AI4S Financial Domain: Agent-Governed Discovery Architectures
This repository is a collection of governed AI4S prototypes for high-accountability financial and professional domains. The project applies the AI for Science discovery loop to algorithmic trading, tax planning, civil litigation strategy, financial advice, and investment banking / M&A. Across all domains, the same rule applies: the model proposes, deterministic engines compute, professional review challenges, and governance decides.
Design principle: this repository is not organized around model capability. It is organized around
scope → hypothesis → computation → stress → review → decision → audit trail.
Why AI4S Matters for Financial and Professional Domains
High-stakes professional work is not solved by a persuasive answer. A strategy must be testable, reviewable, defensible, and compatible with domain constraints. In finance, tax, law, wealth management, and M&A, the real risk is not that an AI system says nothing useful. The risk is that it says something plausible, confident, and wrong.
- AI4S turns plausible output into testable hypotheses: the LLM proposes candidates that must enter a structured evaluation pipeline.
- Deterministic engines prevent narrative hallucination from becoming calculation: numerical results come from code, not prose.
- Stress testing challenges base-case optimism: each candidate is tested under adverse conditions before approval.
- Human review remains a binding control: simulated professional agents model the review that real deployment would require.
- Evaluation agents preserve governance discipline: continuation, stopping, and failure memory are explicit.
The Five AI4S Financial Domain Architectures
The collection is organized into five domain-specific prototypes. Each one includes both a paper PDF and a Colab notebook. The common pattern is identical: generate, compute, stress, review, decide, and iterate.
AI4S: Framework for Scientific Discovery in Finance
Comprehensive presentation deck and companion book covering the complete AI4S architecture, governance framework, discovery-loop methodology, agent-governed workflows, and all five financial-domain applications: algorithmic trading, tax planning, civil litigation strategy, financial advice, and investment banking & M&A.
1. AI4S Algorithmic Trading
Agent-governed alpha discovery with synthetic market data, feature engineering, LLM-generated hypotheses, deterministic backtesting, stress testing, paper simulation, and EvaluationAgent governance.
2. AI4S Tax Planning and Audit Governance
Tax-strategy discovery with a synthetic tax code, taxpayer profile, deterministic tax engine, legal gate, stress testing, simulated CPA review, and TaxEvaluationAgent governance.
3. AI4S Civil Litigation Strategy
Legal theory discovery with synthetic jurisdiction rules, evidence inventory, precedent simulation, ethical gate, settlement analysis, attorney review, and LitigationEvaluationAgent governance.
4. AI4S Financial Advice
Household wealth planning with ranked goals, synthetic market assumptions, Monte Carlo simulation, suitability gate, behavioral review, stress testing, and WealthEvaluationAgent governance.
5. AI4S Investment Banking and M&A
M&A target discovery with acquirer mandate, target universe, deterministic valuation, synergy modeling, deal structuring, regulatory screen, MD review, and DealEvaluationAgent governance.
Common Architecture Across the Collection
Every project in the repository follows the same governance grammar. The domain changes, but the structure remains stable.
Defines the bounded world in which the system operates.
Boundary discipline
The model cannot invent the universe it is evaluated against.
Encodes the specific facts of the problem.
Fact discipline
The workflow begins from structured facts.
Generates strategies, hypotheses, theories, target shortlists, or planning alternatives.
Creativity under constraint
The LLM proposes candidates; it does not authorize outcomes.
Computes domain-specific outcomes.
Computation discipline
Numbers come from reproducible code, not model prose.
Challenges the best candidate under adverse conditions.
Fragility discipline
Base-case success is not enough.
Simulates the review layer appropriate to the domain.
Accountability discipline
Professional judgment remains a gate.
Decides whether to continue, stop successfully, or halt.
Governance discipline
The system learns from failure without searching indefinitely.
The Shared AI4S Workflow
↓
Case / Client / Market / Mandate Profile
↓
LLM Candidate Generation
↓
Parser and Validation Layer
↓
Domain-Specific Deterministic Engine
↓
Stress Testing
↓
Professional Review Agent
↓
Evaluation Agent
↓
Failure Context Injection
↓
Next Research Cycle
Shared Design Principles
1. The LLM proposes; it does not authorize
The model generates candidates, strategies, theories, or target shortlists.
2. Deterministic engines compute outcomes
Metrics are computed by code-controlled engines.
3. Professional gates are mandatory
Each domain includes a domain-appropriate professional review layer.
4. Stress testing is part of the method
Candidates are tested under adverse scenarios.
5. Failure becomes structured memory
Rejected candidates and concerns are converted into failure context.
6. Governance separates research from deployment
These notebooks demonstrate controlled research workflows.
Educational and Research Use
This repository is intended for students, instructors, researchers, and practitioners who want to understand how generative AI can be used responsibly inside financial and professional advisory workflows.
- Start with the AI4S book and master deck to understand the common architecture.
- Read the paper for the domain most relevant to your interest.
- Run the corresponding Colab notebook.
- Inspect the deterministic engine before interpreting the agent outputs.
- Treat every result as a research artifact, not a recommendation.
License and Disclaimers
Educational disclaimer: This material is provided for educational and methodological purposes only. It does not constitute investment advice, financial advice, legal advice, tax advice, accounting advice, regulatory advice, fiduciary advice, transaction advice, or a recommendation to buy, sell, short, hold, trade, invest, litigate, settle, file, structure, merge, acquire, or finance any transaction.