Domain Knowledge-Enhanced Process Mining for Anomaly Detection in Commercial Bank Business Processes

Process anomaly detection in financial services systems is crucial for operational compliance and risk management. However, traditional process mining techniques frequently neglect the detection of significant low-frequency abnormalities due to their dependence on frequency and the inadequate incorporation of domain-specific knowledge. Therefore, we develop an enhanced process mining algorithm by incorporating a domain-specific follow-relationship matrix derived from standard operating procedures (SOPs). We empirically evaluated the effectiveness of the proposed algorithm based on real-world event logs from a corporate account-opening process conducted from January to December 2022 in a Chinese commercial bank. Additionally, we employed large language models (LLMs) for root cause analysis and process optimization recommendations. The empirical results demonstrate that the E-Heuristic Miner significantly outperforms traditional machine learning methods and process mining algorithms in process anomaly detection. Furthermore, the integration of LLMs provides promising capabilities in semantic reasoning and offers explainable optimization suggestions, enhancing decision-making support in complex financial scenarios. Our study significantly improves the precision of process anomaly detection in financial contexts by incorporating banking-specific domain knowledge into process mining algorithms. Meanwhile, it extends theoretical boundaries and the practical applicability of process mining in intelligent, semantic-aware financial service management.