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Challenge

Student Cooperative Micro-Credit Scoring & Fraud Alert System

Digital transformation in student cooperatives and community micro-finance requires automated risk assessment for small working capital loans. Construct a lightweight credit scoring agent using XGBoost and Polars to evaluate loan application risks. Achieve high default prediction accuracy while ensuring zero demographic bias.

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Challenge brief

What you are building

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

Build an automated micro-credit risk evaluation and fraud alert pipeline using Polars and XGBoost.

Delivery guide

How work is evaluated

Evaluation

Evaluates default prediction ROC-AUC score and fair-lending threshold compliance.

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

risk_threshold_check

Verifies that high default risk results in immediate REJECT decision.

Binary check

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

Dimension 2

roc_auc

Area under ROC curve for default prediction model. • target: 0.88 • range: 0-1

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

  • Ingest high-throughput transaction telemetry using Polars DataFrames

  • Train gradient boosted decision trees with XGBoost for default probability estimation

  • Enforce fairness constraints to prevent biased credit decisioning

  • Expose fast evaluation routines for embedded micro-finance mobile apps

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic micro-finance loan applicant repayment dataset.

Challenge setup

Evaluates default prediction ROC-AUC score and fair-lending threshold compliance.

Challenge input

JSON applicant financial profile

Polars

High performance Rust-backed DataFrame library.

Evaluated output

JSON decision and risk score

Evaluation checks
  • Verifies that high default risk results in immediate REJECT decision.
  • Area under ROC curve for default prediction model. • target: 0.88 • range: 0-1
Configured evaluation evidence
  • Benchmark: MicroCredit-FairRisk-Bench
  • Roc Auc target: 0.88
  • 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
  • Capably · Required
  • AutoML (H2O) · Optional
  • MemGPT · Optional

Evaluation contract

  • risk_threshold_check · Weight 1
  • roc_auc · Weight 1

Metrics

  • roc_auc · Target 0.88

Benchmarks

  • MicroCredit-FairRisk-Bench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON applicant financial profile

    Output: JSON decision and risk score

    Execution harness: Python

Evaluation tools

  • Polars · DataFrame Engine

Recipe freeze marker

No recipe freeze time is recorded.

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