Course on Artificial Intelligence applied to Finance

Nid: 29860
Syllabus
Syllabus

1.Fundamentals of Artificial Intelligence in Finance

  • Introduction to AI and its evolution in the financial sector.
  • Key concepts and terminology of AI applied to Corporate Finance and Investments.
  • Differences between machine learning, deep learning, NLP and LLMs and their application in finance.
  • Current overview of the use of AI in financial institutions.
  • Tools, languages and platforms for working with AI in finance: Python, APIs and Bloomberg.
  • Ethics, regulation and risks of the use of AI in finance.
  • Legal and regulatory aspects for the development of financial AI projects.

2.Financial modelling and valuation with AI

  • Traditional versus AI-powered valuation models.
  • Automation of Discounted Cash Flow (DCF) valuation.
  • Machine learning for growth rate estimation and dynamic WACC.
  • Peer valuation with AI: identification of peers and adjustment of multiples.
  • LBO models powered by artificial intelligence.
  • Limitations, biases and good practices in AI valuation models.
  • Valuation case studies in real companies.

3.Predictive analytics and forecast scenarios with AI

  • Time Series Forecasting applied to corporate finance.
  • Prediction of revenue, costs and cash flows with machine learning.
  • Construction of forecast scenarios: base, optimistic, pessimistic and stress scenario.
  • Monte Carlo simulations for sensitivity and scenario analysis.
  • Feature Engineering applied to corporate financial variables.
  • Model interpretability with SHAP Values to explain predictions to Stakeholders.
  • Corporate Forecasting with AI case studies.

4.M&A, due diligence and synergy valuation with AI

  • The M&A process and the areas where AI can generate more value.
  • AI for Target Screening and identification of acquisition opportunities.
  • Automated Due Diligence: contract analysis and detection of legal and financial risks with NLP.
  • Synergy assessment and Post-Merger integration modelling with machine learning.
  • Analysis of target sentiment and reputation with AI tools.
  • Examples and case studies of AI-powered M&A deals.

5.Corporate risk management and anomaly detection with AI

  • Main types of corporate risks: credit, market, operational and reputational.
  • AI models for Credit Scoring and Corporate Default prediction.
  • Fraud and financial anomaly detection with machine learning.
  • Stress Testing and simulation of macroeconomic scenarios with AI.
  • Reputational risk monitoring using NLP and sentiment analysis.
  • Impact of AI on regulation, compliance and regulatory requirements.

6.Intelligent financial reporting and communication with investors

  • Automation of financial reporting: from Excel to Intelligent Dashboards.
  • Automatic generation of financial reports and narratives with LLMs.
  • Building interactive dashboards with real-time market data.
  • AI applied to Investor Relations: sentiment analysis and preparation of Earnings Calls.
  • The new role of the CFO: from financial gatekeeper to data strategist.

7.AI applied to investments & trading

  • Algorithmic trading with AI: strategies, system architecture and Backtesting.
  • Machine learning for market prediction with supervised models and neural networks.
  • Sentiment analysis and use of alternative data in investment strategies.
  • AI-powered portfolio management: advanced optimization, factor investing, and dynamic asset allocation.
  • Market risk management: Dynamic VaR, regime detection, position sizing and tail risk.
  • From Backtest to production: infrastructure, broker APIs, transaction costs and monitoring.

8.Sessions with leading experts in finance and AI

  • Executive vision on the digital transformation of the finance department and investment management.
  • Real cases of AI implementation in corporate finance, markets and investment.
  • Practical applications, lessons learned and future trends.
  • Round table and Q&A session with industry experts.
  • Networking and mentoring with guest professionals.

9.Final practical workshop: AI Valuation, Scenarios and Investment Strategy

  • Analysis of the financial situation of a real company with the support of AI tools.
  • Building an automated DCF model with AI.
  • Development of forecast scenarios with predictive analysis and Monte Carlo simulations.
  • Design of an investment strategy based on the valuation obtained.
  • Automated sensitivity analysis and visualization of results.
  • Preparation of an executive report and final presentation before faculty and invited professionals.