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

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.

早期檢測詐騙以減少損失並防止連鎖損害
使用描述性、預測性和社交網絡技術進行詐騙分析是一本權威的指南，用於建立全面的詐騙檢測分析解決方案。早期檢測是減輕詐騙損害的關鍵因素，但它涉及比在更高級階段檢測詐騙更專業的技術。這本寶貴的指南詳細介紹了這些技術的理論和技術方面，並提供了對實施流程的專業見解。內容包括數據收集、預處理、模型構建和實施後的全面指導，以及各種學習技術和每種數據類型的使用。這些技術對於跨行業的詐騙檢測都是有效的，包括在保險詐騙、信用卡詐騙、反洗錢、醫療詐騙、電信詐騙、點擊詐騙、逃稅等應用中，為您提供了一個高度實用的詐騙預防框架。

估計一個典型組織每年因詐騙損失約5%的收入。更有效的詐騙檢測是可能的，本書描述了您的組織必須實施的各種分析技術，以制止收入損失。

• 檢查歷史數據中的詐騙模式


• 利用有標籤、無標籤和網絡化數據


• 在損害連鎖之前檢測詐騙


• 減少損失，增加恢復，加強安全性

詐騙被允許繼續存在的時間越長，造成的傷害就越大。它呈指數級擴大，對組織造成損害波及，並變得越來越難以追蹤、停止和逆轉。詐騙預防依賴於早期和有效的詐騙檢測，這些技術在這裡討論。《使用描述性、預測性和社交網絡技術進行詐騙分析》幫助您及時阻止詐騙，並消除未來發生的機會。