{"id":2209,"date":"2020-01-22T04:21:01","date_gmt":"2020-01-22T04:21:01","guid":{"rendered":"http:\/\/kusuaks7\/?p=1814"},"modified":"2024-01-25T09:47:23","modified_gmt":"2024-01-25T09:47:23","slug":"ten-ways-ai-is-going-to-improve-fintech-in-2020","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/fintech\/ten-ways-ai-is-going-to-improve-fintech-in-2020\/","title":{"rendered":"Ten Ways AI Is Going To Improve Fintech In 2020"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\t\t\t\tBottom Line:<\/strong>\u00a0AI & machine learning will improve Fintech in 2020 by increasing the accuracy and personalization of payment, lending, and insurance services while also helping to discover new borrower pools.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\nZest.ai\u2019s\u00a02020 Predictions For AI In Credit And Lending\u00a0captures the gradual improvements I\u2019ve also been seeing across Fintech, especially at the tech stack level. Fintech startups, enterprise software providers, and the investors backing them believe cloud-based payments, lending, and insurance apps are must-haves to drive future growth. Combined with Internet & public cloud infrastructure and mobile apps, Fintech is evolving into a fourth platform that provides embedded financial services to any business needing to subscribe to them, as\u00a0Matt Harris of Bain Capital Ventures<\/a>\u00a0writes in\u00a0Fintech: The Fourth Platform – Part Two<\/a>. Embedded Fintech has the potential to deliver $3.6 trillion in market value, according to Bain\u2019s estimates, surpassing the $3 trillion in value created by cloud and mobile platforms.\u00a0Accenture\u2019s recent survey of C-suite executives\u2019 adoption and plans<\/a>\u00a0found that 84% of all executives believe they won\u2019t achieve their growth objectives unless they scale AI, and 75% believe they risk going out of business in 5 years if they don\u2019t. The need to improve payment, lending and insurance combined with customers\u2019 mercurial preferences for how they use financial services are challenges that AI and machine learning (ML) are solving today.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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How AI & Machine Learning Will Improve Fintech In 2020<\/strong><\/h3>\n<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tFintech\u2019s traditional tech stacks weren\u2019t designed to anticipate and act quickly on real-time market indicators and data; they are optimized for transaction speed and scale. What\u2019s needed is a new tech stack that can flex and adapt to changing market and customer requirements in real-time. AI & machine learning are proving to be very effective at interpreting and recommending actions based on real-time data streams. They\u2019re also improving customer experiences and reducing risk, two additional factors motivating lenders to upgrade their traditional tech stacks with proven new technologies.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThe following are ten predictions of how AI will improve FinTech in 2020, thank you\u00a0Zest.ai<\/a>\u00a0for your insights and sharing your team\u2019s expertise on these:\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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  1. Zest predicts lenders will increase the use of ML as the way to grow into the no-file\/thin-file segments, especially rising Gen Zers with little to no credit history.\u00a0<\/strong>Traditional tech stacks make it difficult to find and grow new borrower pools. Utah-based auto lenderPrestige Financial Services chose to rely on an AI solution instead. The chose Zest AI to find and cultivate a borrower pool of people in the 19-35 age group. Using an AI-based loan approval workflow, Prestige was able to increase loan approval rates by 25%, and for people under 20 by threefold.<\/li>\n \t
  2. Mortgage lenders\u2019 adoption of AI for finding qualified first-time homeowners is going to increase as more realize Gen Z (23 \u2013 36-year-olds) are the most motivated of all to purchase a home.<\/strong>\u00a0In 2020, long-standing assumptions about first-time homebuyers and their motivations are going to change. A recent story in\u00a0HousingWire, \u201cThis generation is the most willing to do whatever it takes to buy a home<\/a>,\u201d explains that Gen Z, or those people born between 1996 and 2010, are the most likely to relocate to purchase a new home. A recent TransUnion market analysis found 70% of Gen Z prospective home buyers are willing to relocate to buy their first home, leading all active generations. 65% of Gen Xers, or those born between 1965 to 1980, were the second most likely to move. AI and ML can help lenders more precisely target potential Gen Z first-time homebuyers, measuring the impact of their marketing campaigns on attracting new borrowers. The TransUnion market analysis finds that 58% of respondents are delaying a home purchase due to anticipated high down payments or monthly payments. 51% said the need to obtain a 10% to 20% down payment was stopping them. According to Joe Mellman, TransUnion senior vice president, and mortgage business leader,\u00a0\u201cMany of our potential first-time homebuyer respondents don\u2019t seem to be aware of the wide variety of financing options available to them.\u201d<\/em>\u00a0The TransUnion market analysis found that many of the potential first-time homeowner respondents have never heard of low down-payment options from\u00a0Fannie Mae, Freddie Mac, or of the\u00a0Federal Housing Administration.<\/li>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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  3. Zest predicts banks and other financial institutions will strengthen their business cases for AI pilots and production-level deployments by recognizing the operating expense (OPEX) savings of ML.\u00a0<\/strong>Several recurring costs involved in developing, validating and deploying credit risk models can be reduced or cut by switching to machine learning, according to Zest. Lenders can get the most out of their data acquisition spending by using modern ML tools to assess which data sources yield the most predictive power for a model. Lenders will also switch to ML to simplify their IT and risk operations by consolidating into fewer models that can do the work of what used to be multiple individual linear models for every customer segment.<\/li>\n \t
  4. Compliance cost growth will decline even faster due to ML.<\/strong>\u00a0Financial institutions that have AI\/ML algorithms in production log every change in a model and can produce all the required model risk governance documents in minutes instead of a compliance team manually taking weeks to do it. Automated tools also shrink the time it takes to do fair lending testing by building less discriminatory models on the fly rather than the time-intensive approach of drop-one-variable-and-test. Time is money, especially in lending.<\/li>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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  5. AI and ML will gain critical mass in collections, providing insights into which approach is the most effective for a given customer<\/strong>. Zest has built collections models for a few financial services firms and has found them to be very effective. Collections logic, predicting which customers to wait on when bills are past due, is a strong fit for machine learning. With one bank, Zest found that ML models can, for example, accurately target the borrowers most likely to make a certain minimum payment based on the value of their loan within 60 days of falling behind their due date. In three months, Zest built two models from traditional credit bureaus and the bank\u2019s proprietary collections metrics to predict this repayment propensity of borrowers. One insight into the data was that borrower behavior accounted for just over half of the bank\u2019s ability to collect missed payments, but operations played a significant role.<\/li>\n \t
  6. If there\u2019s a downturn, ML will get blamed (even though it can actually help in a downturn)<\/strong>. Pankaj Kulshreshtha, CEO of Scienaptics, originally made this observation at the Money 20\/20 Conference held earlier this year. Models built only in good times can see their correlations break when times go bad. Lenders who observe best practices in AI and ML adoption will make sure to stress-test their models, perhaps by including synthetic data to add heterogeneity.\u00a0<\/em>Better ML monitoring will be important, too.\u00a0\u201cML models and algorithmic monitors can\u00a0<\/em>do a better job <\/em>seeing around corners, spotting rising numbers of inbound outlier applicants that signal more volatile conditions ahead,\u201d<\/em>\u00a0says\u00a0Seth Silverstein, Executive Vice President of Credit Risk Analytics for Zest AI.<\/a>\u00a0\u00a0An effective ML monitoring tool should excel at spotting outlier applicants and feature drift, ensuring more accurate model outcomes.<\/li>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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  7. 2020 is going to be a break-out year for partnerships and co-opetition as payments, lending and insurance firms vie for a growth position in embedded financial services.\u00a0<\/strong>Matt Harris of Bain Capital Ventures\u2019 prediction of embedded fintech suggests a proliferation of cloud-based Fintech apps around the core: payments, lending, insurance.That creates an ideal situation for AI-related alliances and partnerships among the incumbent lenders, startups, data aggregators and the CRAs. To Harris, the layers of the stack are centered around connectivity, intelligence, and ubiquity. According to Crunchbase, there have been 51 Fintech acquisitions in 2019 alone.\u00a0Plaid\u2019s acquisition of Quovo<\/a>\u00a0in January for approximately $200 million and\u00a0Fiserv\u2019s acquisition of First Data<\/a>\u00a0reflect how Fintechs are creating their own unique tech stacks already.<\/li>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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  8. \u00a0Zest predicts Fintechs<\/strong>\u00a0will seek out AI and ML modeling expertise more so than build expertise and teams on their own, which will be costlier and take longer.\u00a0<\/em><\/strong>Embedded Fintech\u2019s future adoption rate is predicated on how effective development efforts are today at minimizing incidential bias and providing customers with greater visbility into how and why models provide specific results\u00a0\u201cSome of these startups are bringing their own data science and ML models. We have to hope these firms own, build, or buy the tools to ensure their models are inclusive, free of incidental bias, and use transparent AI customers can trust. We see explainable AI as being a essential feature or service in that tech stack,\u201d<\/em>\u00a0says Zest\u2019s Silverstein.<\/li>\n \t
  9. \u00a0Fintechs will rely on AI and ML to help close the talent gap each of them has today while also improving the effectiveness of their talent management strategies.<\/strong>\u00a0Finding, recruiting, and hiring the best candidates for development, engineering, marketing, sales and senior management roles is an area Fintechs will increasingly adopt AI and ML for in 2020. Fintech CEOs and CHROs will begin upskilling programs for themselves and their teams to increase AI fluency and skills mastery in 2020. According to a recent\u00a0Harris Interactive<\/a>\u00a0survey completed in collaboration with\u00a0Eightfold<\/a>\u00a0titled\u00a0Talent Intelligence And Management Report 2019-2020<\/a>, 73% of U.S. CEOs and CHROs plan to use more AI in the next three years to improve talent management.<\/li>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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  10. Credit unions will adopt ML in 2020 to automate routine tasks and free up human underwriters to focus on providing more personalized services, including improvements in inquiry resolution & dispute and fraud management.<\/strong>\u00a0Credit unions are built on an annuity-based business model that delivers successively higher profitability the longer a member is retained. Credit unions will capitalize on ML by driving up loan approvals with no added risk and automating more of the loan approval process.\u00a0By the end of 2020, according to a Fannie Mae survey of mortgage lenders, 71% of credit unions plan to investigate, test, or fully implement AI\/ML solutions \u2013 up from just 40% in 2018. AI and ML will also be adopted across credit unions to improve inquiry resolution & dispute and fraud management while improving multichannel customer experiences. Providing real-time, relevant responses to customers to expedite inquiries and dispute resolutions using AI and ML is going to become commonplace in 2020. AI and ML is predicted to make a significant contribution to automating anomaly detection and borrower default risk assessment as the graphic below from\u00a0Fannie Mae\u2019s Mortgage Lender Sentiment Survey\u00ae How Will Artificial Intelligence Shape Mortgage Lending? Q3 2018 Topic Analysis<\/u><\/a>\u00a0illustrates:<\/li>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    \n\t\t\t\t\t\t\t\t\t“10 Ways AI Is Going To Improve Fintech In 2020” \nFANNIE MAE\u2019S MORTGAGE LENDER SENTIMENT SURVEY\u00ae HOW WILL ARTIFICIAL INTELLIGENCE SHAPE MORTGAGE LENDING? Q3 2018 TOPIC ANALYSIS<\/small><\/span><\/em><\/figure>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

    AI & machine learning will improve Fintech in 2020 by increasing the accuracy and personalization of payment, lending, and insurance services while also helping to discover new borrower pools. Fintech’s traditional tech stacks weren’t designed to anticipate and act quickly on real-time market indicators and data; they are optimized for transaction speed and scale. What’s needed is a new tech stack that can flex and adapt to changing market and customer requirements in real-time.  Here are ten predictions of how AI will improve FinTech in 2020.<\/p>\n","protected":false},"author":138,"featured_media":3435,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[192],"tags":[99],"ppma_author":[2679],"class_list":["post-2209","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-fintech","tag-fintech"],"authors":[{"term_id":2679,"user_id":138,"is_guest":0,"slug":"louis-columbus","display_name":"Louis Columbus","avatar_url":"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/04\/medium_65812956-54d2-4b01-9e05-0d23abb284c7-150x150.jpg","user_url":"https:\/\/softwarestrategiesblog.com\/","last_name":"Columbus","first_name":"Louis","job_title":"","description":"Louis Columbus is currently serving as Principal, IQMS. He is Marketing and Product Management Leader, Forbes Columnist, Software Expertise in Analytics, Cloud, CPQ & ERP Solutions. He teaches MBA courses in international business, global competitive strategies, international market research, and capstone courses in strategic planning and market research. He has taught at California State University, Fullerton: University of California, Irvine; Marymount University, and Webster University."}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/2209","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/138"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=2209"}],"version-history":[{"count":8,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/2209\/revisions"}],"predecessor-version":[{"id":35644,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/2209\/revisions\/35644"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/3435"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=2209"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=2209"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=2209"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=2209"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}