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3 books on AI for Fraud Prevention [PDF]
Updated: May 19, 2024 | 20 |
Books on AI for Fraud Prevention are invaluable resources for startups specializing in developing AI solutions for fraud detection and prevention. These resources offer a comprehensive foundation, covering various aspects of fraud detection, from anomaly detection and pattern recognition to machine learning algorithms and data analytics. They delve into the complexities of identifying fraudulent activities across various industries, emphasizing the significance of real-time monitoring and rapid response. Moreover, these books often include practical examples, case studies, and best practices, allowing startups to fine-tune their fraud prevention systems and adapt to ever-evolving fraud tactics.
1. Machine Learning Approach to Detect Fraudulent Banking Transactions
2022 by Riwaj Kharel
This book delves into the feasibility of utilizing a machine learning algorithm for the purpose of identifying fraudulent banking transactions and explores the mechanics of constructing a fraud management system grounded in machine learning principles. Traditional approaches to fraud detection in financial institutions have predominantly relied on rule-based systems coupled with manual assessments. While these systems have historically exhibited commendable performance, the evolving sophistication of fraudsters has introduced a degree of unpredictability into their outcomes. Fraud encompasses a multitude of methods, frequently characterized by repetitive patterns, making pattern recognition a central focus of effective fraud detection. Data analysts, for instance, can combat insurance fraud by developing algorithms designed to discern trends and anomalies. Machine learning techniques constitute a key tool in the fraud detection arsenal, with data mining being particularly adept at classifying, grouping, and segmenting data to scrutinize vast numbers of transactions in search of discernible patterns indicative of fraudulent activity. This book further delves into the application of machine learning methods for fraud detection through a comprehensive case study and an analysis based on Kaggle datasets.
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2. Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection
2015 by Bart Baesens, Veronique Van Vlasselaer, Wouter Verbeke
"Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection" serves as an authoritative handbook for the establishment of a robust fraud detection analytics system, enabling the early detection of fraud to mitigate losses and prevent further damage. Detecting fraud in its initial stages requires specialized techniques beyond those employed for identifying advanced fraud scenarios. This book meticulously covers both the theoretical foundations and the technical intricacies of these methods, offering valuable insights into their efficient implementation. The comprehensive content encompasses data collection, preprocessing, model development, and post-implementation phases, providing expert guidance on various learning approaches and the specific data types relevant to each. These techniques are versatile and applicable across diverse industries, encompassing domains such as insurance fraud, credit card fraud, anti-money laundering, healthcare fraud, telecommunications fraud, click fraud, tax evasion, and more, providing a highly pragmatic framework for bolstering fraud prevention efforts. It's worth noting that, on average, organizations lose approximately 5% of their annual revenue to fraud."
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3. Fraud and Fraud Detection, + Website: A Data Analytics Approach
2014 by Sunder Gee
The book "Fraud and Fraud Detection, + Website: A Data Analytics Approach" commences by providing an overview of major fraud categories, followed by an in-depth exploration of computerized examinations capable of uncovering these fraudulent activities. Readers will acquire proficiency in utilizing advanced data analysis techniques, including the implementation of automation scripts, to enhance the detection of anomalies necessitating further scrutiny. The accompanying website grants access to a demo version of IDEA, along with sample scripts that empower readers to promptly apply the methods elucidated in the book. Given the substantial growth in electronic databases within business systems, fueled by the era of big data, fraudulent transactions can readily go unnoticed in these vast datasets. "Fraud and Fraud Detection" equips readers with data analytics competencies to expose these irregularities. The book offers comprehensive guidance, featuring step-by-step instructions and pragmatic insights, to bolster the audit and investigative processes. Readers will gain the ability to comprehend various facets of fraud and the corresponding detection methodologies, identify anomalies and high-risk areas using computerized tools, and formulate a systematic approach for fraud detection through data analytics. Furthermore, the book highlights the utilization of IDEA software to automate detection and identification procedures. The delineation of detection techniques for each fraud category renders this book indispensable for students and emerging fraud prevention practitioners, while the step-by-step instructions on automation and advanced analytics will prove invaluable even to seasoned examiners. In an environment where datasets are experiencing exponential growth, enhancing both the speed and sensitivity of detection becomes paramount for fraud professionals seeking to maintain their edge. "Fraud and Fraud Detection" serves as a comprehensive guide to achieving more efficient and effective fraud identification.
Download PDF
How to download PDF:
1. Install Google Books Downloader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
1. Machine Learning Approach to Detect Fraudulent Banking Transactions
2022 by Riwaj Kharel
This book delves into the feasibility of utilizing a machine learning algorithm for the purpose of identifying fraudulent banking transactions and explores the mechanics of constructing a fraud management system grounded in machine learning principles. Traditional approaches to fraud detection in financial institutions have predominantly relied on rule-based systems coupled with manual assessments. While these systems have historically exhibited commendable performance, the evolving sophistication of fraudsters has introduced a degree of unpredictability into their outcomes. Fraud encompasses a multitude of methods, frequently characterized by repetitive patterns, making pattern recognition a central focus of effective fraud detection. Data analysts, for instance, can combat insurance fraud by developing algorithms designed to discern trends and anomalies. Machine learning techniques constitute a key tool in the fraud detection arsenal, with data mining being particularly adept at classifying, grouping, and segmenting data to scrutinize vast numbers of transactions in search of discernible patterns indicative of fraudulent activity. This book further delves into the application of machine learning methods for fraud detection through a comprehensive case study and an analysis based on Kaggle datasets.
Download PDF
2. Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection
2015 by Bart Baesens, Veronique Van Vlasselaer, Wouter Verbeke
"Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection" serves as an authoritative handbook for the establishment of a robust fraud detection analytics system, enabling the early detection of fraud to mitigate losses and prevent further damage. Detecting fraud in its initial stages requires specialized techniques beyond those employed for identifying advanced fraud scenarios. This book meticulously covers both the theoretical foundations and the technical intricacies of these methods, offering valuable insights into their efficient implementation. The comprehensive content encompasses data collection, preprocessing, model development, and post-implementation phases, providing expert guidance on various learning approaches and the specific data types relevant to each. These techniques are versatile and applicable across diverse industries, encompassing domains such as insurance fraud, credit card fraud, anti-money laundering, healthcare fraud, telecommunications fraud, click fraud, tax evasion, and more, providing a highly pragmatic framework for bolstering fraud prevention efforts. It's worth noting that, on average, organizations lose approximately 5% of their annual revenue to fraud."
Download PDF
3. Fraud and Fraud Detection, + Website: A Data Analytics Approach
2014 by Sunder Gee
The book "Fraud and Fraud Detection, + Website: A Data Analytics Approach" commences by providing an overview of major fraud categories, followed by an in-depth exploration of computerized examinations capable of uncovering these fraudulent activities. Readers will acquire proficiency in utilizing advanced data analysis techniques, including the implementation of automation scripts, to enhance the detection of anomalies necessitating further scrutiny. The accompanying website grants access to a demo version of IDEA, along with sample scripts that empower readers to promptly apply the methods elucidated in the book. Given the substantial growth in electronic databases within business systems, fueled by the era of big data, fraudulent transactions can readily go unnoticed in these vast datasets. "Fraud and Fraud Detection" equips readers with data analytics competencies to expose these irregularities. The book offers comprehensive guidance, featuring step-by-step instructions and pragmatic insights, to bolster the audit and investigative processes. Readers will gain the ability to comprehend various facets of fraud and the corresponding detection methodologies, identify anomalies and high-risk areas using computerized tools, and formulate a systematic approach for fraud detection through data analytics. Furthermore, the book highlights the utilization of IDEA software to automate detection and identification procedures. The delineation of detection techniques for each fraud category renders this book indispensable for students and emerging fraud prevention practitioners, while the step-by-step instructions on automation and advanced analytics will prove invaluable even to seasoned examiners. In an environment where datasets are experiencing exponential growth, enhancing both the speed and sensitivity of detection becomes paramount for fraud professionals seeking to maintain their edge. "Fraud and Fraud Detection" serves as a comprehensive guide to achieving more efficient and effective fraud identification.
Download PDF
How to download PDF:
1. Install Google Books Downloader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
