Automated Detection of Deviations in Bankruptcy Processes Using Process Mining
This study investigates the application of process mining (PM) techniques for the analysis of bankruptcy processes based on digital trace logs. The primary objective is to improve transparency, efficiency, and regulatory compliance within bankruptcy proceedings. An automated framework is proposed th...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10955460/ |
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| Summary: | This study investigates the application of process mining (PM) techniques for the analysis of bankruptcy processes based on digital trace logs. The primary objective is to improve transparency, efficiency, and regulatory compliance within bankruptcy proceedings. An automated framework is proposed that addresses three key tasks: 1) the detection of deviations in process execution, 2) the identification of root causes behind these deviations, and 3) the verification of process conformance to predefined regulatory standards. Event logs are used to identify inefficiencies, bottlenecks, and instances of non-compliance with expected process flows, such as delays, irregularities, and resource misallocations. Root cause analysis is employed to pinpoint factors influencing process outcomes, while conformance checking ensures alignment with regulatory models. The framework is evaluated using real-world bankruptcy case data, demonstrating its capability to detect and analyze process inefficiencies. |
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| ISSN: | 2169-3536 |