Constructing an Investment Scam Detection Model Based on Emotional Fluctuations Throughout the Investment Scam Life Cycle

ABSTRACTIn recent years, the COVID-19 pandemic has increased investment scams as individuals seek alternative investment opportunities. This study employs emotion analysis to examine the emotional expressions of scammers in investment scams and proposes an investment scam detection model to identify fraudulent behavior. Using data from seven real-world investment scam chat rooms, we perform a two-stage analysis to evaluate the emotional fluctuations of scammers and construct a machine learning-based investment scam detection model. Our findings reveal distinct patterns of emotional fluctuation in investment scam chat rooms according to the established Investment Scam Life Cycle (ISLC), which portrays the progression of fraudulent behavior. We confirm the applicability of machine learning in scam detection, and the emotion analysis presented in this study can assist researchers and fraud examiners in identifying scam scenarios and reducing financial losses. Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsChin KuoChin Kuo is a Section Chief Auditor at the Far Eastern Group in Taiwan. He specializes in operation and information security audits, fraud detections, and investigations. He is a Certified Fraud Examiner (CFE) and the director of the Association of Certified Fraud Examiners (ACFE) Taiwan chapter. He holds an MBA from the National Ilan University, Taiwan, and a Ph.D. from the Graduate Institute of Management at the National Taiwan University of Science and Technology, Taiwan. He is currently an Executive MBA student at Quantic School of Business and Technology, USA. His primary teaching and research interests include criminology, fraud schemes, anomaly behavior, and white-collar crime.Seng-Su TsangSeng-Su Tsang is Professor of Business Strategy in the Department of Business Administration at the National Taiwan University of Science and Technology since 2008. His current research interests include innovations and strategies in services involving artificial intelligence applications. He is also engaged with an international research team in developing business strategies with respect to retailing. He received BS in chemical engineering from Tsinghua University, Taiwan, MBA from Sun Yat-sen University, Taiwan, and Ph.D. in public policy and management from Carnegie Mellon University, USA. He is a member of Phi Tau Phi, branch of Sun Yat-sen University in 1989. He published peer-reviewed papers in areas including retailing, technology applications, and services innovation.