Interview: Generative AI’s impact on private markets asset management

Aman Soni, VP of data strategy at Canoe Intelligence, shares insights on the role of generative AI in reshaping private market asset management strategies

How could generative AI’s improved valuations in private markets affect asset managers’ traditional risk assessment models, and what adaptations might be needed to integrate this technology effectively?

Generative AI, with its advanced algorithms and capacity to process extensive datasets, can provide deeper insights into real-time asset valuation trends, thereby enhancing risk assessment capabilities. This includes aggregating and analysing assets with similar characteristics over time, potentially allowing for intra-quarter marks with private market comparables, which differs from the current practice of primarily assessing public markets. Asset managers need to adjust their models to integrate this technology effectively, updating risk parameters and integrating AI-generated valuations into decision-making processes. They should also be receptive to adopting new valuation methodologies as valuation points become more available. Collaboration between AI systems and human expertise will be crucial in adopting a data-driven approach.

What challenges might arise in ensuring data quality and reliability for asset managers, and how can they mitigate these risks to maintain trust and credibility?

Asset managers must address issues such as data bias, incomplete datasets and algorithmic transparency to maintain trust and credibility. Mitigating these risks requires robust data validation processes, ongoing monitoring of AI models and transparent communication with stakeholders regarding the limitations of AI-generated insights. Additionally, fostering a culture of accountability and investing in continuous training and development for AI systems can help mitigate risks and uphold credibility in data-driven decision-making.

How will the use of generative AI contribute to the standardisation of alternative investment data in the private markets sector?

Generative AI can standardise alternative investment data in private markets by automating data collection, extraction and normalisation. It processes unstructured data to create standardised datasets, improving comparability across portfolios. This technology is expected to lead entity matching and resolution efforts in private market funds, enabling investors to better understand asset profiles and exposures. Asset managers must leverage generative AI to establish common data standards, improve data quality, and enhance transparency and efficiency in the private markets sector.

“Generative AI can standardise alternative investment data in private markets by automating data collection, extraction and normalisation.”

How might the emergence of performance benchmarks, facilitated by generative AI, impact transparency within the private markets industry?

By leveraging AI-driven insights to analyse vast datasets and derive relevant data points from both cash flow and valuation statements, asset managers can begin to establish comprehensive real time benchmarks that provide investors with greater visibility into not only fund performance and market trends but also underlying asset performance and ultimately deal level benchmarks. These benchmarks offer investors deeper insights into fund performance, market trends, and underlying asset performance, enhancing transparency and accountability in the private markets industry.

Considering the regulatory scrutiny across the US, Europe and the UK, how can generative AI help asset managers meet compliance requirements while navigating the evolving landscape of private markets?

By automating data processing and analysis, generative AI can help asset managers efficiently gather, analyse and report on vast amounts of data, ensuring compliance with regulatory mandates right down to the asset level. Additionally, it can enhance risk management practices, detect potential regulatory violations and facilitate proactive compliance measures.
Exposure reporting remains vital amidst regulatory changes, necessitating the use of Generative AI to standardise and extract asset-level data. Asset managers must prioritise integrating Generative AI into their compliance frameworks, fostering collaboration between AI systems and human experts to navigate regulatory shifts effectively.

What is the current level of generative AI usage in private markets?

The current level of generative AI usage in private markets varies, with adoption rates steadily increasing as asset managers recognise the transformative potential of AI-driven technologies. While some firms have embraced generative AI to enhance data analytics, valuation models, and risk management practices, others are still exploring its applications and assessing its impact on investment strategies.

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