Download Fraud analytics using descriptive, predictive, and social by Bart Baesens PDF

By Bart Baesens

Detect fraud previous to mitigate loss and stop cascading damage

Fraud Analytics utilizing Descriptive, Predictive, and Social community Techniques is an authoritative guidebook for developing a complete fraud detection analytics answer. Early detection is a key think about mitigating fraud harm, however it comprises extra really expert strategies than detecting fraud on the extra complex phases. This useful advisor info either the speculation and technical features of those options, and offers specialist perception into streamlining implementation. assurance contains facts amassing, preprocessing, version development, and post-implementation, with entire information on a number of studying options and the knowledge kinds used by each one. those suggestions are powerful for fraud detection throughout barriers, together with purposes in assurance fraud, bank card fraud, anti-money laundering, healthcare fraud, telecommunications fraud, click on fraud, tax evasion, and extra, supplying you with a hugely sensible framework for fraud prevention.

It is anticipated average association loses approximately five% of its profit to fraud each year. greater fraud detection is feasible, and this ebook describes many of the analytical suggestions your company needs to enforce to place a cease to the profit leak.

  • Examine fraud styles in ancient data
  • Utilize categorized, unlabeled, and networked data
  • Detect fraud earlier than the wear and tear cascades
  • Reduce losses, raise restoration, and tighten security

The longer fraud is authorized to move on, the extra damage it reasons. It expands exponentially, sending ripples of wear during the association, and turns into progressively more complicated to trace, cease, and opposite. Fraud prevention is dependent upon early and powerful fraud detection, enabled through the ideas mentioned the following. Fraud Analytics utilizing Descriptive, Predictive, and Social community Techniques is helping you cease fraud in its tracks, and do away with the possibilities for destiny occurrence.

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Additional info for Fraud analytics using descriptive, predictive, and social network techniques : a guide to data science for fraud detection

Sample text

FRAUD: DETECTION, PREVENTION, AND ANALYTICS! 15 Since detecting fraud based on specified business rules requires prior knowledge of the fraud scheme, the existence of fraud issues will be the direct result of either: ◾ An inadequate internal control system (controls fail to prevent fraud); or ◾ Risks accepted by the management (no preventive or corrective controls are in place). Some examples of fraud-detection rules that can be derived from these business policy excerpts and process deviations, and that may be added to the fraud-detection rule engine are as follows: Business policy excerpt 1: ◾ IF multiple advanced payments for one claim, THEN suspicious case.

This does not necessarily require stealing the physical card, only stealing the card credentials. Behavioral fraud concerns most of the credit card fraud. Also, debit card fraud occurs, although less frequent. Credit card fraud is a form of identity theft, as will be defined below. Insurance fraud Broad category-spanning fraud related to any type of insurance, both from the side of the buyer or seller of an insurance contract. Insurance fraud from the issuer (seller) includes selling policies from nonexistent companies, failing to submit premiums and churning policies to create more commissions.

Gov). ◾ Fraud is costing the United Kingdom £73 billion a year (National Fraud Authority). ◾ Credit card companies “lose approximately seven cents per every hundred dollars of transactions due to fraud” (Andrew Schrage, Money Crashers Personal Finance, 2012). ◾ The average size of the informal economy, as a percent of official GNI in the year 2000, in developing countries is 41 percent, in transition countries 38 percent, and in OECD countries 18 percent (Schneider 2002). Even though these numbers are rough estimates rather than exact measurements, they are based on evidence and do indicate the importance and impact of the phenomenon, and therefore as well the need for organizations and governments to actively fight and prevent fraud with all means they have at their disposal.

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