sum segment-level projections to determine total reserves

This approach captures unique settlement behavior across different claim severities.

2. Identifying IBNR Gaps

IBNR (Incurred But Not Reported) claims represent future liabilities not yet visible in data. Sub-layer analysis uncovers exposure to delayed reporting, especially in large or complex claims.

Indicators of IBNR gaps:

• Atypical drop-off in recent claims
• Lag in expected large claim reporting
• Abnormal shifts in triangle development

Segmenting claims reveals which cohorts may be missing, and informs reserve bolstering accordingly.

3. Segmenting by Claim Severity

Different severity layers settle on distinct timelines:

Tracking these independently helps isolate changes in frequency vs. severity—and supports more dynamic reserve responsiveness.

Tracking Emerging Trends Across Claim Layers

Sub-layer analysis provides visibility into whether severity or frequency is driving loss activity. Key signals include:

• Rising small claim frequency → Operational or procedural issues
• Slow-developing large claims → Emerging litigation or medical complexity
• Jurisdictional shifts → New case law impacting large-loss valuation

Development triangles by severity layer highlight how patterns diverge, improving predictive accuracy for long-tail reserves.

Technology-Enabled Reserve Optimization

Modern reserving requires speed, scale, and insight. Manual analysis of layered data is resource-intensive. AI and automation change the equation:

AI/ML Capabilities:

• Detect latent development trends
• Cluster claims by behavioral pattern
• Predict tail behavior for large losses

Benefits:

• Continuous model updates as new data flows in
• Instant insight into reserve sufficiency by layer
• Portfolio-level reserve integrity across segments

Dynamic Reserve Monitoring in Practice

Accurate reserves require ongoing recalibration, not just initial estimation.

Best practices:

• Monitor each layer against expected development
• Identify anomalies in actual vs. expected payout
• Adjust for macro changes (inflation, legal climate)

Behavioral Reserve Risk:

• Actuarial teams often anchor to initial estimates
• Sub-layer monitoring reduces delayed adjustment bias

Tools:

• Reserve runoff reports by segment
• Automated alerts for development variance
• Quarterly IBNR revalidation by cohort

Bringing It All Together: Intelligent Reserving at Scale

Sub-layer analysis elevates reserve management by:

• Revealing loss behavior across segments
• Enhancing responsiveness to emerging trends
• Improving reserve precision and financial resilience

Strategic benefits:

• Lower risk of reserve insufficiency
• More efficient capital deployment
• Improved rating agency and regulatory confidence