BFSI insights

Benchmarking LLM Agents for Wealth-Management Workflows

Published 1 Dec 2025 ยท arxiv.org
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Overview

Rory Milsom's research evaluates large language model (LLM) agents in wealth-management workflows, focusing on their ability to perform tasks accurately and economically. The study introduces a finance-focused environment and benchmarks 12 task-pairs for wealth management assistants.

Key Insights

  • Workflow Reliability: LLM agents are more limited by workflow reliability than mathematical reasoning.
  • Autonomy Impact: The level of autonomy significantly affects agent performance.
  • Benchmarking Challenges: Incorrect evaluation of models has hindered effective benchmarking.

Why It Matters

This research is crucial for financial services, particularly asset management, as it explores the potential of AI to improve efficiency in wealth management tasks.

Actionable Implications

  • Evaluate the integration of LLM agents in wealth management workflows.
  • Focus on improving workflow reliability and autonomy in AI systems.
  • Reassess current benchmarking methods for AI models.
researcher article financial-services-wealth-management financial-services cross-bfsi risk technology strategy
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