Human-Centered LLM-Agent System for Detecting Anomalous Digital Asset Transactions
Gyuyeon Na, Minjung Park, Hyeonjeong Cha, Sangmi Chai

TL;DR
HCLA is a human-centered multi-agent system that enhances interpretability and accountability in digital asset transaction anomaly detection by reconstructing expert reasoning through natural language interaction and structured evidence analysis.
Contribution
The paper introduces HCLA, a novel human-centered multi-agent framework that improves transparency and decision traceability in cryptocurrency anomaly detection, emphasizing accountability over traditional explainability.
Findings
HCLA improves interpretability and transparency in anomaly detection.
The system achieves strong predictive accuracy with enhanced user interaction.
It effectively reconstructs expert reasoning aligned with regulatory standards.
Abstract
We present HCLA, a human-centered multi-agent system for anomaly detection in digital-asset transactions. The system integrates three cognitively aligned roles: Rule Abstraction, Evidence Scoring, and Expert-Style Justification. These roles operate in a conversational workflow that enables non-experts to express analytical intent in natural language, inspect structured risk evidence, and obtain traceable, context-aware reasoning. Implemented with an open-source, web-based interface, HCLA translates user intent into explicit analytical rules, applies classical anomaly detectors to quantify evidential risk, and reconstructs expert-style justifications grounded in observable transactional signals. Experiments on a cryptocurrency anomaly dataset show that, while the underlying detector achieves strong predictive accuracy, HCLA substantially improves interpretability, interaction, and…
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Taxonomy
TopicsBlockchain Technology Applications and Security · Explainable Artificial Intelligence (XAI) · Financial Distress and Bankruptcy Prediction
