Nickel: AI is central to increasing digital asset allocations
The adoption of AI and machine learning tools is central to increasing digital asset allocations with risk management tools the major focus, new global research from London-based Nickel Digital Asset Management (Nickel) shows.
Nearly four out of five (78%) of institutional investors and wealth managers believe AI tools will be critical or very important in their organisation’s decision to increase digital asset allocations over the next two years, the study with more than 200 senior executives found.
The availability of AI-led risk management tools from institutional-grade managers is key to increasing allocations, the research found, with 78% questioned saying they are likely to increase digital asset exposure over the next 12 to 24 months as a result.
Around three out of four (75%) organisations which already invest in digital assets said they would increase their allocations by more than 25% if AI risk tools could be proven to materially improve drawdown control and oversight.
Pressure to adopt AI-enabled risk management will build, the research found. Within five years 90% agree that institutional digital asset managers will need comprehensive AI-enabled risk management to be considered credible, the study across the US, UK, UAE, Germany, Switzerland, France, Italy, the Netherlands, Singapore, Brazil and the Nordics found.
Institutional investors and wealth managers say AI-enabled portfolio construction and position-sizing tools are marginally most likely to increase their investment committee’s confidence in digital assets with 40% selecting them ahead of 39% choosing automated compliance and regulatory reporting.
Around 36% chose real-time risk monitoring across exchanges, custodians and wallets as most likely to appeal to investment committees while the same number highlighted liquidity and slippage forecasting.
The research found there are still concerns about using AI in digital asset investment processes - around a quarter (25%) pointed to data quality and manipulation as their biggest concern, while 20% cited cybersecurity or model risks and 17% pointed to regulatory uncertainty.