FinRuleBench: A Sandboxed Evaluation Framework for AI's Financial Reasoning Capabilities

FinRuleBench is a sandboxed benchmark framework designed specifically to evaluate the financial reasoning capabilities of AI models. Through simulated trading scenarios, hidden field protection, and deterministic replay mechanisms, it provides a reliable capability evaluation standard for the safe deployment of financial AI. It addresses the problem that traditional AI evaluations lack assessments of complex reasoning, risk control, and compliance boundaries in financial scenarios, establishes industry standards, and helps financial institutions and developers verify model capabilities.

As large language models are increasingly applied in the financial field, AI systems are taking on important decision-making roles. However, financial decisions have high risk and strict regulatory requirements. Traditional evaluations focus on general knowledge Q&A or code generation, lacking systematic assessments of complex reasoning, risk control, and compliance boundaries in financial scenarios. FinRuleBench (formerly LexCapital) provides a fully isolated sandbox environment, allowing developers to test AI's financial decision-making capabilities with zero risk.

FinRuleBench follows three key principles: 1. Sandboxed Security Isolation: All transactions are conducted in a simulated environment with no connection to real funds, eliminating testing risks; 2. Hidden Field Protection: Hide fields such as future prices and trap conditions to simulate information asymmetry in the real world; 3. Deterministic Replay and Reproducible Scoring: Generate replay records to ensure consistent results, use quantitative scoring based on asset value, maximum drawdown, etc., and directly disqualify (DQ) with zero points for non-compliant operations.

Covers four key dimensions: 1. Financial Rule Reading and Comprehension: Accurately understand rules such as trading restrictions and position requirements and convert them into constraints; 2. Legal Compliance Boundary Identification: Identify allowed operation spaces under complex constraints; 3. Synthetic Market Trap Response: Test robustness against edge cases like abnormal fluctuations and misleading signals; 4. Risk Calibration and Uncertainty Handling: Evaluate risk-return trade-offs and conservative strategy choices when information is limited.

Provides a complete CLI toolchain: 1. Scenario Validation and Prompt Rendering: The validate command checks scenario formats, and render-prompt views the actual prompts for models; 2. Evaluation Modes: Supports external model evaluation (via adapter calls) and self-evaluation (AI autonomous decision-making); 3. Batch Evaluation and Result Aggregation: run-suite runs scenarios in batches, and score-dir generates comprehensive scoring reports.

FinRuleBench establishes industry standards for financial AI capability evaluation: For financial institutions, it is a verification method for model selection and safe deployment; for developers, it points out optimization directions; in the context of strict regulation, it serves as compliance support material; the sandbox design reduces evaluation risks and adoption thresholds.

FinRuleBench represents the trend of AI evaluation towards specialization in vertical fields. Models with strong general capabilities may not be suitable for high-risk financial fields. Sandbox evaluation can identify AI capability boundaries and potential risks in advance. It is recommended that teams planning to deploy financial AI include it in their toolkits.