期刊名称：Journal of the Association for Information Systems
印刷版ISSN：1536-9323
出版年度：2021
卷号：22
期号：1
页码：156-178
DOI：10.17705/1jais.00657
出版社：Association for Information Systems
摘要：We address the challenge of building an automated fraud detection system with robust classifiers that mitigate countermeasures from fraudsters in the field of information-based securities fraud. Our work involves developing design principles for robust fraud detection systems and presenting corresponding design features. We adopt an instrumentalist perspective that relies on theory-based linguistic features and ensemble learning concepts as justificatory knowledge for building robust classifiers. We perform a naive evaluation that assesses the classifiers’ performance to identify suspicious stock recommendations, and a robustness evaluation with a simulation that demonstrates a response to fraudster countermeasures. The results indicate that the use of theory-based linguistic features and ensemble learning can significantly increase the robustness of classifiers and contribute to the effectiveness of robust fraud detection. We discuss implications for supervisory authorities, industry, and individual users.
其他关键词：Fraud Detection, Market Manipulation, Design Principles, Text Mining, Data Mining, Instrumentalism, Ensemble Learning
