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Fraud Analytics Using Descriptive, Predictive And Social Network Techniques: A Guide To Data Science For Fraud Detection

TLDR
Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques helps you stop fraud in its tracks, and eliminate the opportunities for future occurrence.
Abstract
Detect fraud earlier to mitigate loss and prevent cascading damage Fraud Analytics Using Descriptive, Predictive, and Social Network Techniquesis an authoritative guidebook for setting up a comprehensive fraud detection analytics solution. Early detection is a key factor in mitigating fraud damage, but it involves more specialized techniques than detecting fraud at the more advanced stages. This invaluable guide details both the theory and technical aspects of these techniques, and provides expert insight into streamlining implementation. Coverage includes data gathering, preprocessing, model building, and post-implementation, with comprehensive guidance on various learning techniques and the data types utilized by each. These techniques are effective for fraud detection across industry boundaries, including applications in insurance fraud, credit card fraud, anti-money laundering, healthcare fraud, telecommunications fraud, click fraud, tax evasion, and more, giving you a highly practical framework for fraud prevention. It is estimated that a typical organization loses about 5% of its revenue to fraud every year. More effective fraud detection is possible, and this book describes the various analytical techniques your organization must implement to put a stop to the revenue leak. Examine fraud patterns in historical data Utilize labeled, unlabeled, and networked data Detect fraud before the damage cascades Reduce losses, increase recovery, and tighten security The longer fraud is allowed to go on, the more harm it causes. It expands exponentially, sending ripples of damage throughout the organization, and becomes more and more complex to track, stop, and reverse. Fraud prevention relies on early and effective fraud detection, enabled by the techniques discussed here. Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques helps you stop fraud in its tracks, and eliminate the opportunities for future occurrence.

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Citations
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Journal ArticleDOI

Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy

TL;DR: A formalization of the fraud-detection problem is proposed that realistically describes the operating conditions of FDSs that everyday analyze massive streams of credit card transactions and a novel learning strategy is designed and assessed that effectively addresses class imbalance, concept drift, and verification latency.
Journal ArticleDOI

The value of big data for credit scoring: Enhancing financial inclusion using mobile phone data and social network analytics

TL;DR: The results show that combining call-detail records with traditional data in credit scoring models significantly increases their performance when measured in AUC, and the calling behavior features are the most predictive in other models, both in terms of statistical and economic performance.
Book

Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner

TL;DR: This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining.
Journal ArticleDOI

A new direction in social network analysis: Online social network analysis problems and applications

TL;DR: This study is original by presenting an important source of research by explaining the problems of online social network and the studies performed in this area and a reference work for researchers interested in analyzingOnline social network data and social network problems.
Journal ArticleDOI

Big data analytics to identify illegal construction waste dumping: A Hong Kong study

TL;DR: Wang et al. as discussed by the authors used behavioral indicators and up-to-date big data analytics to identify possible drivers for illegal dumping (e.g., long queuing times) and produced a list of 546 waste hauling trucks suspected of involvement in illegal dumping.
References
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Journal ArticleDOI

Statistical Fraud Detection: A Review

TL;DR: This work describes the tools available for statistical fraud detection and the areas in which fraud detection technologies are most used, and statistics and machine learning provide effective technologies for fraud detection.
Journal ArticleDOI

Adaptive Fraud Detection

TL;DR: This paper uses a rule-learning program to uncover indicators of fraudulent behavior from a large database of customer transactions, which are used to create a set of monitors, which profile legitimate customer behavior and indicate anomalies.
Journal ArticleDOI

New insights into churn prediction in the telecommunication sector: a profit driven data mining approach

TL;DR: A novel, profit centric performance measure is developed, by calculating the maximum profit that can be generated by including the optimal fraction of customers with the highest predicted probabilities to attrite in a retention campaign.
Journal ArticleDOI

Horses for Courses’ in demand forecasting

TL;DR: It is found that forecasting accuracy is influenced as follows: for fast-moving data, cycle and randomness have the biggest (negative) effect and the longer the forecasting horizon, the more accuracy decreases, and for intermittent data, inter-demand interval has bigger impact than the coefficient of variation.
Trending Questions (1)
What is the fraud detection rate of Social Network Analysis?

The fraud detection rate of Social Network Analysis is not mentioned in the provided information.