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How AI simulation accelerates growth in wealth and asset management

AI simulation enables wealth and asset managers to anticipate client behavior and make strategic decisions with greater speed and accuracy.


In brief
  • AI simulation is a new paradigm for strategic insight, allowing financial firms to test new propositions at scale using real-time client insights.
  • Simulation uses a powerful application of Agentic AI to model the decision-making behavior of real-world populations.
  • A pioneering test against market-leading research validates the strategic value of AI simulation for wealth and asset managers.

The age of guessing what consumers want is over. Welcome to the era of knowing what they'll actually do – before they do it. 

Traditional decision making is built on a fundamental flaw — by the time you gather intelligence, analyze it, and act upon it, the world has already changed. Worse still, the very methods used to generate that intelligence are skewed by hidden biases and gaps. What people say they will do does not match how they behave. The combination of delay and distortion leaves firms flying blind precisely when clarity matters the most.

Now, a new breed of artificial intelligence (AI) simulation technology is transforming strategic planning from periodic guesswork to continuous confidence. No longer confined to cutting costs or automating tasks, AI has matured into a strategic capability — able to supplement, and at times surpass, traditional research and strategic planning methods. Firms can test thousands of scenarios, predict market reactions, and optimize strategies in real time — not in months, but in hours.

The EY organization has pioneered the first application of AI simulation to strategic insights in the wealth and asset management industry. Using technology from Aaru — an AI simulation startup — we recreated our market-leading 2025 EY Global Wealth Research Report in a single day, showing a median correlation of 90%; findings that normally take six months.

Aaru AI simulation of the 2025 EY Global Wealth Research Report
correlation across 53 single-choice questions and 3,600 respondents globally

This isn't science fiction. It’s a completely new paradigm for strategic intelligence in financial services. AI simulation can fundamentally rewire how organizations sense, decide, and act. By combining real-world datasets with synthetic data to create behaviorally grounded agents, AI simulation enables organizations to test scenarios at scale before making strategic decisions. The question isn’t whether AI simulation will transform our industry, but whether firms will be using it or losing it.

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Image downloaded by Charlie Brewer at 9:52 on the 06/02/19
1

Chapter 1

The decision-making revolution: embracing AI simulation

AI simulation is reshaping decision-making for wealth managers, providing real-time insights and eliminating the guesswork in strategy.

Every executive knows the feeling — you're making billion-dollar decisions based on three-month-old information and guided by data about what customer surveys claim they’ll do — not what they will actually do. You launch, you hope, you wait — months later, you learn whether what those clients told you plays out.

But what if you could test every strategic option before committing? What if you could model the reactions of customers, competitors, employees and markets — not only through surveys or focus groups, but through behavioral simulation that captures how humans actually decide? What if your organization could sense and respond to market changes in real time, not in quarterly cycles? This isn't a thought experiment. It’s happening now.

AI simulation tracks real-world behavior, letting organizations explore how decisions and actions might unfold under different conditions, offering insight into likely outcomes instead of just analyzing the past.

At its core are synthetic populations — digital agents built from real-world data and, where needed, synthetic data — artificially generated datasets that mirror the statistical and behavioral patterns of real populations. Machine learning analyzes real-world data — demographics (e.g., sources from national censuses, the United Nations, the International Monetary Fund), economic and behavioral outcomes (e.g., sales, transactions, reports), and sentiment and preferences (e.g., social media platforms, product reviews) to determine which segments and attributes matter in a given scenario. It then constructs agents with characteristics like age, income, risk preferences and behavioral tendencies. Agents act according to these traits, leaving a traceable record of choices and trade-offs, producing a structured and data-driven model of how people behave in hypothetical situations.

Major organizations are already using AI simulation to transform decision-making. A leading luxury automobile manufacturer, for example, uses digital avatars to simulate its production line with hyper-realistic detail, allowing it to analyze worker behaviors and anticipate potential manufacturing issues before they arise.1  Interpublic Group uses Aaru to predict audience responses before campaigns launch.2  Similarly, Heartland Forward deployed Aaru’s simulation technology to gauge AI sentiment across 20 states3, replacing months of traditional polling with days of simulation.

AI simulation makes predicting customer and market behavior in real time more possible. Modeling behavior with synthetic populations and data-driven agents allows organizations to test scenarios and see likely outcomes before taking action. This isn’t an incremental improvement. It’s a genuine paradigm shift.

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2

Chapter 2

Understanding AI simulation and its impact on the industry

AI simulation offers a new way to understand client behavior, helping wealth managers adapt strategies with greater confidence and making timely, data-informed decisions.

AI simulation represents a fundamental breakthrough in strategic intelligence. Unlike traditional surveys, it doesn’t ask people what they say they would do — it models what they are likely to do. Instead of asking 1,000 people hypothetical questions, one can now simulate 100,000 digital personas that behave with human-level realism — in hours, not months.

AI simulation at scale
sample size of digital agents, generated in hours

These aren’t simple chatbots running through surveys. Using advanced language models and behavioral architectures, AI agents replicate the complex decision-making patterns of real populations. They evaluate options, weigh trade-offs, form opinions, and make choices based on embedded behavioral traits and environmental context. Every decision leaves a traceable logic trail, revealing not just what choice was made, but why.

Technology addresses a problem that has plagued decision-makers forever: the gap between what people say and what they actually do. Research shows that when asked hypothetical questions, people report willingness to pay a price three times higher than when actual money is involved.4  While 65% of people state a preference for sustainable products, actual purchases hover at 26%.5  In wealth management, investors say they are comfortable with risk — until real money is at stake.6

Traditional research draws its power from individuals, but its limitations lie in retrospective data and stated intentions. AI simulation, by contrast, adds a new richness by modeling behavior and predicting likely future actions, including the trade-offs and context that drive real-world decisions.

Think of it as creating a digital twin of your market — a living laboratory where you can test strategies, predict reactions and refine approaches before risking capital or reputation. It is the difference between navigating with a paper map and using GPS that shows traffic, suggests routes and reroutes you in real time.

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3

Chapter 3

From guesswork to certainty with real-time insights

Transforming decision-making processes enables organizations to move from guesswork to certainty by providing real-time insights that refine strategies.

With all the promise that synthetic data and AI simulation appear to offer, the EY organization wanted to put AI simulation to an ultimate test. 

We challenged Aaru to re-create our 2025 EY Global Wealth Research Report, one of the industry’s most comprehensive research initiatives spanning 3,600 affluent investors across more than 30 markets.7 Traditional fieldwork takes six months and is resource intensive. In just one day simulation survey results were correlated over 90%+ to the actual survey — an impressive benchmark for any predictive model. But more importantly, in areas where Aaru’s predictions diverged from our survey responses, the AI simulation proved more accurate in predicting real-world behavior. 

The inheritance loyalty myth

  • What people say (EY survey): 82% of heirs claim they’ll keep their parents' advisor.8
  • AI Simulation prediction: 43% retention rate.
  • Real-world data: Industry studies show 20% to 30% of heirs keep their parents’ advisor.9

This isn’t a survey failure — it’s a reflection of human nature. Behavioral science teaches us that people are unable to think in hypotheticals. It’s well understood that people tend to give socially acceptable responses when responding to market studies. They may be affected by survey fatigue or answer without thinking. Sometimes, people just lie. AI simulation’s behavioral modeling strips away these biases to reveal likely actions. For wealth and asset managers, this transforms strategic planning — intergenerational wealth retention isn’t automatic, it requires proactive relationship building. 

The consolidation contradiction

  • What people say: 69% prefer a single financial provider.
  • AI simulation prediction: 37% prefer a single provider.
  • Reality: Only 33% of high-net-worth individuals use one advisor alone.
2025 EY Global Wealth Research Report
claim to prefer a single financial provider, compared to 37% of agents, and 33% real-world

We can’t claim that AI simulation guarantees what people will do — future behavior is inherently uncertain. But by modeling behavioral patterns, preferences and trade-offs across synthetic populations, it provides structured foresight into how people are likely to act under new scenarios.

AI simulation captures behavioral insights often absent from traditional research. Clients express a preference for simplicity, but act on their desire for specialized experience. This insight reshapes how firms approach client acquisitions and retention.

The point is not just that AI simulation can replicate surveys quickly, although that potential is exciting. It is that AI simulation can complement traditional research and practical experience with advanced behavioral intelligence by modeling what people actually do, not what they claim they’ll do.

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4

Chapter 4

Transforming strategic planning with continuous intelligence

Shifting from periodic snapshots to continuous intelligence enhances strategic planning and improves the responsiveness to dynamic market change.

The true power of AI simulation isn’t speed – it's the shift from periodic snapshots to continuous intelligence. Consider what becomes possible when every strategic decision can be pre-tested:

  • Strategic planning transforms from annual cycles to continuous adaptation. Test market entry strategies across hundreds of scenarios. Refine pricing strategies in real time as a competitive dynamics shift. Predict customer defection before it happens and intervene proactively.

  • Product development shifts from launch-and-learn to learn-before-launch. Understand adoption curves before writing code. Test feature combinations across segments. Improve the user experience through thousands of simulated interactions.

  • M&A execution moves beyond financial modelling to behavioral prediction. Model how acquired customers will actually behave post-merger. Test integration strategies before Day One. Predict the cultural fit through simulated interactions.

  • Risk management evolves from reacting to anticipating. Stress-test strategies against black swan events. Understand the cascade effects before they cascade. Build resilience through simulated crisis scenarios.

With the digital population built, we can now rerun simulations whenever market conditions shift. Election results? Tariff announcements? Rate changes? Within 24 hours, we can project how investor sentiment and behavior are likely to change. No recruiting, no fieldwork, just instant, actionable intelligence.

The comparison to traditional approaches is stark, as shown below.

Traditional intelligence

AI simulation intelligence

Months to deliver

Hours to insights

Point-in-time snapshot

Continuous monitoring

Based on claimed behavior

Based on predicted actions

Limited sample sizes

Unlimited scale

Historical looking

Future focused

Personally identifiable information (PII) compliance burden

No PII required

Traditional decision-making is built on a 20th-century foundation — gather historical data, analyze trends, extrapolate from those trends and make informed projections. AI simulation offers a 21st-century alternative: create digital markets, test “infinite” scenarios and see outcomes before committing.

This isn’t an incremental improvement in intelligence. It's a fundamental transformation in how organizations take decisions. Just as GPS replaced paper maps and smartphones replaced phone booths, AI simulation shifts the probability curve, showing what people are likely to do.

The test against the global EY organization gold standard of wealth management research shows something profound — AI simulation doesn’t just accelerate research — it can deliver superior insights by modeling actual behavior rather than stated intentions. When billions of dollars and client relationships are at stake, the difference between what people say and what they do isn’t academic. It has real-world implications.

Within 18 months, AI simulation will shift from a competitive advantage to table stakes. Within three years, no major strategic decision will be made without it. Organizations that haven’t started by then will be left behind.

At the EY organization, we aren't just observing this revolution – in collaboration with our clients, we're designing it. Our proven methodology, demonstrated accuracy, and implementation experience make the theoretical practical and the future achievable.

The question isn’t whether AI simulation will transform strategic decision-making. It’s whether you’ll lead the transformation or remain on the sidelines.


Summary

AI simulation is revolutionizing decision-making for wealth managers by providing real-time insights into consumer behavior. Traditional methods often fall short, leading to guesswork and missed opportunities. By leveraging AI simulation, organizations can test various scenarios and optimize strategies proactively. This innovative approach not only enhances strategic planning but also positions wealth managers to respond effectively to market changes. As AI simulation continues to evolve, it is set to transform how firms operate, ensuring they stay ahead in a competitive landscape.

Contact us
Reach out to the EY AI Simulation team to see how this technology could work for you.

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