AI in Fintech: Complete Guide
Fintech AI Landscape
Fraud Detection Architecture
Algorithmic Trading Strategies
Interview Q&A
Q1: How do you handle class imbalance in fraud detection?
Answer: Fraud datasets are typically 99.9% legitimate. Use SMOTE for oversampling, random undersampling, or hybrid approaches. Apply class weights in model training. Use precision-recall AUC instead of ROC AUC. Focus on minimizing false negatives (missed fraud) while keeping false positives manageable. Consider anomaly detection approaches that don't require balanced labels.
Q2: What features are most predictive for credit risk scoring?
Answer: Payment history (35%), amounts owed (30%), length of credit history (15%), new credit (10%), credit mix (10%). Additional AI features: transaction velocity, income estimation, employment stability, social signals (with consent), alternative data (rent, utilities). Use SHAP values for explainability requirements.
Q3: Explain the difference between high-frequency trading and algorithmic trading.
Answer: Algorithmic trading uses computer programs to execute trades based on predefined rules. High-frequency trading (HFT) is a subset that executes thousands of trades per second with minimal latency. HFT requires co-location with exchanges, specialized hardware, and ultra-low latency networks. Most algorithmic trading is not HFT.
Q4: How do you ensure model explainability in regulated financial environments?
Answer: Use inherently interpretable models (linear, decision trees) where possible. Apply SHAP or LIME for post-hoc explanations. Maintain model documentation and audit trails. Implement challenger models for validation. Provide feature importance rankings. Generate human-readable explanations for each decision. Comply with SR 11-7 model risk management guidance.
Q5: What are the key challenges in building a robo-advisor?
Answer: Risk profiling accuracy, portfolio optimization under constraints, tax-loss harvesting, rebalancing algorithms, regulatory compliance (Investment Advisers Act), client communication during drawdowns, handling edge cases (inheritances, emergencies), and maintaining fiduciary duty while scaling.
Q6: How do you detect money laundering patterns with AI?
Answer: Use graph neural networks to identify suspicious transaction networks. Apply rule-based systems for known patterns (structuring, layering). Use clustering to detect unusual behavior. Monitor for rapid fund movements across accounts. Flag transactions with high-risk jurisdictions. Implement dynamic risk scoring that updates based on new intelligence.
Q7: What is the role of NLP in financial analysis?
Answer: Sentiment analysis of news and social media for trading signals. Earnings call transcript analysis. Regulatory filing comprehension (10-K, 10-Q). Customer complaint analysis. Contract review and extraction. Market research summarization. Risk report generation from unstructured data.
Q8: How do you handle concept drift in financial models?
Answer: Monitor model performance metrics continuously. Implement automated retraining pipelines. Use online learning for rapid adaptation. Track feature drift with statistical tests. Maintain champion-challenger model comparisons. Implement model versioning and rollback capabilities. Set performance thresholds that trigger retraining.