Statistical methods and data mining techniques, if used correctly, can help crime detection and prevention. The three sections of Data Mining and Crime Analysis in Financial Markets present the methods, techniques, and approaches for recognizing, analyzing, and ultimately detecting and preventing financial frauds, especially complex and sophisticated ones that characterize modern financial markets. The first two appeal to readers with technical backgrounds, describing. the data analysis and ways to manipulate markets and commit crimes. The third section gives life to the data and crimes through a series of interviews with bankers, regulators, lawyers, investigators, rogue traders, and others. Sharply focused on analyzing the origin of a crime from an economic perspective, Data Mining and Crime Analysis in Financial Markets shows Big Data in action, noting both pros and cons of the approach. Provides an analytical/empirical approach to financial crime investigation, including data sources, data manipulation, and conclusions that data can provide Emphasizes case studies, primarily with experts, traders, and investigators worldwideUses R for statistical examples INDICE: PrologueShort history of financial marketsOrigin of crime on Wall StreetTypologies of crime on financial marketsModern financial crimeForensic statistics as an investigation tool (Theoretical background)Investigating crime on financial markets (Empirical studies with applications in R)Case studies (Applications in R)Preventing crime and preserving markets' integrityEpilogue
- ISBN: 978-0-12-801221-5
- Editorial: Academic Press
- Encuadernacion: Cartoné
- Páginas: 312
- Fecha Publicación: 01/08/2015
- Nº Volúmenes: 1
- Idioma: Inglés