Fraud Analytics in Action
Data Science and Machine Learning Techniques for Detecting Fraud in the Digital Age
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Beschreibung
This book is a comprehensive guide designed for professionals in high-risk sectors, bridging the gap between traditional auditing methods and the modern digital landscape. This book offers innovative solutions, practical Python coding exercises, and real-world scenarios, equipping readers with invaluable insights into fraud analytics. Tailored for internal auditors, data scientists, and analysts, it's an essential resource for navigating the complexities of fraud in the digital era.
Abdelrahim Aqqad has more than 15 years of experience as a Chief Internal Auditor alongside expertise in governance, risk management, internal control, and process re-engineering. He has authored numerous articles and spoken at various conferences, establishing himself as a thought leader in fraud analytics and risk management.
This book is a comprehensive guide designed for professionals in high-risk sectors, bridging the gap between traditional auditing methods and the modern digital landscape. This book offers innovative solutions, practical Python coding exercises, and real-world scenarios, equipping readers with invaluable insights into fraud analytics. Tailored for internal auditors, data scientists, and analysts, it's an essential resource for navigating the complexities of fraud in the digital era.
Brings together machine learning, data science, and practical fraud-risk methods Includes Python code, sample datasets, and complete analytical workflows Connects each fraud pattern to the right detection technique—anomaly detection, clustering, social network analysis, NLP
Autor*in
Abdelrahim Al Aqqad
Themen in »Fraud Analytics in Action«
Fraud Analytics Python Coding Digital Transformation Cybersecurity Financial Technology Machine Learning Financial Fraud Risk Management Data Science Internal Auditing