AI in Finance

Description

Transform the way you analyze 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 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.

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. You should be comfortable using Excel, and 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?
• Difference between AI, Machine Learning, Deep Learning, and LLMs
• Why AI matters in financial markets today
• Regulatory and compliance context for AI use in finance
• search Buy-side application

Data on Financial Markets

• Market data types: price, volume, order-book data, fundamentals, news
• Alternative data, e.g.: sentiment, macro indicators, geolocation, job postings, and other real-world data
• Cleaning and preparing data for AI systems
• Common pitfalls: survivorship bias, data snooping, leakage, look-ahead bias, survivorship bias
• computer Importing market and alternative data

AI Techniques Used in Market Applications

• Predictive modeling: forecasting returns/volatility
• Classification models for signal generation
• Unsupervised ML for clustering regimes or identifying anomalies
• Natural Language Processing (NLP) for news and sentiment analysis
• Reinforcement Learning in algorithmic trading
• LLMs for research acceleration and market intelligence
• computer News sentiment – Trading Signals using NLP

Building an AI-Powered Market Model

• Constructing a workflow
• Explainability in machine-learning models
• Risk controls and model governance
• computer Running a simplified workflow

Practical Session 1 – Using the AI-Powered Market Model

• Loading and exploring sample market datasets
• Running pre-built models (e.g., price prediction, sentiment-driven signals)
• Viewing outputs: plots, forecasts, confidence intervals
• computer Adjusting parameters, re-running models, and analyzing results

Practical Session 2 – Generating Trading or Investment Insights

• Using AI results to generate actionable insights
• Creating a simple trading rule from model outputs
• Measuring performance: hit rate, PnL curves, Sharpe ratio
• computer Generate signals, compare strategies, and discuss outcomes

AI for Risk Management

• AI for stress testing, scenario generation, and volatility forecasting
• Identifying market anomalies and regime shifts
• Using AI output to support portfolio risk decisions
• computer Monitoring risk metrics

AI in Research and Advisory Functions

• Using LLMs to process reports, macro data, transcripts
• Automating research workflows
• Prompt design for financial analysis
• computer Using an LLM to generate a research note

Responsible and Safe Use of AI in Finance

• Governance and auditability
• Ethical considerations
• SEC, ESMA, and FCA regulatory perspectives
• Human-in-the-loop decision-making

computer Capstone Exercise – Creating an AI-driven Trading Strategy

• Choose a dataset (e.g., FX, equities, indices, crypto)
• Select a model / analytical tool
• Create and test a strategy or investment insight
• Evaluate and present the results

Wrap-Up and Next Steps

• Lessons learned
• Where is AI in finance heading?
• How to integrate AI into daily workflows
• Additional resources for further learning

When and Where
   12 Jan 2026 - 13 Jan 2026
   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.