{"id":1344,"date":"2015-06-04T17:09:00","date_gmt":"2015-06-04T17:09:00","guid":{"rendered":"https:\/\/tst418075918.wordpress.com\/2015\/06\/04\/the-best-way-to-beat-fraud-is-to-avoid-the-people-who-are-likely-to-do-fraud\/"},"modified":"2015-06-04T17:09:00","modified_gmt":"2015-06-04T17:09:00","slug":"the-best-way-to-beat-fraud-is-to-avoid-the-people-who-are-likely-to-do-fraud","status":"publish","type":"post","link":"https:\/\/test.veracityid.com\/new\/the-best-way-to-beat-fraud-is-to-avoid-the-people-who-are-likely-to-do-fraud\/","title":{"rendered":"The best way to BEAT fraud is to avoid the people who are likely to Do fraud."},"content":{"rendered":"

Imagine you’re responsible for security for a sold out concert. You know that roughly 5% of the 10,000 ticket holders are going to cause problems for the rest of the audience. In the past you’ve managed this risk by hiring at great expense, a bunch of off duty police officers and security guards to watch all ten thousand fans, waiting for something bad to happen. If you get lucky you’ll see something going down and you can pounce. Congratulations! Only 499 “problems” to go.<\/p>\n

But what if you could identify the 500 bad actors before they got inside? What if the “problem fans” exhibited tell tale signs or patterns that you could systematically evaluate as they are queuing to get in? Then you could either turn them away at the door or you could isolate and focus on them. Doing this would prevent a lot of chaos, save money on security guards and avoid treating everyone in the audience as if they were potential “problems” – an outcome that is wholly preferable to the traditional enforcement approach.<\/p>\n

Sadly this is a pipe dream in the physical world because the attributes that would tip you off to their criminal status are often not physically visible as they walk up. But a new company called VeracityID<\/b> is solving this problem in the financial services world. Specifically, they’ve figured out how to identify the estimated 5% of all financial customers that commit most of the fraud. And they do so in real time as prospective customers submit their on line or agent mediated application. They’ve just successfully demonstrated this approach in a half million dollar paid proof of concept for the Auto Insurance subsidiary of a Fortune 50 insurer. In it VeracityID took one quarter of their book, constituting 24 million records and combined it with a wide range of external data sources.<\/p>\n

<\/p>\n

<\/a><\/div>\n

Then, using proprietary social network analytics and boolean rules engine, they were able to identify fully 5% of the book that had either committed fraud by lying on their application or were at high risk of doing so based upon their social network risk score.<\/p>\n

The insurance company in our example calculated that on a “same store” basis \u00a0that using the VeracityID solution would have saved them between three and five percent of revenue which is huge in insurance terms. Which is why they’ve agreed to pay VeracityID millions of dollars to integrate their service into their front end online and agent application process.<\/div>\n
<\/div>\n

About the Company<\/b>
VeracityID is a new Silicon valley company founded by big data analytics and technology experts from McKinsey, Deloitte, Oracle, AT Kearny and IBM with decades of experience in financial services, healthcare and other data intensive online services. The key elements of their model include:<\/p>\n

    \n
  • Creating very targeted “apps”<\/b><\/i> that address specific high value financial services problems (their first area of focus is identifying personal lines automotive insurance fraud risk).<\/li>\n
  • Deploying each “app” in a web service<\/b><\/i> SaaS Cloud model.<\/li>\n
  • Connecting the fraud dots.<\/i><\/b>\u00a0by analyzing customer application data, internal company data and external databases using Social Networking Analytics and a boolean rules engine.<\/li>\n
  • Avoiding the fraud loss\u00a0<\/b><\/i>by delivering decisions in real time at the point of on line or agent mediated application – before<\/i>\u00a0a product and price offer is made.<\/li>\n
  • Doing so at cost benefit ratios<\/i><\/b> of 10 to 20 to one.<\/li>\n<\/ul>\n

    <\/p>\n

    Getting Started with VeracityID<\/b>
    Automotive focus. \u00a0<\/b>For the next year we are focused on leveraging the work we’ve done by adding additional Auto Insurers in North America and the UK. \u00a0We are also interested in developing relationships with participants in adjacent spaces.<\/p>\n

    Proof Project. \u00a0<\/b>The VeracityID approach is new and unique and we don’t expect you to fully embrace the solution without testing it yourselves. \u00a0This is why we always start with a Paid Proof Project. You provide us with all of the relevant data for a representative segment of your business. \u00a0We then simulate the enrollment of that segment and in conjunction with you estimate the bottom line benefit of a full implementation.<\/p>\n

    Using the benefits insights from the Proof Project we set performance objectives for\u00a0Veracity ID Deployment. \u00a0<\/b>Deployment has two key components: \u00a0the first is technical, ensuring the integration of your data with our “app” and making sure system performance and accuracy parameters are established. \u00a0The second is configuration of the business rules and the design and routing of event and data reporting. \u00a0This typically includes a change management component to ensure that your various functional groups understand and are fully prepared to capitalize on the improvement opportunities that VeracityID uncovers.<\/p>\n

    Timing Considerations. \u00a0<\/b>VeracityID allows its clients to identify and avoid fraudulent customers. As more and more industry participants utilize the VeracityID Automotive Insurance Fraud Identification “App” the number of companies absorbing the bulk of the industry fraud risk will shrink. \u00a0Being the last competitor in an industry to effectively identify it’s fraudulent customers is not likely to be a viable survival strategy.<\/div>\n","protected":false},"excerpt":{"rendered":"

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