Fight Fraud With More Assets
Preventing fraud is a big job, but one that gets easier with the right tools. Fraudsters are getting their hands on more information about your customers every day. Using more than just a few data sets to see what is going on with those same customers is the single biggest advantage you can have to fight fraud and identity theft. Analytical modeling on those multiple sources of Big Data gives a an even bigger advantage to fraud reduction efforts.
Multiple data sets are those data collections which go beyond credit data. They can range from address changes that credit files might not see, to previous addresses, employment records, even information about fraud from your peers and related industries. By creating the ability to successfully manage multiple database results, a company enhances its ability to discover fraud. For example, if an applicant uses an address that your system determines is three years out of date, that would be a red flag. The same applies to employment data that doesn’t match up or if an existing customer trying to extend services has data that doesn’t match.
On the other hand if an applicant can be located consistently on multiple non- credit data sources, more than likely they are who they say they are and the information about them is correct.
With better fraud flags, you also get to a higher level of real-time “sniff tests.” Your representatives can engage in interactive questions which are set to screen for fraud. For example, in a true name-fraud model, if an applicants can’t remember who provided natural gas service at a previous address, they lmay not be the person they claim to be. In fact, good models will enable you to immediately decline potential fraudsters at minimal cost in either time or dollars.
Using multiple data sets appropriate to your business, you can build a true non-financial risk profile for your products or services. This includes the use of utility and telecom data, device history, address histories, and even fraud data from industry partners. Using these you can quickly identify fradulent identities.
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