AI & Machine Learning in Finance

Leverage cutting-edge AI and machine learning techniques to revolutionize financial analysis, trading, and risk management.

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CURRICULUM

Foundations of AI and Machine Learning in Finance

01

This module introduces the fundamentals of artificial intelligence and machine learning, with a focus on their applications in finance. Students will learn key concepts such as supervised and unsupervised learning, model evaluation metrics, and data preprocessing techniques. Practical examples will include financial datasets like stock prices, economic indicators, and transaction data. Programming in Python, participants will implement machine learning workflows using libraries such as Pandas, Scikit-learn, and NumPy.

Predictive Modeling and Algorithmic Trading

02

This module focuses on building predictive models for financial applications, including price prediction, credit risk assessment, and portfolio optimization. Students will explore time series analysis, regression techniques, and classification models. Additionally, the module introduces algorithmic trading strategies, covering concepts such as moving average crossovers, mean reversion, and momentum strategies. Using Python, participants will implement trading algorithms with libraries like PyAlgoTrade or Backtrader and backtest them using historical market data.

Natural Language Processing (NLP) for Financial Insights

03

Students will explore NLP techniques to extract actionable insights from unstructured financial data, such as news articles, earnings reports, and social media. Topics include sentiment analysis, named entity recognition, and topic modeling. Participants will use tools like NLTK, SpaCy, and Transformers (Hugging Face) to analyze financial text data. Practical exercises include building sentiment analysis models to gauge market sentiment and predict its impact on asset prices.

Risk Management and Fraud Detection with AI

04

The final module delves into using AI for advanced risk management and fraud detection. Students will learn techniques for anomaly detection, clustering, and reinforcement learning to address challenges such as fraud prevention, anti-money laundering (AML), and operational risk. Real-world case studies will demonstrate AI’s role in enhancing compliance and regulatory frameworks. Using Python, participants will implement machine learning models for fraud detection and build dashboards with Power BI or Streamlit to visualize insights. The course concludes with a capstone project where students develop an AI-driven solution tailored to a financial problem, integrating predictive modeling, NLP, or risk management tools.

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