AI for Finance & FinTech — Inference Phase

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Inference Specialization

AI for Finance & FinTech

AI finance engineers build systems for algorithmic trading, credit scoring, fraud detection, risk management, and financial NLP. Historically the highest-paying ML application domain. Now evolving rapidly with LLMs for document analysis and advisory.

Days

Projects

$230K

Median

Top 3%

Global Comp.

$140K–$350K

Salary Range

Week 1: Financial ML Foundations and Time Series · Days 1–10  

Master financial data structures, time series forecasting, and sound feature engineering for market prediction.

D1–3

Financial data: time series, microstructure, and alternative data

Tick data, OHLCV, order book data, survivorship bias, look-ahead bias — the cardinal sins of financial ML

↗ ML for Trading — Georgia Tech/Coursera (free audit)

D4–6

Time series forecasting: ARIMA, Prophet, and Temporal Fusion Transformer

Financial non-stationarity, regime detection, walk-forward cross-validation, TFT for multi-horizon forecasting

↗ Sequences and Time Series — DeepLearning.AI/Coursera (free audit)

D7–9

Feature engineering for financial ML without leakage

Technical indicators, order flow features, earnings signals, alternative data fusion with strict temporal controls

↗ Advances in Financial ML — Marcos de Prado (free excerpts online)

🏗 Project — Day D10: Stock Return Prediction Pipeline with Walk-Forward Validation 

· 100 or more engineered features: technical, fundamental, and sentiment

· Walk-forward validation proving no look-ahead bias

· 3 models compared: XGBoost vs LSTM vs TFT

· Sharpe ratio and maximum drawdown evaluation per model

· Backtest report with performance attribution analysis

Week 2: Risk Models, Credit Scoring, and Fraud Detection · Days 11–28  

Build the core risk models that power financial services: credit scoring, fraud detection, and market risk measurement.

D11–14

Credit risk: PD, LGD, EAD, scorecards, and Basel III

Probability of default modelling, logistic scorecards, IRB approach, and regulatory model validation requirements

↗ Credit Risk Modeling in Python — DataCamp (free first module)

D15–18

Fraud detection: anomaly detection and Graph Neural Networks

Transaction fraud: isolation forest, autoencoders, GNN for fraud ring detection on payment transaction graphs

↗ PyTorch Geometric documentation (free)

D19–22

Market risk: VaR, CVaR, and Monte Carlo simulation

Value-at-Risk, Expected Shortfall, Monte Carlo scenario generation, stress testing regulatory requirements

↗ Risk Management in Banking — NYU/Coursera (free audit)

D23–27

Model explainability in regulated finance: SHAP and LIME

SHAP values, LIME, building interpretable model reports for regulatory review and model risk management

↗ Machine Learning Explainability — Kaggle (free)

🏗 Project — Day D28: Real-Time Fraud Detection System 

· Scoring API with under 10ms p99 latency

· GNN component for fraud ring detection on transaction graph

· Alert dashboard built in Streamlit

· Precision and recall curve at 0.1% false positive rate

· SHAP per-prediction explanation for every alert

Week 3: LLMs in Finance and Algorithmic Trading · Days 29–40  

Financial NLP for alpha signals, LLM-powered regulatory document analysis, RL for order execution, and a full quant AI system.

D29–32

Financial NLP: FinBERT, earnings call analysis, SEC 10-K parsing

Sentiment signals from filings, earnings call tone analysis, alpha extraction from news and regulatory documents

↗ FinBERT model — ProsusAI on HuggingFace (free)

D33–35

LLMs for regulatory docs: contract analysis and compliance

Clause extraction, obligation detection, risk flagging in ISDA agreements and syndicated loan documents

↗ LlamaIndex structured document extraction guides (free)

D36–38

RL for algorithmic trading and optimal order execution

RL agents for execution optimisation, portfolio management, TWAP and VWAP strategies with FinRL framework

↗ FinRL — AI4Finance Foundation GitHub (free)

🏗 Project — Day D40: Quant AI Trading System — End-to-End Capstone 

· Data pipeline combining price, fundamental, and news sentiment data

· Ensemble model using XGBoost plus FinBERT signals

· Backtest from 2015 to 2024 with walk-forward validation

· Alpaca paper trading API integration for live testing

· Risk management module: position sizing, stop-loss, drawdown limits

· Full strategy attribution report