Socure is redefining identity verification with groundbreaking technology, supporting myriad organizations with the most accurate authentication tools in the industry. What we build helps businesses scale faster, stop fraud, and ultimately allows millions of people who are excluded from the digital economy (due to outdated fraud detection models) to take part in it like everyone else. Our culture is about innovation, winning, and customer obsession. We are full of top performers that prioritize excellence and results, as well as support for one another, on the path to achieving our mission: to verify 100% of good identities in real time and completely eliminate identity fraud for every applicant on the internet. To learn more about working at Socure visit our career page here: https://www.socure.com/company/careers

It's not just your face that can be convincingly replicated by a deepfake. It's also your voice -- quite easily as journalist Chloe Beltman found: Given the complexities of speech synthesis, it's quite a shock to find out just how easy it is to order one up. For a basic conversational build, all a customer has to do is record themselves saying a bunch of scripted lines for roughly an hour. "We extract 10 to 15 minutes of net recordings for a basic build," says Speech Morphing founder and CEO Fathy Yassa. The hundreds of phrases I record so that Speech Morphing can build my digital voice double seem very random: "Here the explosion of mirth drowned him out."

Founded in 2012, Socure is the leader in high-assurance digital identity verification technology. Named to Forbes' 2019 AI 50 list as one of America's most promising AI companies, and a recent winner of API World's Best Data API, Socure's technology applies artificial intelligence and machine learning techniques with trusted online/offline data intelligence from email, address, phone, IP, social media and the broader Internet to verify identities in real-time. Customers include three of the top five U.S. banks, seven of the top 10 U.S. card issuers, as well as the majority of leading digital banks, lenders and insurers across the U.S. We are funded by some of the world's best investors and entrepreneurs including Scale Venture Partners, Commerce Ventures, Work-Bench, Santander InnoVentures and Two Sigma Ventures. Our trophy case includes numerous industry awards and accolades, including being named one of Forbes America's Best Startup Employers 2021 as well as the Best New Technology Introduced over the Last 12 months – Data and Data Services at the 2020 American Financial Technology Awards (AFTAs), being ranked #70 on Deloitte's Technology Fast 500, getting listed as a Gartner Cool Vendor, and winning Finovate's Award for Best Use of AI/ML, to name a few! The only way we can further our mission of becoming the single, trusted source of identity verification and eliminating identity fraud is by building the best team on the planet. This is where you come in!

No, when it comes to the biggest immediate impact that artificial intelligence (AI) will have upon the world, financial services will likely take that honor -- a looming prize that provides fresh opportunity to consider how machines will overcome bias. Anyone who reads the news or follows science fiction knows how important AI is becoming to business, and how much change it will almost certainly bring to civilization. However, Sunil Madhu, founder and chief strategy officer of online security and predictive analytics firm Socure, told Karen Webster during a recent podcast interview that bias remains a vital issue for the future of AI and machine learning. After all, he said, "human bias cannot be trained out," especially since the majority of human decision making, according to the latest scientific findings, is governed by the biological, chemical and electrical mechanics of the subconscious. People don't even know what they know, or how they end up knowing what they know.

In Episode Three of PYMNTS' machine learning podcast, Socure CEO Sunil Madhu and PYMNTS CEO Karen Webster get technical as they explore the methods and processes that are making machines progressively smarter. In Episode Two, Madhu and Webster talked about different kinds of artificial intelligence (AI) and how each is equipped to handle certain kinds of jobs. Machines can learn to sort through all kinds of data: names, phone numbers, email addresses, network data, geolocation, biometrics, images and more. According to Madhu, 60 percent to 70 percent of the work in data science goes into data engineering, and that work is (for now) done by human beings, though engineers are gradually teaching machines to take over more and more of their own education.