The typical organization loses 5%* of its annual revenues to fraud, or a worldwide loss of $4.3 trillion annually based on the 2012 Gross World Product.
From this giant pot of fraud, identity fraud in the US is responsible for $37 billion in losses* and over 11 million reported cases.*
Luckily for us, we have very big digital footprints. In the time I’ve spent working at Pipl, I can tell you that the amount of data we share publicly online is staggering.
With so much data available online, it’s becoming much easier to verify someone’s identity and prevent fraud.
Here are 10 ways companies can fight fraud using social profiles and online data.
Do you remember the story of the Wired writer getting his digital life erased by hackers?
If you don’t, here’s the summary: Hackers impersonating the writer fooled Amazon & Apple’s customer support into giving them partial credit card numbers and a new email password which were used to delete and take control of any device, account, and data the writer had connected to his email address.
All the hackers needed to fool Apple was an email address, billing address, & four digits from the victim’s credit card.
If these companies had more data at hand, it would’ve been much harder for the hackers to fool them. Imagine a hacker being asked by customer support “what were your past 3 addresses?” or if they used a video/Skype call to compare the hacker’s face to social profile images.
Speaking of which…
The problem with using Google for this is that they’ll give you anything related to your search, making it hard to find the right images for ID verification. Using a people-centered search engine or database, allows you to find pictures shared online in social networks and photo sharing sites by the person you’re trying to verify.
If the pictures don’t match then you probably are looking at a fake ID.
One of the easiest things to check is if the phone number or email provided is fake or invalid. There are plenty of free services like Free Email Verifier (not affiliated with Pipl) you can use. Also, it’s worth checking if the phone number or email address is associated with the customer by running a reverse search on the email address or phone number.
Another way to prevent fraud and raise red flags is by running a digital footprint search. If you can’t find anything online or just find the things that are easiest to set up and/or fake, like an email address or social network profile, then it’s time to be suspicious.
Isn’t it suspicious if a 22 year old waiter makes purchases worth thousands of dollars?
With Big Data you can enrich your customer data with age, occupation, education, etc. to build rich customer profiles. Most of this data is available in public profiles from sites like Linkedin, Facebook, etc.
If the profile doesn’t fit the purchase, you have a reason to launch an investigation.
There are two scenarios for this:
For either scenario, Big Data comes in handy to ensure you aren’t defrauded.
Many data records, both online and off, link people to their relatives. Checking these is a quick way to see if someone is lying.
People forget to adjust their social network privacy settings – this is good news for you.
A simple social profile search can find you a username and 2 minute check can discover evidence of insurance or workcover fraud.
A great way to combat return fraud is to pull in data from public records and social networks.
Check public records to see if the address matches the customer’s actual address or that of the customer’s business or relatives. Check social profile feeds to see if any posts or images relating the shipped items show up.
And then there’s the kitchen sink. Run a reverse lookup on a person’s name, phone number, address, etc. and see what pops up.
With this, you get a complete view of the person you’re dealing with – public records, social profiles, mentions in publications, background checks, etc. You can see how this works by trying out the Pipl.com search engine or check out the API’s live demo.