LEADER 04024nam 22005415 450 001 9911031633503321 005 20251003131001.0 010 $a981-9508-40-1 024 7 $a10.1007/978-981-95-0840-2 035 $a(MiAaPQ)EBC32328205 035 $a(Au-PeEL)EBL32328205 035 $a(CKB)41543265700041 035 $a(DE-He213)978-981-95-0840-2 035 $a(OCoLC)1546219557 035 $a(EXLCZ)9941543265700041 100 $a20251003d2025 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aFinancial Fraud Detection Using Machine Learning /$fby Xiyuan Ma, Desheng Wu 205 $a1st ed. 2025. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2025. 215 $a1 online resource (231 pages) 225 1 $aAI for Risks,$x2731-6335 311 08$a981-9508-39-8 327 $a Introduction -- The Definition of Financial Fraud -- The Basic Theory of Financial Fraud -- Financial Fraud Litigation and Forensic Accounting -- Resampling Techniques and Feature Selection -- Detection Models and Applications -- Financial Fraud Detection Based on Litigation and Resampling Methods -- Financial Fraud Detection Based on Feature Selection and the GONE Framework -- Financial Fraud Detection Based on Multi-Source Data -- The Classical Case of Financial Fraud. 330 $aThis book serves as a comprehensive guide to learning various aspects of financial fraud, encompassing the related research, the current situation, potential causes, implementation process, detection methods, regulatory penalties and management challenges in publicly listed companies. In this book, readers learn about the fraudulent practices that may occur in corporate operations, the executing mechanisms, an identifying indicators framework, and diverse detection methods including qualitative and quantitative models. Quantitative models include discriminant analysis, econometric analysis, and machine learning (ML) models. This book highlights the application of ML algorithms to detect financial fraud detection and discusses their limitations, such as high false-positive costs, delayed detection, the demand for interdisciplinary expertise, dependency on specific application scenarios, and issues with fraud data quality. Each related chapter provides a structured overview of the problems addressed, the algorithms used, experimental result and comparisons. Additionally, this book examines the cost-benefit trade-offs faced by companies engaging in financial fraud, considering factors such as ethical dilemmas, opportunities, practical needs, exposure risks, and litigation costs. This book is written for financial regulation institutions, business leaders, auditors, academics, and anyone interested in financial fraud detection. It offers practical insights into effectively preventing and controlling financial fraud and an overview of the latest advancements in ML technologies. Through real-world case studies, readers will gain a deeper understanding of the financial fraud, how ML can be used to detect it, as well as its pitfalls and limitations. Overall, this book bridges the gap between theory and application, equipping readers to understand how to detect financial fraud with the power of accounting and ML in the modern business environment. 410 0$aAI for Risks,$x2731-6335 606 $aFinancial risk management 606 $aRisk management 606 $aRisk Management 606 $aIT Risk Management 615 0$aFinancial risk management. 615 0$aRisk management. 615 14$aRisk Management. 615 24$aIT Risk Management. 676 $a658.155 700 $aMa$b Xiyuan$01851413 701 $aWu$b Desheng$01435928 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911031633503321 996 $aFinancial Fraud Detection Using Machine Learning$94445225 997 $aUNINA