ECommerce fraud is growing quickly, creating new challenges in terms of prevention and detection. As merchants gather more and more information about customers and their behaviors, the key element in the fight against fraud is now to draw on the connections within the data collected to uncover fraudulent behaviors. In this post we explain why and how graph technologies are crucial in the detection of eCommerce fraud.…

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Many businesses, especially small businesses, underestimate the danger their company’s data is in. They have the idea that because they’re fairly small, no one would want to try to steal the customer information they collect. After all, why go after a few thousand customer records when you could attack a large corporation and potentially walk away with tens of…

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Fraud detection is all about connecting the dots. We are going to see how to use graph analysis to identify stolen credit cards and fake identities. For the purpose of this article we have worked with Ralf Becher, irregular.bi. Ralf is Qlik Luminary and he provides solutions to integrate the Graph approach into Business Intelligence solutions like QlikView and Qlik Sense.

Third party fraud in retail

Third party fraud occurs when a…

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Original post here:  http://sctr7.com/2014/06/27/the-cutting-edge-network-analytics-for-financial-fraud-detection-and-mitigation/

The application of network analysis to the growing challenge of fraud and financial crime is a fast emerging advanced data analytics frontier. As any good fraud investigation knows, fraud and financial crime are as much…

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Read the Preface for Siegel’s Book – Predictive Analytics

Here is the preface for Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die

By Eric Siegel, with a foreword from Tom Davenport

(Wiley, February 2013)

 

To order the book:…

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Excerpt:

There are four key data visualization techniques used by data analysis pros in the government and local law enforcement.  As financial institutions, e-commerce organizations and social network analysts begin to apply data visualization more frequently, these techniques will help guide the process of uncovering meaningful insights hidden within mountains of disparate data.  This post focuses on advanced data visualization using relationship graphs.

In our last post…

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For quite some time, Intelligence Analysts in key government agencies have used these four techniques to reveal significant insights hidden beneath layers of disparate data. Today, they can be applied to your business. Just follow these guidelines.

Whether you are a fraud analyst, cyber security expert or social networking analyst, you'll reveal essential information to manage your business…

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Abstract

Businesses have a large and growing need to analyze data. This is no easy task today with the exploding volumes of data pouring in from everywhere, and the enormous pressure to turn these mountains of data into information that can be acted upon quickly.



It is no surprise that organizations spend over $15B annually on Business Intelligence (BI) and Data Mining technologies. But with all of the focus on infrastructure technologies, there is little emphasis on the… Continue

COLLABORATE 10 – IOUG (Independent Oracle User Group) Forum April 18-22,2010, Las Vegas, Nevada
1. Building a Check Fraud Detection System Using Oracle 11g and Oracle Data Mining, April 20, 2010
2. Building a Card Fraud Reporting Portal, April 22, 2010

I have been building data mining solutions within relational databases. Almost every process is done within the database environment. We enjoy the functions provided by databases: security, scalability, analytic functions, manageability, easy model deployment, SQL, etc. Since data mining is done on the server side, the crash of my laptop hard drive has minimum impact on my work (it happened twice in the last two years).