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Challenges

Why agent-based modeling?

There are several market dynamics that traditional methods struggle to model, such as contagion effects, feedback loops and structural changes.

Agent-based models attempt to capture the behavior and interaction of individual entities within an environment and can recreate complex dynamics.

This means that with agent-based modeling, you can solve problems where traditional methods are not sufficient, such as risk management, market execution or financial crime analytics.

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ABM for market simulation

Model realistic market dynamics with a next generation, high-fidelity agent-based market simulator.

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ABM for FinCrime analytics

Minimize financial crime while reducing cost in a GDPR compliant virtual environment.

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ABM for risk management

Run multiple what-if scenarios to make better informed, forward-looking business decisions.

Agent-based modeling with Simudyne
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As its adoption becomes more widespread, Simudyne’s platform will ultimately help cultivate a stronger, more efficient tech-enabled financial services sector.
Andy Challis
MD, principal investments at Barclays Bank
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