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Big Data Technology, Semantics and Analytics
Trends, success and applications for big data including the use of semantic technology
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Visa Says Big Data Identifies Billions of Dollars in Fraud

Visa Says Big Data Identifies Billions of Dollars in Fraud | Big Data Technology, Semantics and Analytics |
Visa’s chief enterprise risk officer, Ellen Richey, says “you see the criminal capability evolving on the technology side.” She gives CIO Journal an inside look at how the company has used Big Data to make its network more secure...
Tony Agresta's insight:

The approach Visa takes in identifying fraud is grounded in 16 different predictive models and allows for new independent variables to be added to the model.  This improves accuracy while alowing the models to be kept up to date.  Here's an excerpt from the WJS Article:


"The new analytic engine can study as many as 500 aspects of a transaction at once. That’s a sharp improvement from 2005, when the company’s previous analytic engine could study only 40 aspects at once. And instead of using just one analytic model, as it did in 2005, Visa now operates 16 models, covering different segments of its market, such as geographic regions."


The article also states that the analytics engine has the card number and not the personal information about the transaction - likley stored in a different system.  I wonder if Visa, at some point in the process, also takes the fraud transactions and analyzes them visually to identify connections and linkages based on address, other geographic identifiers, 3rd party data, employer data and more?  Are two or more of the fraud cases in some way connected?  Does this represent a ring of activity presening higher risk to merchants, customers and Visa?


The tools on the market to do this work are expanding.   The data used to analyze this activity (including unstructured data) is being stored in databases that allow for the visual analysis of big data.  Graph databases replete with underlying intelligence extracted from text that identify people, places and events can be used to extend the type of analysis that Visa is doing and prioritize investigations.   Through more efficient allocation of investigation resources, fraud prevention can jump to a higher level.

luiy's curator insight, April 27, 2013 2:37 PM

“From the strategic point of view, we are achieving an amazing improvement, year over year, in our ability to detect fraud,” says Richey. “It’s not just our ability to analyze our transactions, but our ability to add new kinds of data, such as geo-location, to that analysis. With every new type of data, we increase the accuracy of our models. And from a strategic point of view we can think about taking and additional step change of fraud out of our system.”

In the future, Big Data will play a bigger role in authenticating users, reducing the need for the system to ask users for multiple proofs of their identify, according to Richey, and 90% or more of transactions will be processed without asking customers those extra questions, because algorithms that analyze their behavior and the context of the transaction will dispel doubts. “Data and authentication will come together,” Richey said.

The data-driven improvement in security accomplishes two strategic goals at once, according to Richey. It improves security itself, and it increases trust in the brand, which is critical for the growth and well-being of the business, because consumers won’t put up with a lot of credit-card fraud. “To my mind, that is the importance of the security improvements we are seeing,” she said. “Our investments in data and analysis are baseline to our ability to thrive and grow as a company.”

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HSBC to Pay $1.92 Billion to Settle Charges of Money Laundering

HSBC to Pay $1.92 Billion to Settle Charges of Money Laundering | Big Data Technology, Semantics and Analytics |
The announcement of a settlement on Tuesday came after state and federal authorities decided against indicting the British bank in a money-laundering case.
Tony Agresta's insight:

If you read this, notice one of the last paragraphs - 


"HSBC has since moved to bolster its safeguards. The bank doubled its spending on compliance functions and revamped its oversight, according to a spokesman. In January, HSBC hired Stuart A. Levey as chief legal officer to come up with stricter internal standards to thwart the illegal flow of cash. Mr. Levey was formerly an under secretary at the Treasury Department who focused on terrorism and financial intelligence."


Big Data Analytics is one way to do this.   But HSBC may have fallen into the trap where they focus on one form of analysis to detect money laundering.  Predictive models used to identify transactions that may be fraud or money laundering can be a useful way to detect this type of activity.   But all models contain some amount of error.  When network analysis, geospatial analysis and temporal analysis are also applied, money laundering schemes can be revealed using data visualization that show unusual patterns of behavior, linkages between people and events, fund transfers that take place at odd times and more.   Most of these institutions need to combine descriptive reporting, alerts which are triggered when outlier transactions are ready for approval, predictive models and interactive data visualization including link analysis to explore hidden relationships in data.   Without this comprehensive approach, this problem will continue to occur.  The data is all there.  Now it needs to be integrated (including unstructured data in the form of notes) and analyzed using all of the major techniques.

WebMarketingStore's comment, May 2, 2014 1:59 AM
Staggering: $1.9b is the 'settlement' amount? How much might the damage have been, full-tilt?