Assessing the Benefits of Using Cost-Sensitive Machine Learning Methods in Fraud Detection: An Auditor-Centric Approach

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Keywords	Fraud detection, Cost-sensitive learning, Financial restatements, Auditing cost, Machine learning
Thesis Advisor	Peek, E., Madelaine, Alexandre
Persistent URL	hdl.handle.net/2105/71939
Series	Business Analytics & Management
Organisation	Rotterdam School of Management
Citation APA Style AAA Style APA Style Cell Style Chicago Style Harvard Style IEEE Style MLA Style Nature Style Vancouver Style American-Institute-of-Physics Style Council-of-Science-Editors Style BibTex Format Endnote Format RIS Format CSL Format DOIs only Format	van Remmerden, Bas. (2024, July 2). Assessing the Benefits of Using Cost-Sensitive Machine Learning Methods in Fraud Detection: An Auditor-Centric Approach. Business Analytics & Management. Retrieved from http://hdl.handle.net/2105/71939

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