Jumio, the leading provider of orchestrated end-to-end identity proofing, eKYC and AML solutions, announced its participation and exhibition at Money 20/20 USA, taking place October 23-26 in Las Vegas. On October 23 at 3:25 p.m. PT, Jumio will take the stage with Mercari to present "Can Online Marketplaces Survive AI-Powered Fraud?" This special one-on-one session will feature Stuart Wells, Jumio chief technology officer, and Lisa Lechner, Mercari chief compliance officer. Throughout the conference, attendees can also visit Jumio booth #2103 to learn how the global digital identity leader is accelerating digital trust by putting the power of AI and biometrics to work for account onboarding, KYC/AML compliance, identity fraud prevention, and more. Jumio will also showcase the Jumio Heat Map, based on live Jumio data and intended to simulate transactions and attack vectors as they're launched from across the globe.

We at Finalytics.ai just got back from participating in our very first Money 20/20 as a sponsor and thought it would make sense to jot down some of our takeaways. We believe credit unions need to keep these in mind as they visit their strategic plans for 2022 and beyond. While 2020 and 2021 were dominated by the impact of the pandemic, 2022 will see a slightly more reasoned approach to digital transformation throughout financial services. We are seeing a larger focus on building fintech and legacy financial institution (FI) partnerships throughout the industry. While attending sessions with bankers, fintechs and banktech companies, we noted that there was a heightened awareness that fintech firms need bank and credit customers to scale while FIs need the innovations from fintech firms.

As computational technology advances, leveraging trends in data and meta-data will help organizations understand both their customers and other businesses more extensively. AI and ML are going to affect all realms of society, and payments are not immune to this trend. Democratization of tools for analytics in the field will help open up doors for an expanded crowd. As tools and APIs for developers looking towards AI or ML expand, developers will be able to access these complex tools more easily. Speaking to this, Dr. Arif Ahmed of U.S. Bank remarked how, "With deep learning, you have better ways to conceptualize problems. You see how voice recognition, fraud recognition, and more are improving. You start with the technology, and then you host concepts. . Pattern recognition from AI and ML advancements will have a strong impact as it relates to Anti-Money-Laundering (AML) and Know-Your-Customer (KYC) practices. Particularly in the litigation response matters, Husayn Kassai of Onfido explained how often times remediation work today is outdated. "The current way that it is carried out isn't necessarily fit for a digital age," said Kassai. "It doesn't make sense to have fully human authentication systems at a bank." Ensuring a proper intake of data will be key here, the panel said, as financial services players transition to updated or increasingly distributed backend platforms. In the future, many consumer-facing products, including chatbots, will make their way into digital services. For lots of financial players, the ability of machines to understand human slander falls short, as placing consumer-facing concerns in context is a major challenge. People can build chatbots with specific purposes, such as manuals to build a plane or figure out the nature of a mortgage contracts. To minimize errors, look for chatbots in financial services to be developed with specific purposes, such as mortgage loan contracts or ATM interfacing. David Gilvin of IBM remarked how "AI is always on, 365, 24/7. .

Data scientists can provide firms with access to a magical world of machine learning and all that it promises. This question was posed to a panel of experts in the field at Money 20/20 Europe in Copenhagen. Jay van Zyle of Innosect, moderating, asked if a data scientist is now just a computer person that went on a statistics course, or conversely, a stats person that went on a computer programming course? Or is it entirely a new discipline? Marco Bressan, chief data scientist, BBVA provided an elegant analogy to illustrate the plight of the data scientist: "One simple way is to look at data scientists doing machine learning as farmers. While traditional software developers you could look at more like manufacturers. "Traditional software developers would put modules together, and that would come out one machine.