Learning Roadmap
How to Become a AI Stress Testing Specialist
A step-by-step, phase-based learning path from beginner to job-ready AI Stress Testing Specialist. Estimated completion: 9 months across 6 phases.
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Foundations: Quantitative Finance & Python for Risk
6 weeksGoals
- Master Python data science stack (NumPy, Pandas, SciPy, Matplotlib)
- Understand core financial risk concepts: VaR, CVaR, expected shortfall, drawdown
- Learn basic statistical testing and hypothesis testing for model validation
Resources
- Coursera: Financial Engineering and Risk Management (Columbia)
- Book: 'Quantitative Risk Management' by McNeil, Frey, Embrechts
- Kaggle: Financial risk modeling datasets and notebooks
MilestoneCan independently compute VaR/CVaR for a portfolio and explain tail risk to a non-technical stakeholder
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ML Fundamentals & Model Validation
6 weeksGoals
- Build end-to-end ML pipelines for classification and regression tasks common in finance
- Learn model validation techniques: cross-validation, out-of-time testing, backtesting
- Understand model risk management frameworks (SR 11-7, TRIM)
Resources
- Fast.ai Practical Deep Learning course
- Book: 'Hands-On Machine Learning' by Aurélien Géron
- Federal Reserve SR 11-7 guidance document (mandatory reading)
MilestoneCan build a credit scoring model and produce a model validation report acceptable to a model risk team
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Adversarial ML & AI Safety
8 weeksGoals
- Master adversarial attack methods: FGSM, PGD, C&W, universal perturbations
- Learn LLM-specific attacks: prompt injection, jailbreaking, data poisoning, extraction
- Study AI safety and alignment literature relevant to high-stakes applications
Resources
- MIT 6.S898: Deep Learning and Robustness
- HuggingFace TextAttack documentation and tutorials
- OpenAI red-teaming network published reports
- Paper: 'Adversarial Examples Are Not Easily Triggers' (Carlini et al.)
MilestoneCan craft adversarial examples against both tabular ML models and LLM-based systems, and document attack success rates
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LLM Evaluation & Red-Teaming for Finance
6 weeksGoals
- Build evaluation harnesses using OpenAI Evals, LangSmith, and custom frameworks
- Design domain-specific red-teaming scenarios for financial AI assistants
- Implement guardrails, output filtering, and safety layers for production LLMs
Resources
- OpenAI Evals GitHub repository and documentation
- LangChain evaluation and testing modules
- Anthropic's research on constitutional AI and harmlessness
- Google DeepMind's frontier safety evaluations
MilestoneCan build a comprehensive red-teaming suite for a financial LLM chatbot that covers hallucination, prompt injection, data leakage, and regulatory compliance scenarios
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MLOps, Monitoring & Production Stress Testing
6 weeksGoals
- Implement model monitoring with drift detection, performance degradation alerts, and fairness tracking
- Build chaos engineering experiments for ML pipelines (data outage, feature corruption, latency injection)
- Integrate stress test suites into CI/CD with automated pass/fail gating
Resources
- AWS SageMaker Model Monitor documentation
- Arthur AI and Robust Intelligence platform guides
- Book: 'Designing Machine Learning Systems' by Chip Huyen
- Gremlin or Chaos Monkey documentation for chaos engineering principles
MilestoneCan deploy a production-grade model monitoring system with automated adversarial test triggers and regulatory reporting outputs
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Regulatory Mastery & Executive Communication
4 weeksGoals
- Deep-dive into EU AI Act, Basel model risk requirements, SEC algorithmic trading rules, and MAS FEAT principles
- Learn to write stress test reports that satisfy model risk committees and external auditors
- Develop executive presentation skills for communicating technical risk to boards and regulators
Resources
- EU AI Act full text and implementation guidelines
- PRA Supervisory Statement SS1/23 on model risk management
- Deloitte and McKinsey reports on AI governance in financial services
- Sample model risk documentation packages (anonymized, from practitioner communities)
MilestoneCan produce a complete model stress testing documentation package and present findings to a model risk governance board with confidence
Practice Projects
Apply your skills with hands-on projects. Ordered by difficulty.
Adversarial Robustness Benchmark for Financial Sentiment Models
BeginnerBuild a benchmark suite using HuggingFace TextAttack that evaluates the robustness of financial sentiment analysis models against text perturbation attacks. Test models like FinBERT against synonym swaps, character-level perturbations, and sentence-level transformations using financial news datasets.
LLM Financial Chatbot Red-Teaming Toolkit
IntermediateDesign and implement a comprehensive red-teaming toolkit for a financial advisory chatbot. Cover prompt injection, jailbreaking, hallucination probing, data leakage testing, and regulatory compliance violation scenarios. Use OpenAI Evals framework for structured evaluation.
Synthetic Market Crash Generator for Trading Model Stress Testing
IntermediateBuild a synthetic data generator that creates realistic extreme market scenarios (flash crashes, correlated sector failures, liquidity crises) and replays them against an algorithmic trading model to measure maximum drawdown, recovery time, and circuit-breaker effectiveness.
CI/CD Adversarial Test Integration Pipeline
IntermediateBuild a GitHub Actions pipeline that automatically runs a suite of adversarial robustness tests on every model pull request. Include data drift checks, adversarial accuracy benchmarks, fairness evaluations, and performance regression tests with configurable pass/fail thresholds.
RAG System Robustness Evaluator for Financial Knowledge Bases
AdvancedBuild an evaluation framework that stress tests a RAG system used for financial document analysis. Test retrieval poisoning (injecting misleading documents), context manipulation, numerical accuracy under adversarial inputs, and cross-document consistency checks. Use LangSmith for tracing and evaluation.
Correlated AI Failure Stress Test for Multi-Model Portfolio Risk System
AdvancedDesign and implement a stress testing framework that models correlated failures across multiple AI models (credit risk, market risk, fraud detection) in a portfolio risk system. Simulate scenarios where data pipeline failures, market shocks, and adversarial attacks occur simultaneously, and measure aggregate model risk exposure.
Fairness Stress Testing Dashboard for Lending AI Models
IntermediateBuild an interactive dashboard that continuously monitors and stress tests the fairness of a lending AI model across demographic groups, economic scenarios, and temporal windows. Detect fairness degradation before it causes regulatory or reputational harm.
Adversarial Document Attack Simulator for AI-Powered Compliance Screening
AdvancedCreate a system that generates adversarial financial documents (modified SEC filings, obfuscated sanctions entities, manipulated financial statements) and tests whether AI compliance screening tools can detect violations. Measure evasion rates and build defense recommendations.
Ready to Start Your Journey?
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