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Challenge

Credit Oman Automated Trade Credit Risk Assessment Engine

With over $589m credit limits approved by Credit Oman in H1 2026, automating trade credit underwriting requires evaluating buyer financial metrics and macroeconomic trade indicators. Build an automated credit scoring engine using Python and XGBoost that predicts buyer default probability and recommends credit line caps. Achieve an ROC-AUC score above 0.88 on test portfolios.

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Challenge brief

What you are building

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

Build an ML trade credit underwriting and risk scoring pipeline using XGBoost and Python.

Delivery guide

How work is evaluated

Evaluation

Evaluates XGBoost model default prediction ROC-AUC and credit cap decision 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

Risk Cap Check

Fails if default probability > 0.15 but decision is APPROVED.

Binary check

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

Dimension 2

ROC-AUC Score

Model discrimination metric on buyer default validation set. • 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

  • Train XGBoost models on export trade transaction histories

  • Perform feature engineering on debt ratios, payment delays, and sector risk

  • Establish automated credit line limit decision rules

  • Generate explainable SHAP feature importance reports for credit officers

Resources and assets

Reference links and supporting material

Dataset notes

Export buyer financial health logs and payment clearance records from Omani trade portfolios.

Challenge setup

Evaluates XGBoost model default prediction ROC-AUC and credit cap decision accuracy.

Challenge input

JSON object with liquidity_ratio, debt_to_equity, historical_delay_days, requested_limit_usd

XGBoost

Standard gradient boosting framework for tabular financial risk assessment.

Evaluated output

JSON object with default_probability, approved_credit_limit_usd, decision

Evaluation checks
  • Fails if default probability > 0.15 but decision is APPROVED.
  • Model discrimination metric on buyer default validation set. • target: 0.88 • range: 0-1
Configured evaluation evidence
  • Benchmark: FinRiskBench
  • ROC AUC Score 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
  • Yupp AI · Required
  • Sacred · Optional
  • Bland AI · Optional

Evaluation contract

  • Risk Cap Check · Weight 1
  • ROC-AUC Score · Weight 1

Metrics

  • ROC-AUC Score · Target 0.88

Benchmarks

  • FinRiskBench
  • security

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON object with liquidity_ratio, debt_to_equity, historical_delay_days, requested_limit_usd

    Output: JSON object with default_probability, approved_credit_limit_usd, decision

    Execution harness: Python

Evaluation tools

  • XGBoost · Machine Learning Model Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Credit Oman Automated Trade Credit Risk Assessment Engine