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Challenge

Actuarial Inflation Impact & Dynamic Pricing Engine

Recent Singapore CPI developments require insurers to rapidly adjust policy premiums against inflation rate shifts. Build an automated actuarial recalculation pipeline that ingests CPI reports and adjusts insurance policy pricing models dynamically.

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Challenge brief

What you are building

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

Construct an actuarial dynamic pricing model using inflation trend analysis pipelines.

Delivery guide

How work is evaluated

Evaluation

Evaluates premium adjustments against verified statistical inflation factors.

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

math_correctness

Verifies correct calculation of updated premium

Binary check

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

Dimension 2

calculation_error_margin

Margin of error in premium output • target: 0 • range: 0-0.01

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

  • Parse MAS CPI monthly statistical report datasets

  • Calculate dynamic loss cost inflation factors

  • Output updated actuarial pricing tables automatically

Resources and assets

Reference links and supporting material

Dataset notes

Singapore CPI historical and monthly summary statistical tabular data.

Challenge setup

Evaluates premium adjustments against verified statistical inflation factors.

Challenge input

JSON containing base_premium, line_of_business, cpi_index_change

Python Pandas

Tabular statistical manipulation

Evaluated output

JSON containing updated_premium, adjustment_percentage

Evaluation checks
  • Verifies correct calculation of updated premium
  • Margin of error in premium output • target: 0 • range: 0-0.01
Configured evaluation evidence
  • Benchmark: ActuarialMath-Bench
  • Calculation Error Margin target: 0
  • 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
  • Traceloop · Required
  • Cognition Devin · Optional
  • Google Gemini · Optional
Observation
  • Cognition Devin · Optional
Orchestration
  • Cognition Devin · Optional

Evaluation contract

  • math_correctness · Weight 1
  • calculation_error_margin · Weight 1

Metrics

  • calculation_error_margin · Target 0

Benchmarks

  • ActuarialMath-Bench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing base_premium, line_of_business, cpi_index_change

    Output: JSON containing updated_premium, adjustment_percentage

    Execution harness: Python

Evaluation tools

  • Python Pandas · Data Analytics Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Actuarial Inflation Impact & Dynamic Pricing Engine