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.
What you are building
The core problem, expected build, and operating context for this challenge.
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.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Master DSPy for programmatically defining, optimizing, and evaluating LLM-powered systems, focusing on prompt and module optimization for financial accuracy.
Leverage Gemini 3 Pro's 'Deep Think' mode for complex financial calculations, risk modeling, and qualitative assessment of business health from unstructured data.
Design and implement MCP-enabled tool integration for secure access to simulated enterprise financial databases (e.g., P&L statements, balance sheets) and credit bureau APIs.
Build a hybrid reasoning system that combines instantaneous analysis with deeper, adaptive thinking budgets for computationally intensive tasks like scenario planning or anomaly detection.
Orchestrate a workflow using Semantic Kernel 'skills' or functions to encapsulate financial analysis tasks (e.g., 'calculate_debt_to_equity', 'assess_cash_flow'), making them easily callable by agents.
Develop agents capable of generating comprehensive financial risk reports and personalized credit recommendations, clearly articulating reasoning and data sources.
How this agent runs
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
The configured agent processes the input under the challenge policy.
Evaluated output
{'risk_category': 'low|medium|high|very_high', 'recommended_credit_limit': 'float', 'reasoning': 'string', 'key_financial_ratios': {'debt_to_equity...
- 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...
- FinancialRatioAccuracy target: 0.98
- Python execution harness
View technical recipe
Configured tools
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Evaluation contract
- The evaluation module defines the checks.
Recipe state
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Run this agent on your dataset and AI stack
Bring your dataset, model providers, and success criteria. We will scope the right managed run for your team.
Scope a managed run[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
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