Human-Centered LLM-Agent System for Detecting Anomalous Digital Asset Transactions

• URL: http://arxiv.org/abs/2510.20102v1
• Date: Thu, 23 Oct 2025 01:04:36 GMT
• Title: Human-Centered LLM-Agent System for Detecting Anomalous Digital Asset Transactions
• Abstract summary: We present HCLA, a human-centered multi-agent system for anomaly detection in digital asset transactions.<n>The system links three roles: Parsing, Detection, and Explanation, into a conversational workflow that lets non-experts ask questions in natural language.<n>We describe the architecture, interaction loop, dataset, evaluation protocol, and limitations, and discuss how a human-in-the-loop design improves transparency and trust in financial forensics.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present HCLA, a human-centered multi-agent system for anomaly detection in digital asset transactions. The system links three roles: Parsing, Detection, and Explanation, into a conversational workflow that lets non-experts ask questions in natural language, inspect structured analytics, and obtain context-aware rationales. Implemented with an open-source web UI, HCLA translates user intents into a schema for a classical detector (XGBoost in our prototype) and returns narrative explanations grounded in the underlying features. On a labeled Bitcoin mixing dataset (Wasabi Wallet, 2020-2024), the baseline detector reaches strong accuracy, while HCLA adds interpretability and interactive refinement. We describe the architecture, interaction loop, dataset, evaluation protocol, and limitations, and discuss how a human-in-the-loop design improves transparency and trust in financial forensics.

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