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Challenge

Actuarial Climate Risk Assessment Pipeline with LangChain and Fireworks AI

Climate change poses escalating risk to long-term insurance underwriting and asset durability. Build an automated climate vulnerability scoring engine using LangChain and Fireworks AI fast inference endpoints to evaluate real estate portfolio risks. Deliver reliable actuarial risk reports at scale.

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Challenge brief

What you are building

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

Build an actuarial risk assessment tool using LangChain and Fireworks AI to quantify climate risk metrics for insurance asset portfolios.

Delivery guide

How work is evaluated

Evaluation

Evaluates portfolio climate vulnerability calculation speed and loss ratio accuracy.

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

loss_ratio_bounds

Ensures calculated annual loss ratio falls within theoretical actuarial limits.

Binary check

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

Dimension 2

processing_latency_ms

Average execution time per property profile. • target: 300 • range: 0-2000

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

  • Implement LangChain chains for combining geospatial weather projections with asset vulnerability data

  • Utilize Fireworks AI low-latency API for fast batch evaluation of property portfolios

  • Calculate actuarial expected loss ratios under IPCC climate scenario models

  • Generate automated regulatory reporting metrics for Singapore insurance regulators

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic coastal real estate dataset for Singapore commercial properties with elevation and risk exposure metrics.

Challenge setup

Evaluates portfolio climate vulnerability calculation speed and loss ratio accuracy.

Challenge input

JSON property asset detail

LangChain

Assigned framework for workflow LCEL.

Fireworks AI

Assigned high-performance inference tool.

Evaluated output

JSON climate risk profile

Evaluation checks
  • Ensures calculated annual loss ratio falls within theoretical actuarial limits.
  • Average execution time per property profile. • target: 300 • range: 0-2000
Configured evaluation evidence
  • Benchmark: ActuaryRisk-Bench
  • Processing Latency Ms target: 300
  • 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
  • LangChain · Required
  • Langchain · Optional
Policy Serving
  • Fireworks AI · Optional
Training Infra
  • Fireworks AI · Optional
Orchestration
  • LangChain · Required
  • Langchain · Optional

Evaluation contract

  • loss_ratio_bounds · Weight 1
  • processing_latency_ms · Weight 1

Metrics

  • processing_latency_ms · Target 300

Benchmarks

  • ActuaryRisk-Bench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON property asset detail

    Output: JSON climate risk profile

    Execution harness: Python

Evaluation tools

  • LangChain · Chain Framework
  • Fireworks AI · Inference Provider

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Actuarial Climate Risk Assessment Pipeline with LangChain and Fireworks AI