From Information to Delegation: Mapping Human-AI Financial Decision Making
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Abstract (arXiv)
As AI increasingly participates in human decision making, understanding how decision-making authority is distributed between humans and AI has become a fundamental behavioural question. We introduce a behavioural measurement framework combining intent and delegated decision authority to quantify what consumers seek from AI and how much decision-making authority they assign to it. Applied to 1.5 million real-world ChatGPT and Gemini interactions from 6,304 users in the United States and India, we find that financial services are already a substantial AI use case. Consumers overwhelmingly use AI to retrieve information and shape financial judgement, while delegation of financial execution remains rare. By shifting attention from conversation topics to delegated decision authority, this work establishes a behavioural baseline for measuring the transition to increasingly agentic AI.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 32 references that are in the map. First posted on arXiv 3 Aug 2026.
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References · 9
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2020Longformer: The Long-Document Transformercited by 3,362
- 2020Big Bird: Transformers for Longer Sequencescited by 1,673
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- 2023GPT has become financially literate: Insights from financial literacy tests of GPT and a preliminary test of how people use it as a source of advicecited by 7
- 2025Financial Named Entity Recognition: How Far Can LLM Go?cited by 7
- 2025Synthesizing Behaviorally-Grounded Reasoning Chains: A Data-Generation Framework for Personal Finance LLMscited by 1
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