Realtime Data for a Realtime Threat: Preventing Fraud in a Mobile World

 In Credit Risk Management, Fraud, Instant Decisioning

Mobile is changing the way we live. Just as it has increased the frequency and immediacy of our communications (by being constantly plugged in to email, texting, social networks, etc.), it is likely going to exponentially increase the frequency and immediacy of financial transactions. With the upsurge of mobile applications that involve banking and payments there will almost certainly be an uptick in fraud related to hackers trying to obtain valuableblank consumer data via these apps and their associated devices. There is a great deal of information available on our mobile devices that could assist fraudsters in hacking our accounts or establishing credit in our names. To combat this, banks are going to need more customer data, the capability to sort through that data and the ability to pick out discrepancies in realtime.

In the past I’ve written about the value of using alternative data in the credit decisioning process and advocated for the mainstream use of new and varied types of data in addition to traditional credit bureau data. I support, now more than ever, the case for using more data during account opening and credit decision processes in order to more effectively manage fraud in a mobile world.

I recently tested a service offered by ID Analytics called My ID Score™.This is a free public service that provides a way to easily and quickly assess your risk of identity theft. It was an interesting exercise for a couple of reasons. I tested my married name and could not be located but my maiden name pulled up information from 20 years ago that I wouldn’t have expected. The good news is my risk of identity theft is low, but it did make me think about all of the information that is housed out there about each of us and how it could potentially be used by fraudsters. Again, with the emergence of increasing mobile activities for financial transactions, protecting our personal information is going to become even more important.

Let me get back to my advocacy for alternative data and the role I believe it will play in the mobile protection space. Information that is not found in traditional credit files includes a wealth of sources; professional licenses, asset information, education history, utility records, rent payments, address changes and collections data. This deeper level consumer data can be used to detect synthetic identities, which usually lack anything more than a name, address and social security number. Having access to a more complete data set can also be used to catch fraudsters who have stolen legitimate consumers’ identities.  As the velocity of fraud increases, having access to realtime data (which many alternative data providers offer) will also be critical.

Additionally, analytic models and scoring algorithms can be used to detect discrepancies in this data that are indicative of fraud. For example, an application that appears legitimate on the surface may turn out to contain a phone number that is associated with multiple addresses. That definitely raises a red flag. The same goes for device identity. My colleague recently heard of an application for an account that appeared to be coming from an iPhone in New York. Upon further inspection the device was actually a PC—not an iPhone—and the true address of the device was Nigeria, not New York. Knowing this information, it’s clear that this was in fact a fraudulent application.

Fraudsters are good at evolving, their livelihood depends on it. Consumers must do their part to ensure their identities are safe but financial institutions must also evolve to beat fraudsters at their own game. The data to assist in fighting fraud is out there and readily available. It’s a powerful tool when combined with the appropriate analytics and scoring models. The pace of mobile development is fast. Fraudsters are fast. FIs must be faster.

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