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Hierarchical Anomaly Detection and SHAP-Based Root-Cause Attribution for Robotic Process Automation Workflows

Proceedings of the International Conference on Business Excellence

Volume 20 (2026): Issue 1 (July 2026)

By: Yanka ALEKSANDROVA, Mihail RADEV, Mila GEORGIEVA and Desislava KOLEVA

Open Access

|Jul 2026

Articles in this issue

DOI: https://doi.org/10.2478/picbe-2026-0043 | Journal eISSN: 2558-9652

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Language: English

Page range: 538 - 552

Published on: Jul 16, 2026

Published by: Bucharest University of Economic Studies

In partnership with: Paradigm Publishing Services

Publication frequency: 1 issue per year

Keywords:

Robotic Process Automation,

hierarchical anomaly detection,

root cause analysis,

explainable artificial intelligence,

Isolation Forest,

Long Short-Term Memory Autoencoder,

operational diagnostics

Related subjects:

Business and economics,

Political economics,

Political economics, other,

Business management,

Business management, other,

Industrial chemistry,

Energy harvesting and conversion

© 2026 Yanka ALEKSANDROVA, Mihail RADEV, Mila GEORGIEVA, Desislava KOLEVA, published by Bucharest University of Economic Studies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 20 (2026): Issue 1 (July 2026)

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