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Challenge

AI-Driven Financial Risk & Credit Scoring

This challenge tasks developers with creating an advanced agent-based system for financial risk assessment and personalized credit scoring for mid-sized companies. The system will leverage Gemini 3 Pro for its deep numerical reasoning and data analysis capabilities, combined with DSPy for programmatic prompting and optimization to ensure highly accurate outputs. MCP will be used to integrate with simulated enterprise financial databases and credit bureaus, allowing agents to access and process critical financial data under an adaptive thinking budget for efficient resource use.

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Challenge brief

What you are building

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

Develop an agent system for financial risk assessment using DSPy, Gemini 3 Pro for deep reasoning, and MCP for financial data integration with adaptive thinking budgets.

Delivery guide

How work is evaluated

Evaluation

Evaluation will focus on the accuracy and depth of financial analysis, the effectiveness of DSPy in optimizing LLM outputs, and the correct integration of financial data via MCP, alongside the intelligent use of adaptive thinking budgets.

Datasets

Challenge datasets

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

Learning goals

What you should walk away with

  • Apply DSPy for systematic prompt engineering and pipeline optimization.

  • Utilize Gemini 2.5 Pro for deep, accurate numerical and qualitative financial reasoning.

  • Employ adaptive thinking budgets for efficient LLM resource management.

  • Implement a multi-stage financial analysis pipeline using agentic principles.

  • Integrate financial data sources via MCP with robust error handling.

Resources and assets

Reference links and supporting material

Dataset notes

Sample data for 2 tasks

Challenge setup

Evaluation will focus on the accuracy and depth of financial analysis, the effectiveness of DSPy in optimizing LLM outputs, and the correct integration of financial data via MCP, alongside the intelligent use of adapt...

Challenge input

{'company_name': 'string', 'annual_revenue': 'float', 'total_assets': 'float', 'total_liabilities': 'float', 'net_income': 'float', 'credit_score_s...

Agent execution

No agent tool is listed for this challenge.

Evaluated output

{'risk_category': 'low|medium|high|very_high', 'recommended_credit_limit': 'float', 'reasoning': 'string', 'key_financial_ratios': {'debt_to_equity...

Evaluation checks
  • Verify successful (simulated) data retrieval from financial databases via MCP for risk assessment.
  • Confirm that DSPy-optimized prompts or modules are demonstrably used in the financial analysis pipeline.
  • Ensure the system correctly adjusts its thinking budget (or simulates adjustment) based on task complexity...
Configured evaluation evidence
  • FinancialRatioAccuracy target: 0.98
Configured runtime
  • Python execution harness

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

No tool records are attached.

Evaluation contract

  • Verify successful (simulated) data retrieval from financial databases via MCP for risk assessment.
  • Confirm that DSPy-optimized prompts or modules are demonstrably used in the financial analysis pipeline.
  • Ensure the system correctly adjusts its thinking budget (or simulates adjustment) based on task complexity or specified 'deep think' flags.

Metrics

  • FinancialRatioAccuracy · Target 0.98
  • CreditRecommendationCoherence · Target 0.9

Workflow tasks

  1. Task 1

    Input: {'company_name': 'string', 'annual_revenue': 'float', 'total_assets': 'float', 'total_liabilities': 'float', 'net_income': 'float', 'credit_score_sim': 'int'}

    Output: {'risk_category': 'low|medium|high|very_high', 'recommended_credit_limit': 'float', 'reasoning': 'string', 'key_financial_ratios': {'debt_to_equity': 'float', 'current_ratio': 'float'}}

    Execution harness: Python

  2. Task 2

    Input: {'scenario_description': 'string', 'base_revenue_projection': 'float', 'economic_downturn_impact': 'float'}

    Output: {'scenario_analysis_summary': 'string', 'budget_used_ms': 'int', 'decision_justification': 'string'}

    Execution harness: Python

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about AI-Driven Financial Risk & Credit Scoring