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Challenge

Industry Loss Warranty (ILW) Index Trigger Calculation & Estimation Engine

Industry Loss Warranties (ILWs) pay out when total industry losses exceed pre-defined index thresholds. Build an XGBoost and Monte Carlo simulation pipeline that forecasts industry catastrophe loss totals and calculates ILW trigger probability during active storm seasons.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Develop an XGBoost and Monte Carlo prediction pipeline to estimate industry catastrophe loss triggers for ILW contracts.

Delivery guide

How work is evaluated

Evaluation

Evaluates accuracy of final industry loss estimations and ILW trigger probability calibration.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

prob_validity

Verifies probability value is between 0 and 1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

loss_estimate_mape

Mean Absolute Percentage Error of industry loss prediction • target: 0.12 • range: 0-0.5

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Train XGBoost regressors on earlyPCS storm reporting indices

  • Execute Monte Carlo simulations for industry aggregate losses across Atlantic hurricane seasons

  • Determine contract payout probability for specific ILW trigger bounds (e.g. $50B PCS index)

  • Evaluate forecast uncertainty and confidence intervals

Resources and assets

Reference links and supporting material

Dataset notes

Historical catastrophe storm bulletins and final industry loss reporting index dataset.

Challenge setup

Evaluates accuracy of final industry loss estimations and ILW trigger probability calibration.

Challenge input

JSON containing storm_central_pressure_mb, landfall_state, and initial_pcs_bulletin.

XGBoost

Loss development regression modeling

Evaluated output

JSON with predicted_final_industry_loss, trigger_50b_probability, and recommended_hedge.

Evaluation checks
  • Verifies probability value is between 0 and 1
  • Mean Absolute Percentage Error of industry loss prediction • target: 0.12 • range: 0-0.5
Configured evaluation evidence
  • Benchmark: ILWForecastBench
  • Loss Estimate Mape target: 0.12
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Action Space
  • llama.cpp · Optional
  • Replicate · Optional
Policy Serving
  • Mistral Saba · Required

Evaluation contract

  • prob_validity · Weight 1
  • loss_estimate_mape · Weight 1

Metrics

  • loss_estimate_mape · Target 0.12

Benchmarks

  • ILWForecastBench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing storm_central_pressure_mb, landfall_state, and initial_pcs_bulletin.

    Output: JSON with predicted_final_industry_loss, trigger_50b_probability, and recommended_hedge.

    Execution harness: Python

Evaluation tools

  • XGBoost · Machine Learning Framework

Recipe freeze marker

No recipe freeze time is recorded.

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