Abstract
This study introduces a novel AI-powered Business Intelligence Dashboard System (AIBIDS) designed to detect and visualize calendar-based anomalies in cryptocurrency returns. Focusing on Bitcoin as a case study, the system integrates unsupervised machine learning algorithms to identify periods of abnormal market behavior across multiple temporal resolutions. The proposed system leverages a star-schema OLAP data warehouse, enabling real-time anomaly detection, dynamic visualization, and drill-down exploration of market irregularities. Empirical results confirm the presence of pronounced calendar effects in Bitcoin returns, such as heightened anomalies during Q1 and Q4, and reveal model-specific sensitivities to local versus global volatility. Our novel platform offers a practical, scalable innovation for investors, analysts, and regulators seeking to monitor cryptocurrency markets more effectively, and contributes to the emerging FinTech literature on AI-driven anomaly detection and behavioral market dynamics.
| Original language | English |
|---|---|
| Article number | 712 |
| Journal | Journal of Risk and Financial Management |
| Volume | 18 |
| Issue number | 12 |
| DOIs | |
| State | Published - 1 Dec 2025 |
| Externally published | Yes |
Keywords
- AI models
- anomaly detection
- autoencoder
- bitcoin
- business intelligence system
- generative AI
- learning
ASJC Scopus subject areas
- Accounting
- Business, Management and Accounting (miscellaneous)
- Finance
- Economics and Econometrics
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