AI for Finance
Apply best-practice AI techniques to financial data for risk management, fraud prevention, and personalised banking.
Get Course Info
Audience: Financial professionals · FinTech architects · Developers
Duration: 3–4 days
Format: Lectures & demos/labs (50 % / 50 %)
Overview
AI is transforming financial services—from credit-risk assessment to fraud detection. This course shows how to identify high-impact AI use-cases, engineer features, and build fair, interpretable models for structured and unstructured financial data.
Objective
Apply best-practice AI techniques to financial data for risk management, fraud prevention, and personalised banking.
What You Will Learn
- Identify AI opportunities in finance (low-hanging fruit)
- Feature engineering & data prep for structured data
- Classic ML vs deep-learning approaches
- Text analysis for financial insights (NLP, TF-IDF, Spacy)
- GANs & Reinforcement-Learning for adversarial scenarios
- Fairness, bias detection, and regulatory compliance
- Real-world implementations: credit risk, personalised banking, fraud prevention
Course Details
Audience: Financial professionals · FinTech architects · Developers
Duration: 3–4 days
Format: Lectures & demos/labs (50 % / 50 %)
- Interest in finance
- Programming familiarity
Setup: Cloud lab environment provided · Laptop · Browser
Detailed Outline
- History & types of AI
- Training ML models
- Prediction demos
- Feature engineering
- Standard ML
- Deep-learning advantages
- NLTK & TextBlob
- TF-IDF
- Spacy
- 2018 NLP revolution
- GANs
- Reinforcement learning
- Balancing & trading
- Fairness & bias
- Safety & interpretability
- Regulatory hurdles
- Credit-risk management
- Textual insights
- Personalised banking
- Fraud prevention
Ready to Get Started?
Contact us to learn more about this course and schedule your training.