Purpose This paper aims to discuss the application of Big Data analytics to the brainstorming session in the current auditing standards. Design/methodology/approach The authors review the literature related to fraud, brainstorming sessions and Big Data, and propose a model that auditors can follow during the brainstorming sessions by applying Big Data analytics at different steps. Findings The existing audit practice aimed at identifying the fraud risk factors needs enhancement, due to the inefficient use of unstructured data. The brainstorming session provides a useful setting for such concern as it draws on collective wisdom and encourages idea generation. The integration of Big Data analytics into brainstorming can broaden the information size, strengthen the results from analytical procedures and facilitate auditors’ communication. In the model proposed, an audit team can use Big Data tools at every step of the brainstorming process, including initial data collection, data integration, fraud indicator identification, group meetings, conclusions and documentation. Originality/value The proposed model can both address the current issues contained in brainstorming (e.g. low-quality discussions and production blocking) and improve the overall effectiveness of fraud detection.

Purpose – This study aims to examine the value relevance of ethics information. Design/methodology/approach – This study adopts event study methodology to test the market’s reaction around the announcements of World’s Most Ethical Companies (WME), a ranking based on firms’ overall corporate social responsibility performance. The authors calculate the abnormal returns of firms on the WME lists to investigate how stockholders respond to the disclosure of ethical information. Findings – The authors find significant and positive abnormal returns around the announcements of the lists of ethical firms. Specifically, positive market reaction on the first day after the WME announcement (Day 1) is observed. Originality/value – This study contributes to the existing literature of the relationship between business ethics and firm value. The authors provide evidence that ethics can be aligned with firms’ financial goals. Further, this study is the first to use the WME announcement as a proxy for ethical firms.

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Microfinance institutions (MFIs), widely regarded as bankers to the poor, have extended their financial functions beyond lending to managing deposits. We empirically examine the influence of MFI deposit-taking on MFI financial performance. Using data of 1,301 MFIs worldwide, we find that an MFI's deposit level is an important determinant of its financial viability. However, the relationship is influenced by MFIs' institutional type (for-profit or nonprofit) and the legal environment (common law or civil law). The results suggest that the positive financial impact of deposits has not been fully realised, reflecting the need to further improve cost management and revenue generation.

This paper provides a detailed description of the recommendation system and collaborative filtering algorithm to optimize the English learning platform through the collaborative filtering algorithm and analyses the algorithmic principles and specific techniques of collaborative filtering. After introducing the recommendation system and collaborative filtering algorithm, this paper elaborates on the theoretical basis and technical principles of the recommendation algorithm based on cognitive ability and difficulty and provides an in-depth analysis of the design and implementation of the recommendation algorithm by combining cognitive diagnosis theory, readability formula, and English knowledge map, which provides a comprehensive and solid theoretical guidance and support for the application development of the online English learning platform. The system is tested by building a Spring Cloud platform, importing actual business data, focusing on the validation of the recommendation model, and connecting the recommendation system to the formal production system to analyse the recommendation effect. Compared with the original recommendation method, the online English learning platform designed and implemented in this paper based on the cognitive ability and difficulty collaborative filtering recommendation algorithm has a better recommendation effect. The system is proved to be well designed and has certain reference and guiding value for the whole web-based online learning platform and has a broader application prospect nowadays and in the future.