To gain financial data insights, the majority of family offices now turn to AI, according to new research from Ocorian. The global study reveals 86 percent of these private wealth groups are utilising AI to improve their daily operations and data analysis.
Representing a combined wealth of $119.37 billion, these organisations want machine learning to modernise their workflows. The technology offers practical benefits for institutions handling complex portfolios, particularly in detecting anomalies, streamlining reporting, and navigating strict regulatory frameworks.
Securing financial data insights via AI and system governance
Implementing these tools requires careful alignment with existing enterprise architectures. Financial institutions frequently rely on major cloud ecosystems, such as Microsoft Azure or Google Cloud, to provide the necessary computing power and security protocols for advanced data processing. By using these platforms, operations teams can deploy machine learning models that identify potential fraud patterns or compliance breaches much faster than manual reviews allow.
While 26 percent of surveyed wealth executives strongly agree that AI will reshape administration and boost performance within the next year, 72 percent expect the broader effects to materialise over a two to five-year horizon.
This cautious timeline reflects the reality of integrating complex algorithms into highly-regulated environments. Integrating new systems without disrupting daily client services presents a major challenge. Legacy data architectures often require heavy re-engineering before they can fully support predictive analytics.
Michael Harman, Commercial Director for the UK and Channel Islands at Ocorian, said: “Family offices are gradually adopting AI and technology as part of their operations and are particularly using it for data insights … there is a realisation that it will have a major impact and family offices need to start exploring the sector and will need support in making the transition.”
Balancing operational upgrades with capital exposure
Despite high operational adoption rates, direct capital allocation into the AI sector remains low. Only seven percent of respondents across 16 territories – including the UK, US, UAE, and Singapore – are currently seeking direct investment opportunities in such technology firms.
This current hesitation highlights a preference for using proven enterprise solutions rather than absorbing the venture-style risks associated with emerging startups. Leaders are focused on immediate operational stability and verifiable returns on investment.
However, this dynamic is likely to change rapidly over the next three years, as 74 percent of these organisations expect to increase their investments in digital assets. Within that group, 20 percent plan to increase their financial commitment to the sector dramatically.
Outsourcing the technical burden to established service providers allows institutions to benefit from enhanced fraud detection and compliance monitoring without directly managing the algorithmic infrastructure. Success will depend on establishing clean data pipelines and ensuring cross-functional teams understand how to interpret algorithmic outputs for risk assessment.
By prioritising secure and scalable cloud platforms, and focusing on specific operational pain points like regulatory reporting, financial leaders can effectively use these AI capabilities to bolster their data insights while maintaining the necessary oversight required in modern wealth management.
See also: AI agents enter banking roles at Bank of America
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