AIMA Global Investor Board Insights: AI, Machine Learning and Predictive Analytics in Investment Management

Published: 28 February 2025

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  • Overview:
    • AI and predictive analytics can be used to streamline processes and build robust and scalable systems to deliver data-driven insights with transparency and accountability while reducing costs.
    • Machine learning models can identify complex, nonlinear relationships that humans may miss, identifying patterns in structured and unstructured data. 
  • Data & Monitoring: 
    • Extracting and structuring private market data (LPAs, PDFs) can improve monitoring and decision-making. AI can flag key risk indicators like GP commitments, legal terms, and liquidity risks earlier than traditional methods.
    • Success depends on data sample size, data quality, modelling and overcoming technical hurdles (e.g., PDF feature extraction). 
  • Implementation: 
    • Algorithms contextualize performance, helping investors better assess managers and avoid staying invested with underperformers.
    • AI applications in fee validation, legal term reviews, and portfolio analysis can reduce reliance on external consultants. This helps reduce costs while improving accuracy and transparency.
    • Collaboration between investment professionals and data scientists is crucial. Firms that integrate AI effectively, both practically and culturally, will make faster, more informed, data-defended decisions that can be time-weighted and confidence-weighted, then audited over time.
  • Overview:
    • AI and predictive analytics can be used to streamline processes and build robust and scalable systems to deliver data-driven insights with transparency and accountability while reducing costs.
    • Machine learning models can identify complex, nonlinear relationships that humans may miss, identifying patterns in structured and unstructured data. 
  • Data & Monitoring: 
    • Extracting and structuring private market data (LPAs, PDFs) can improve monitoring and decision-making. AI can flag key risk indicators like GP commitments, legal terms, and liquidity risks earlier than traditional methods.
    • Success depends on data sample size, data quality, modelling and overcoming technical hurdles (e.g., PDF feature extraction). 
  • Implementation: 
    • Algorithms contextualize performance, helping investors better assess managers and avoid staying invested with underperformers.
    • AI applications in fee validation, legal term reviews, and portfolio analysis can reduce reliance on external consultants. This helps reduce costs while improving accuracy and transparency.
    • Collaboration between investment professionals and data scientists is crucial. Firms that integrate AI effectively, both practically and culturally, will make faster, more informed, data-defended decisions that can be time-weighted and confidence-weighted, then audited over time.