Attendees will be shown how to leverage crowdsourcing to commit various types of fraud such as Tax Refund Fraud, Disaster-Relief Fraud, Benefits Fraud (Medicaid Fraud, Medicare Fraud, Welfare Fraud and Section 8 Housing Assistance Fraud). We will look at which opportunities and segments of the population are easy targets for large scale identity fraud what insights are gained from this analysis and what can be done on the ground to narrow the window of opportunity for these types of operations and schemes.
Attendees will also be given a solution to how to detect and combat these various types of fraud. This session will discuss the challenge of resolving identity from billions of identity fragments and why the bigger data, the better the resolution. The session will outline what this means for Business and why Consumers should care about how to best generate a meaningful public data identity for themselves.
We will look at some of the interesting high level identity statistics and insights we have gained from large scale data analysis and exploration using our massive data assets.
The discussion will include questions like: What is the average age a person starts to establish an identity footprint (with and without immigrant identities)?
Lastly, we will cover the one surprising Big Data perspective that differentiates fake or synthetic identities from real identities. We’ll give you a hint: it’s the one thing that identity thieves forget to do when they create a fake or synthetic identity, and this one thing, helps distinguish real identities from fake identities.
Data Scientist at LexisNexis Risk Solutions. I have responsibilities to the core HPCC Systems platform technology and spearhead large scale graph analytics projects working with big data, for various industries to help customers target fraud, collusion and other red flag indicates. Prior to LexisNexis, Mr. Prichard worked for Topspeed Software R&D in London.
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