Private Equity firms are increasingly adopting AI-driven analytics to enhance portfolio monitoring, performance attribution, and value creation tracking. This course equips professionals with the latest tools and techniques to automate reporting, forecast performance, identify risks, and uncover performance drivers using AI and machine learning models. Practical sessions include real-time dashboarding, anomaly detection, and NLP-driven qualitative analysis.
- Introduction to AI use cases in PE portfolio management
- Performance attribution with machine learning
- Real-time KPI monitoring dashboards using AI tools
- Forecasting fund and portfolio company performance
- Anomaly and risk detection in portfolio metrics
- AI-based benchmarking against market comparables
- Automating board and LP reporting with AI
- NLP for sentiment analysis in portfolio reviews
- Integrating ESG performance analytics using AI
- Case studies from top-tier PE firms
- Apply AI techniques to monitor PE portfolios more efficiently
- Attribute performance outcomes using advanced analytics
- Create interactive dashboards to track financial and operational KPIs
- Forecast financial trends using regression and time-series models
- Detect portfolio company risks early with anomaly detection
- Automate performance and ESG reporting for stakeholders
- Perform qualitative analysis of portfolio reviews using NLP
- Benchmark portfolio performance using machine learning models
- Private equity and venture capital professionals - Portfolio monitoring and valuation teams - Financial analysts and data scientists in investment firms - Fund administrators and investment officers - Reporting and investor relations professionals - Strategy consultants and CFOs of portfolio companies
- Real-time dashboards with Power BI, Streamlit, and Tableau - Python-based forecasting and performance attribution labs - AI tools for NLP, anomaly detection, and ESG reporting - Interactive workshops on automation of reporting - Case-based learning from real PE portfolios - Final project: AI-powered portfolio monitoring & attribution report
- Overview of portfolio monitoring challenges in PE
- Key performance indicators: financial, operational, ESG
- AI tools landscape in portfolio analytics
- Data pipelines and quality for AI modeling
- Automation opportunities in reporting and alerts
- Case study: Traditional vs AI-enhanced monitoring
- Attribution models: absolute vs relative performance
- Regression and XGBoost for driver analysis
- Classifying performance factors using AI
- Portfolio factor modeling and impact quantification
- Building custom attribution frameworks
- Lab: ML model for performance decomposition
- Visualizing portfolio KPIs using dashboards
- Tools: Power BI, Tableau, Streamlit, Google Data Studio
- Real-time alerts using data streams and AI triggers
- Integration of portfolio systems with dashboarding tools
- Automating board and LP reports
- Lab: Build a PE monitoring dashboard
- Early detection of underperformance
- Outlier detection using unsupervised learning
- Pattern recognition in financial metrics
- Sector and peer comparison with clustering
- Visual analytics for risk signals
- Lab: Python-based anomaly detection project
- Text analysis in portfolio reporting and reviews
- NLP for board meeting minutes and sentiment tracking
- ESG performance modeling with AI and alternative data
- AI use cases in diversity, governance, and impact tracking
- Final presentations: Portfolio analytics project simulation
- Feedback and certification
Group & Corporate Discounts: Available for companies enrolling multiple participants to help maximize ROI. Individual Discounts: Offered to self-sponsored participants who pay in full and upfront. Registration Process: Corporate nominations must go through the client’s HR or Training department. Self-nominations must be prepaid via the “payment by self” option. Confirmation: All registrations are subject to DIXONTECH’s approval and seat availability. Refunds: Provided in case of course cancellation or no seat availability. Tax Responsibility: Clients are responsible for any local taxes in their country.