AI in Finance

Description

Transform the way you analyse markets with the power of AI. This intensive, hands-on course shows finance professionals how AI is reshaping trading, research, and portfolio management – and how to apply these capabilities immediately.

Using real market data, practical case studies, and ready-to-use AI tools, participants leave with skills they can deploy the very next day. Gain a competitive edge and turn AI from a buzzword into a bottom-line advantage.

Learning Outcomes

By attending this two-day course, you will:

• Understand how AI is reshaping financial markets to deliver real commercial value.
• Identify meaningful opportunities to use AI in trading, investment research, portfolio management, and risk oversight.
• Interpret AI-generated insights to support faster, more informed financial decisions.
• Evaluate AI-driven strategies using clear performance and risk metrics.
• Apply practical AI tools to analyse markets, evaluate ideas, and enhance investment processes.

Who Should Attend

Anyone who wants to understand and apply AI in financial markets in a practical, results-driven way. This includes investment analysts, asset managers, traders, and those working in execution and risk management.

Prerequisites

You should have a basic understanding of financial markets, asset classes, market terminology, and common investment strategies. The ability to run pre-written Python code in a Jupyter notebook is useful but not essential.

Seminar Content

Introduction and Foundations

• What is AI?
• Machine learning, deep learning, generative AI, and LLMs
• AI agents and automated financial workflows
• Why AI matters in financial markets today
• Opportunities and limitations of AI in financial decision-making
• Regulatory and compliance context
• search Case Study: AI applications in investment management

Data in Financial Markets

• Market data: prices, volumes, order books, fundamentals, and news
• Alternative data: consumer spending, sentiment, macroeconomic indicators, geolocation, and other real-world data
• Using generative AI to explore and analyse large financial datasets
• Preparing and combining datasets for AI applications
• Data quality and common modelling pitfalls: survivorship bias, look-ahead bias, leakage, and overfitting
• computer Workshop: Importing and exploring market and alternative data

AI Techniques Used in Market Applications

• Predictive modelling: forecasting returns, volatility, and market trends
• Classification models for trading signals and investment decisions
• AI-assisted portfolio construction and optimisation
• Unsupervised learning: identifying market regimes and anomalies
• Natural language processing (NLP) and sentiment analysis of financial news
• Generative AI and LLMs for financial analysis
• AI agents for market monitoring and investment research
• computer Workshop: Using AI to turn financial news into trading signals

Building an AI-Powered Market Model

• The AI workflow: data, model selection, training, testing, and deployment
• Evaluating predictive accuracy and financial usefulness
• Explainability and interpreting model outputs
• Avoiding overfitting and ensuring robust out-of-sample performance
• computer Workshop: Running and evaluating an AI market model

Practical Session 1 – Using AI Models

• Loading and exploring real market datasets
• Running ready-built AI models for market prediction and analysis
• Interpreting forecasts, probabilities, and model outputs
• computer Workshop: Adjusting model parameters and comparing results

Practical Session 2 – Generating Trading and Investment Insights

• Translating AI predictions into trading, investment, and portfolio allocation decisions
• Constructing and backtesting AI-driven strategies
• Evaluating returns, Sharpe ratios, drawdowns, and transaction costs
• Comparing AI strategies against conventional approaches
• computer Workshop: Generating signals, testing strategies, and evaluating performance

AI for Risk Management

• AI applications in volatility forecasting and risk assessment
• Identifying market anomalies and changing market regimes
• AI-assisted scenario analysis and portfolio stress testing
• Using AI insights to support portfolio risk decisions
• computer Workshop: Evaluating portfolio exposures under different market scenarios

AI in Investment Research and Decision-Making

• Using LLMs to analyse financial reports, earnings announcements, and economic data
• Retrieval-Augmented Generation (RAG) – combining AI with trusted financial information
• AI agents for automating financial research and analysis
• Prompting techniques for effective financial analysis
• Validating AI-generated research and recommendations
• computer Workshop: Using AI to analyse financial documents, conduct research, and prepare an investment briefing

Responsible and Safe Use of AI in Finance

• AI governance, accountability, and regulatory expectations
• Hallucinations, unreliable outputs, and model limitations
• Data confidentiality, security, and intellectual property
• Model monitoring, auditability, and human oversight
• Risks associated with autonomous AI agents

Capstone Exercise

• Select a financial objective and build on the models and datasets used earlier
• Apply an AI model or analytical tool
• Generate and evaluate a trading, investment, or risk-management recommendation
• Present findings, including financial implications, limitations, and risks

Wrap-Up and Next Steps

• Key lessons and practical applications
• Emerging developments in AI for financial markets
• Integrating AI into everyday financial workflows
• Opportunities for further development

When and Where
   13 Jan 2027 - 14 Jan 2027
   09:00-17:00
   New York
   Data Science

Book Now!

Other Dates and Locations
Search for AI in Finance in our course schedule for alternative dates and locations where this course is offered.