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Casualty ILS Reserve Trajectory Forecasting with LangChain and LangGraph

Casualty Insurance-Linked Securities involve long-tail liability dynamics requiring stateful trajectory modeling. You will build a stateful trajectory forecasting workflow using LangChain and LangGraph to estimate ultimate loss development and reserve adequacy. The workflow must predict 10-year loss development trajectories with a Root Mean Squared Error (RMSE) below 0.05.

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What you are building

Build a stateful casualty loss trajectory development agent using LangChain and LangGraph.

How work is evaluated

Evaluation

Evaluates RMSE of predicted loss development factors against actuarial ground truth curves.

Datasets

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

How submissions are scored

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

ten_year_curve_length

Validates predicted_factors contains exactly 10 elements.

Binary check

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

Dimension 2

trajectory_rmse

Root Mean Squared Error of predicted factor curve • target: 0.05 • range: 0-0.2

Binary check

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

Learning goals

  • Construct multi-step stateful graph nodes using LangGraph for loss development cycles.

  • Integrate LangChain tools for actuarial chain-ladder model calculations.

  • Predict long-tail casualty claim escalation probabilities over multi-year horizons.

Resources

Dataset notes

Dataset of 200 historical casualty loss development triangles spanning 10 development years with ground-truth ultimate loss outcomes.

Challenge setup

Evaluates RMSE of predicted loss development factors against actuarial ground truth curves.

Challenge input

JSON object with portfolio_id, historical_development_years (array), initial_paid_losses

LangChain

Assigned framework for chain and tool orchestration.

LangGraph

Required extension for stateful node-based graph execution.

Evaluated output

JSON object with predicted_ultimate_loss, predicted_factors (array of 10 floats), reserve_adequacy_ratio

Evaluation checks
  • Validates predicted_factors contains exactly 10 elements.
  • Root Mean Squared Error of predicted factor curve • target: 0.05 • range: 0-0.2
Configured evaluation evidence
  • Benchmark: Actuarial Time-Series Trajectory Benchmark
  • Trajectory Rmse target: 0.05
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • LangChain · Required
  • Langchain · Optional
  • LocalAI · Optional
Orchestration
  • LangChain · Required
  • Langchain · Optional

Evaluation contract

  • ten_year_curve_length · Weight 1
  • trajectory_rmse · Weight 1

Metrics

  • trajectory_rmse · Target 0.05

Benchmarks

  • Actuarial Time-Series Trajectory Benchmark
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON object with portfolio_id, historical_development_years (array), initial_paid_losses

    Output: JSON object with predicted_ultimate_loss, predicted_factors (array of 10 floats), reserve_adequacy_ratio

    Execution harness: Python

Evaluation tools

  • LangChain · Agent Framework
  • LangGraph · Stateful Workflow Orchestrator

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Casualty ILS Reserve Trajectory Forecasting with LangChain and LangGraph