AI IN PRACTICE: HOW ASSET MANAGERS AND INSTITUTIONAL INVESTORS ARE USING AI IN 2025
Estimated reading time: 5min
AI in finance is a concrete enabler of productivity, insight, and control. As we enter 2026, AI-powered asset management and automated decision-making are redefining the daily workflows of both asset managers and institutional asset owners such as insurers and pension funds.
IN ASSET MANAGEMENT: AUTOMATION MEETS INTELLIGENCE
For asset managers, artificial intelligence in financial services is now embedded in portfolio construction, research, and reporting. Large firms deploy machine learning for portfolio optimization, using models that scan structured and unstructured data, earnings transcripts, economic indicators, sentiment feeds, to detect market signals.
More recently, generative AI in finance is being used to generate investment ideas, explain trades, and draft thematic exposures. Some managers now rely on AI copilots to accelerate portfolio reviews, rebalancing suggestions, and attribution analysis.
On the risk side, automated risk management in finance uses AI to detect early warning signs, stress test portfolios under complex scenarios, and monitor counterparty exposures in real time. AI models can capture nonlinear risks that traditional VaR models often miss.
AI fraud detection in finance is also gaining ground: ML algorithms continuously screen trading behaviors and flag anomalies for compliance teams.
Client reporting is another frontier. Generative AI assistants now produce portfolio summaries, investment commentaries, and ESG impact reports, all tailored to specific client profiles.
This plays directly into the trend of hyper-personalized financial services, where institutional clients demand more targeted insights.
IN INSURANCE AND PENSION FUNDS: AI BEHIND THE SCENES
Institutional investors—especially insurers and pension funds—are also scaling AI into their daily operations. In insurance, AI improves underwriting accuracy, speeds up claims management, and enhances fraud analytics. Some firms use predictive analytics in banking and insurance to estimate policyholder behavior or loss probabilities based on real-time inputs.
Generative AI is also helping compliance teams draft solvency reports or IFRS 17 disclosures — supporting AI compliance and regulation in a complex supervisory environment.
Pension funds and public institutions are applying AI to cash flow forecasting, longevity modelling, and strategic asset allocation. These models allow funds to better align assets with future liabilities while accounting for market stress and inflation shocks.
Meanwhile, AI robo-advisors and digital wealth management tools are becoming standard in member-facing interactions: chatbots guide users through benefits queries, and retirement calculators simulate outcomes using AI-driven projections.
GOVERNANCE: RISKS OF USING AI IN FINANCE
While operational gains are clear, challenges around explainable AI in finance and responsible AI banking remain top of mind.
Financial institutions must ensure that AI models are auditable, unbiased, and compliant with evolving regulations. Regulators like ESMA and EIOPA have repeatedly warned that poor data quality, lack of oversight, and “black box” logic can lead to spurious outcomes in both risk management and client servicing.
Firms now prioritize AI governance frameworks, complete with validation pipelines, audit trails, and human oversight. This is no longer optional, it’s part of earning trust from clients, regulators, and internal stakeholders alike.
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COMMON QUESTIONS ABOUT THIS TOPIC
Title
What is Artificial Intelligence (AI) in asset management?
AI refers to the use of machine-learning, natural-language and automation techniques to enhance portfolio construction, risk modelling, ESG analysis and operational efficiency.
Title
How can AI improve investment decision-making?
AI helps identify hidden patterns in large datasets, supports predictive modelling, improves scenario analysis, and enhances both risk and return insights compared to traditional statistical methods.
Title
What are the main use cases of AI for asset managers and asset owners?
Key use cases include: market prediction models, risk-indicator automation, ESG data extraction and scoring, anomaly detection, reporting automation, and client-personalisation tools.
Title
What are the biggest challenges when adopting AI in finance?
Challenges include data quality and governance, model transparency, bias mitigation, regulatory expectations for explainability, and the integration of AI outputs into existing investment processes.
Title
How should firms start integrating AI into their investment or risk framework?
They should begin with a clear use-case, ensure robust data governance, pilot models with measurable KPIs, embed human oversight, and scale gradually as reliability and internal governance mature.