AI for Finance, Risk, and Compliance Training in Kraków, Poland

This training provides a comprehensive understanding of how artificial intelligence is transforming finance, risk management, and compliance. Participants will learn to apply AI techniques to real-world financial challenges, from fraud detection and credit risk assessment to regulatory reporting and anti-money laundering. The course combines theoretical foundations with practical exercises, enabling professionals to leverage AI for more accurate predictions, efficient processes, and robust compliance frameworks.

Introduction to AI in Finance, Risk, and Compliance

• Overview of AI and its applications in the financial sector
• Key drivers: data volume, regulatory pressure, and competitive advantage
• Ethical considerations and regulatory landscape

Hands-on exercises

Data Foundations for Financial AI

• Types of financial data: structured, unstructured, and time series
• Data cleaning, normalization, and handling missing values
• Feature engineering for financial datasets

Hands-on exercises

Supervised Learning for Credit Risk and Fraud Detection

• Classification algorithms: logistic regression, decision trees, random forests
• Model training, evaluation, and hyperparameter tuning
• Case studies: credit scoring and transaction fraud detection

Hands-on exercises

Unsupervised Learning for Anomaly Detection and Clustering

• Clustering techniques: K-means, DBSCAN, hierarchical clustering
• Anomaly detection methods: isolation forest, autoencoders
• Applications in anti-money laundering and insider trading

Hands-on exercises

Time Series Analysis and Forecasting

• Time series components: trend, seasonality, noise
• ARIMA, Prophet, and LSTM models for financial forecasting
• Volatility modeling and risk prediction

Hands-on exercises

Natural Language Processing for Compliance

• Text preprocessing and tokenization
• Sentiment analysis for market monitoring
• Named entity recognition for regulatory filings

Hands-on exercises

Model Interpretability and Explainable AI

• Importance of interpretability in finance
• SHAP, LIME, and feature importance techniques
• Bias detection and fairness in AI models

Hands-on exercises

Deployment and Monitoring of AI Systems

• Model deployment strategies: APIs, batch processing, edge
• Monitoring model drift and performance degradation
• Governance and version control for AI models

Hands-on exercises

Regulatory Compliance and Ethical AI

• Overview of key regulations: GDPR, SOX, Basel III, AML directives
• AI governance frameworks and best practices
• Case studies on regulatory use of AI

Hands-on exercises

Summary and Conclusion