Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection (Hardcover)
暫譯: 詐騙分析:使用描述性、預測性及社交網絡技術的數據科學指南(精裝版)
Bart Baesens, Veronique Van Vlasselaer, Wouter Verbeke
- 出版商: Wiley
- 出版日期: 2015-08-17
- 定價: $1,860
- 售價: 8.0 折 $1,488
- 語言: 英文
- 頁數: 400
- 裝訂: Hardcover
- ISBN: 1119133122
- ISBN-13: 9781119133124
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相關分類:
Data Science、Machine Learning
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商品描述
Detect fraud earlier to mitigate loss and prevent cascading damage
Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques is 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.
商品描述(中文翻譯)
**及早偵測詐騙以減輕損失並防止連鎖損害**
《使用描述性、預測性和社交網絡技術的詐騙分析》是一本權威指南,旨在建立全面的詐騙偵測分析解決方案。及早偵測是減輕詐騙損害的關鍵因素,但這需要比在更高階段偵測詐騙更專業的技術。本指南詳細介紹了這些技術的理論和技術方面,並提供專家的見解以簡化實施過程。內容涵蓋數據收集、預處理、模型建立和實施後的步驟,並對各種學習技術及其所使用的數據類型提供全面的指導。這些技術在各行各業的詐騙偵測中都有效,包括保險詐騙、信用卡詐騙、反洗錢、醫療詐騙、電信詐騙、點擊詐騙、逃稅等應用,為您提供一個非常實用的詐騙預防框架。
據估計,典型組織每年因詐騙損失約5%的收入。更有效的詐騙偵測是可能的,本書描述了您的組織必須實施的各種分析技術,以阻止收入流失。
- 檢查歷史數據中的詐騙模式
- 利用標記數據、未標記數據和網絡數據
- 在損害擴大之前偵測詐騙
- 減少損失、增加回收並加強安全性
詐騙持續的時間越長,造成的傷害就越大。它以指數方式擴大,對整個組織造成連鎖損害,並變得越來越複雜,難以追蹤、停止和逆轉。詐騙預防依賴於早期和有效的詐騙偵測,這是由此處討論的技術所實現的。《使用描述性、預測性和社交網絡技術的詐騙分析》幫助您及時阻止詐騙,並消除未來發生的機會。