insurance-loss-reserving用 chainladder-python 做精算损失准备金估算:从历史理赔三角到 IBNR 准备金、 尾部参数拟合。支持再保险 / 巨灾 / 一般责任险多产品线。
Install via ClawdBot CLI:
clawdbot install tangweigang-jpg/insurance-loss-reservingGrade Limited — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Generated May 12, 2026
An actuary at a property & casualty insurer needs to calculate unpaid claim reserves using the chain-ladder method. The skill processes historical claims triangles, computes development factors, and outputs IBNR estimates for financial reporting.
A reserving analyst wants to build a development factor model to track how claims evolve over time. The skill automatically generates loss development factors (LDFs) from cumulative paid or incurred loss triangles, enabling trend analysis and reserve adequacy testing.
A compliance officer needs to produce reserve estimates that meet local solvency regulations. The skill provides a standardized chain-ladder computation with transparent LDF selections, supporting audit trails and regulatory submissions.
A portfolio manager with multiple insurance subsidiaries wants to aggregate reserve estimates across different business lines. The skill can be applied per line of business, outputting separate triangles and IBNR figures that roll up into a consolidated reserve report.
Deploy the skill as a cloud-based service for insurance companies to automate reserve calculations. Revenue comes from subscription fees per user or per calculation, with tiered pricing based on volume.
Integrate the chain-ladder capability into larger actuarial or claims management platforms as a value-added module. Monetize through licensing deals or revenue-sharing agreements with platform providers.
Offer professional services to customize the skill for specific company data formats and regulatory regimes. Revenue from consulting fees plus recurring maintenance and support contracts.
💬 Integration Tip
The skill requires a well-formed cumulative loss triangle as input; ensure your data pipeline can produce this format. Validation of triangle ordering and numeric consistency is critical for accurate LDFs.
Scored May 12, 2026
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