AI in Finance

Intermediate

CPD: 14 hours

4 half-days

Live

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.

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Seminar ContentIntroduction and FoundationsWhat is AI?Difference between AI, Machine Learning, Deep Learning, and LLMsWhy AI matters in financial markets todayRegulatory and compliance context for AI use in financesearch Buy-side applicationData on Financial MarketsMarket data types: price, volume, order-book data, fundamentals, newsAlternative data, e.g.: sentiment, macro indicators, geolocation, job postings, and other real-world dataCleaning and preparing data for AI systemsCommon pitfalls: survivorship bias, data snooping, leakage, look-ahead bias, survivorship biascomputer Importing market and alternative dataAI Techniques Used in Market ApplicationsPredictive modeling: forecasting returns/volatilityClassification models for signal generationUnsupervised ML for clustering regimes or identifying anomaliesNatural Language Processing (NLP) for news and sentiment analysisReinforcement Learning in algorithmic tradingLLMs for research acceleration and market intelligencecomputer News sentiment – Trading Signals using NLPBuilding an AI-Powered Market ModelConstructing a workflowExplainability in machine-learning modelsRisk controls and model governancecomputer Running a simplified workflowPractical Session 1 – Using the AI-Powered Market ModelLoading and exploring sample market datasetsRunning pre-built models (e.g., price prediction, sentiment-driven signals)Viewing outputs: plots, forecasts, confidence intervalscomputer Adjusting parameters, re-running models, and analyzing resultsPractical Session 2 – Generating Trading or Investment InsightsUsing AI results to generate actionable insightsCreating a simple trading rule from model outputsMeasuring performance: hit rate, PnL curves, Sharpe ratiocomputer Generate signals, compare strategies, and discuss outcomesAI for Risk ManagementAI for stress testing, scenario generation, and volatility forecastingIdentifying market anomalies and regime shiftsUsing AI output to support portfolio risk decisionscomputer Monitoring risk metricsAI in Research and Advisory FunctionsUsing LLMs to process reports, macro data, transcriptsAutomating research workflowsPrompt design for financial analysiscomputer Using an LLM to generate a research noteResponsible and Safe Use of AI in FinanceGovernance and auditabilityEthical considerationsSEC, ESMA, and FCA regulatory perspectivesHuman-in-the-loop decision-makingcomputer Capstone Exercise – Creating an AI-driven Trading StrategyChoose a dataset (e.g., FX, equities, indices, crypto)Select a model / analytical toolCreate and test a strategy or investment insightEvaluate and present the resultsWrap-Up and Next StepsLessons learnedWhere is AI in finance heading?How to integrate AI into daily workflowsAdditional resources for further learning

When and Where
   10 Nov 2026 - 13 Nov 2026
   a.m. sessions – 08:00 to 12:00 (for Asia / EMEA participants)
   p.m. sessions – 13:00 to 17:00 (for EMEA / Americas participants)
   All times are GMT/BST (London time)
   Virtual
   Data Science

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

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