![\"\"](\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/ID-Verification-as-Service-McKinsey.png\")
<\/a><\/p>\nIn addition to supply-side requirements for identity verification, consumers are demanding improvements in the space. In 2018, an estimated 7% of consumers became victims of identity fraud according to Javelin Strategy & Research\u2019s annual report [2]. And this rise in fraud is increasing at an alarming rate, causing consumers to lose hundreds of millions of dollars to fraudsters. The US Federal Trade Commission (FTC) collects consumer protection complaints through the Consumer Sentinel Network; and in 2017, the FTC reported identity theft as one of the top three categories of complaints [3].<\/p>\n
Machine learning in combination with computer vision and OCR is changing the landscape of identity verification. And in an increasingly digital world, this is a key stepping stone to enabling businesses to build trust with consumers. For companies and their customers, this requires a frictionless verification experience that is simultaneously reliable. Identity verification companies like Jumio are influenced heavily by machine learning because it enables them to improve the process of detecting fraud in documentation. As an example, Jumio\u2019s technology began by creating ID-specific templates as a cross reference (e.g. a MA state driver\u2019s license). The company then developed image processing software to be able to detect edges of the document and identify specific patters and characters on the document to aid in fraud detection [4]. However, the complexity of identifying fake documents becomes more complex when the image of the document is a bit blurry or being held at an angle. \u00a0Machine learning, and more specifically deep learning enables Jumio to improve their process through the use of artificial neural networks.<\/p>\n
In the immediate term, Jumio is working to improve their software\u2019s ability to correctly identify a document as authentic or not. In the world of machine learning, this means accessing a large volume of data to train the system. Jumio\u2019s technology is augmented by human experts in verification that provide feedback to the system. This is presumably a costly model to sustain, however this hybrid approach is important to maintaining relationships with their clients who have much to lose if the computer system is wrong.<\/p>\n
Looking forward to the next few years, Jumio has identified key trends that they believe will affect their business [5]. One of these is the shift to biometric facial recognition as a preferred authentication method. For example, the Apple release of the iPhone X in late 2017 featured facial recognition as the unlock mechanism for the phone. And in the summer of 2018, US Customs and Border Protection (CBP) announced its use of facial recognition at vehicle borders to the country [6]. With the increasing prevalence of this technology, Jumio is working to integrate facial recognition into its product.<\/p>\n
Jumio\u2019s Netverify Identity Verification product involves first verifying the authenticity of the ID as a document. Next Jumio makes sure that the person holding the document is a live person and the same person as shown in the photo on the document.<\/p>\n
As previously mentioned, Jumio\u2019s model relies on a hybrid authentication approach (the software model and the human verification expert). However, with the rapid growth in demand for identity verification services, Jumio will need to focus on how to reduce the operational inefficiency of manually checking verifications in order to compete at scale. One potential avenue for improving the machine learned models is to focus on seeking strategic partnerships that allow Jumio to train their models even faster with access to large sets of data. For example, working alongside the TSA or CBP to train the system will improve the consistency and accuracy in detecting fraud.<\/p>\n
Beyond the midterm pressures of creating a scalable business and incorporating technologies like IoT and facial recognition, there is a question remaining about where the identity documentation will be in 10 years from now. Will we still be using physical documentation like passports and driver\u2019s licenses? Or how will digital identity documentation take shape? And what types of opportunities will exist for firms that focus on verification technology? (786 words)<\/p>\n
Footnotes<\/p>\n
[1] Iyer, V. (2018).\u00a0The next $20 billion digital market \u2013 ID verification as a service<\/i>. [online] Fuelbymckinsey.com. Available at: https:\/\/fuelbymckinsey.com\/article\/the-next-20-billion-digital-market-id-verification-as-a-service.<\/p>\n
[2]\u00a0Javelinstrategy.com. (2018).\u00a02018 Identity Fraud: Fraud Enters a New Era of Complexity<\/i>. [online] Available at: https:\/\/www.javelinstrategy.com\/coverage-area\/2018-identity-fraud-fraud-enters-new-era-complexity.<\/p>\n
[3]\u00a0Consumer Sentinel Network, Federal Trade Commission (2017).\u00a0Consumer Sentinel Network Data Book 2017<\/i>. [online] Available at: https:\/\/www.ftc.gov\/policy\/reports\/policy-reports\/commission-staff-reports\/consumer-sentinel-network-data-book-2017\/main.<\/p>\n
[4]\u00a0Patel, L. (2018).\u00a0Machine Learning, Deep Learning & the Wisdom of the Crowd<\/i>. [online] Jumio. Available at: https:\/\/www.jumio.com\/deep-learning-online-identity-verification\/.<\/p>\n
[5]\u00a0Valdivia, J. (2018).\u00a07 Identity Trends That Will Dominate 2018<\/i>. [online] Jumio. Available at: https:\/\/www.jumio.com\/identity-trends-2018\/.<\/p>\n
[6]\u00a0Levin, S. (2018). US government to use facial recognition technology at Mexico border crossing.\u00a0The Guardian<\/i>. [online] Available at: https:\/\/www.theguardian.com\/technology\/2018\/jun\/05\/facial-recognition-us-mexico-border-crossing.<\/p>\n
Cover image source<\/p>\n
Jumio’s Netverify Demonstration Graphic. (2018). [image] Available at: https:\/\/www.jumio.com\/trusted-identity\/netverify\/identity-verification\/.<\/p>\n","protected":false},"excerpt":{"rendered":"
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