Corporate social responsibility (CSR) and environmental, social, and governance (ESG) disclosures are critical for sustainable value creation. However, traditional evaluation methods struggle to quantify authentic performance and detect disclosure biases. In response, this study proposes an automated CSR polarity dictionary construction method that innovatively combines natural-language-processing technology and the multinomial inverse regression (MNIR) method. This method analyzes the correlations between corporate CSR reports and CSR ratings and constructs a dictionary that best reflects the CSR level of listed companies. We also used the CSR dictionary to construct a CSR disclosure level index for listed companies’ annual reports. This study reveals that CSR disclosure levels in annual reports expose manipulative disclosure practices and image management. However, this behavior has been proven to fail in generating excess returns for the company in the stock market. This phenomenon provides novel insights into corporate stock market performance management. In addition, the CSR disclosure level index is shown to effectively reflect the CSR level of enterprises in different industries and provides a theoretical reference for the social responsibility management of companies with different pollution levels. These findings facilitate efficient information release and strengthen ESG assessment frameworks through data-driven standardization.

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.

The efficiency and reliability of business processes in commercial banks are critical to financial stability and compliance. However, traditional evaluation methods that rely on retrospective qualitative assessments and static frameworks struggle to address the dynamic complexities inherent in modern banking operations. These approaches lack real-time monitoring, fail to leverage granular event log data, and overlook organizational interdependencies, hindering proactive risk management and optimization. To bridge these gaps, this study proposes a data-driven evaluation framework that integrates three core dimensions: efficiency, quality, and flexibility. We developed a hybrid analytical model by integrating process mining with DEMATEL-AHP to analyze a Chinese bank’s performance guarantee process, comparing pre- and post-centralization workflows. The analysis revealed that post-centralization processes exhibited improved flexibility but reductions in efficiency and quality. Moreover, the social network analysis highlighted structural shifts, including expanded audit participation and reduced departmental cohesion, contributing to inefficiencies. This study advances business process management by demonstrating that a data-driven process evaluation framework offers greater persuasiveness and methodological rigor than traditional qualitative approaches.

The implementation of the registration system reform in China has profoundly influenced the information disclosure practices of listed companies, with increasing scrutiny on the transparency and comprehensibility of corporate disclosures. The primary objective of information disclosure is to convey internal organizational information effectively to external stakeholders with public texts. However, the clarity and readability of these disclosures are significantly affected by the strategic motivations of the management. Based on this, this paper aims to construct a systematic readability assessment framework grounded in linguistic theories, integrating 14 sub-indicators across four dimensions: Morphology, Grammar, Semantics, and Multimodal. Using a hierarchical dimensionality reduction method, we develop a comprehensive readability index to evaluate readability level in the annual reports of Chinese listed companies. This study also examines the impact of managerial opportunism on the manipulation of readability in corporate disclosures. This paper selected more than 4000 listed companies as samples for empirical analysis. The empirical results have revealed that companies with poor operating performance are more likely to produce annual reports with lower readability levels, potentially employing obfuscation strategies to conceal unfavorable information. Moreover, there exists negative correlation between the readability of annual reports and managers' excess compensation, suggesting that managers may manipulate readability to pursue excess compensation. Meanwhile, readability manipulation may correlate with managerial structural attributes. This study highlights the critical importance of measuring the quality of information disclosure, because improved readability standards are essential for mitigating managerial opportunism and fostering greater transparency in information disclosure.