Training AI and ML Models for Alternative Investment Valuation

Beyond IF > THEN > ELSE

From forecasting everything from revenues to cashflows to interest rates, from screening securities to constructing portfolios to calculating the magnitude of potential “tail events” at the extremes of statistical horizons, technological automation of the collecting, ordering and analysis of data has always been an inseparable component of the workings of finance.

Advances have led to artificial intelligence (AI) and machine learning (ML) taking a firm hold within all areas of finance, particularly in the realm of analysis and decision science.

Whereas decision outcomes were once drawn from historical statistical aggregations — which themselves evolved into broad multivariate regression models — we are now increasingly witnessing autonomous systems handling complex analyses and decisions, practically in real time, as robust AI and ML models generate ever stronger causal relationships and better accuracy in predictions.

This is changing the ways practitioners process and evaluate machine-derived valuations, especially for the often complex and nontraditional assets and instruments found in the alternative investments sector.

Of key importance is a fundamental understanding of the nature of the training set employed, the categories and characteristics of the models used, and how they are applied in an algorithmic fashion.

Join us as our experts develop and explain technical use cases in AI and ML valuation techniques for private equity and credit instruments, and discuss the profound implications for the evolution of the technology over the years ahead.

• Experienced experts in both valuation and AI share their knowledge
• Explore interesting real world applications
• Understand how the valuation output depends on the training set applied
• Become savvy in the terminology of AI-derived valuations

This webcast is part of our Portfolio Valuation Forum series, covering financial instrument and valuation topics in the world of alternative investments

Key Topics

• Understand the training sets and models
• How AI can be applied to private equity valuation
• Use case of AI in credit analysis
• Implications for practitioners, auditors and regulators

Learning Outcomes

• Identify how AI is applied within valuation models.
• Recognize how data drives AI-informed decision-making.
• Compare different approaches.
• Determine main issues and common challenges for practitioners and auditor/regulator.
• Differentiate between the commonly adopted AI technologies.

Who Will Benefit

Valuation practitioners, financial auditors, regulators, investment managers, banking product developers, controllers, CFO analysts, data science and financial technology academic researchers, educators and students, strategy and operations consultants to financial services sector, financial software developers