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Unlocking the Power of Fraud Detection: Unveiling Fraud Analytics Using Descriptive Predictive and Social Network Techniques

Emanuel Bell

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Published in Fraud Analytics Using Descriptive Predictive And Social Network Techniques: A Guide To Data Science For Fraud Detection (Wiley And SAS Business Series)

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Fraud is a pervasive threat that undermines the integrity of businesses and economies worldwide. From financial crimes to healthcare scams, the consequences of fraudulent activities are far-reaching, causing significant financial losses, reputational damage, and erosion of trust. To combat this insidious challenge, organizations are increasingly turning to advanced fraud analytics techniques to detect and prevent fraudulent activities proactively.

Fraud Analytics: A Multidisciplinary Approach

Fraud analytics involves leveraging data analytics, statistical modeling, and technology to identify and analyze patterns indicative of fraudulent behavior. It combines elements from diverse disciplines, including data science, machine learning, and social network analysis, to provide a comprehensive understanding of the fraud landscape.

Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection (Wiley and SAS Business Series)

by Bart Baesens

4 out of 5

Language	:	English
File size	:	16636 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	382 pages
Lending	:	Enabled

Descriptive Predictive and Social Network Techniques

The book "Fraud Analytics Using Descriptive Predictive and Social Network Techniques" delves into the intricacies of using advanced techniques to uncover fraud. It provides a detailed examination of:

Descriptive Analytics

* Identifying anomalies and outliers in data to pinpoint potential fraudulent transactions. * Visualizing data to detect patterns and trends that may indicate fraudulent activity.

Predictive Analytics

* Building predictive models to forecast the likelihood of fraud based on historical data and behavioral patterns. * Utilizing machine learning algorithms to classify transactions as fraudulent or legitimate.

Social Network Analysis

* Mapping relationships between individuals and entities to reveal hidden connections and potential collusions. * Identifying clusters and communities that may facilitate fraudulent activities.

Key Features of the Book

* Comprehensive Coverage: Explores a wide range of fraud analytics techniques, providing a thorough understanding of the subject. * Practical Insights: Offers real-world case studies and examples to illustrate the application of fraud analytics in various industries. * Technical Depth: Provides in-depth explanations of data mining, modeling, and social network analysis algorithms. * Current Research: Discusses the latest advancements and emerging trends in fraud analytics. * Interactive Exercises: Includes interactive exercises and assignments to reinforce key concepts and enhance learning.

Benefits of Fraud Analytics

* Improved Fraud Detection: Enhanced detection capabilities lead to faster identification and prevention of fraudulent activities. * Reduced Financial Losses: Proactive detection minimizes financial losses and protects organizational assets. * Enhanced Reputation: Effective fraud prevention builds trust and safeguards an organization's reputation. * Optimized Investigations: Analytics-driven investigations accelerate the process and provide deeper insights for law enforcement and fraud examiners. * Regulatory Compliance: Adherence to regulatory requirements and industry best practices for fraud detection.

"Fraud Analytics Using Descriptive Predictive and Social Network Techniques" is an indispensable resource for professionals seeking to enhance their fraud detection capabilities. Its in-depth coverage, practical insights, and technical depth empower readers with the knowledge and skills to combat fraud effectively. By embracing the advanced techniques outlined in this book, organizations can safeguard their operations, protect their assets, and contribute to a more secure financial landscape.

Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection (Wiley and SAS Business Series)

by Bart Baesens

4 out of 5