{"id":44472,"date":"2020-01-17T09:07:17","date_gmt":"2020-01-17T17:07:17","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/industry\/microsoft-in-business\/?p=44472"},"modified":"2023-06-16T08:47:44","modified_gmt":"2023-06-16T15:47:44","slug":"whats-next-for-ai-in-financial-services-in-2020-the-5-top-trends","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/industry\/microsoft-in-business\/business-transformation\/2020\/01\/17\/whats-next-for-ai-in-financial-services-in-2020-the-5-top-trends\/","title":{"rendered":"What\u2019s Next for AI in Financial Services in 2020? The 5 Top Trends"},"content":{"rendered":"<p><em>Follow Pascal Belaud on <a href=\"https:\/\/aka.ms\/Pascal\">LinkedIn<\/a><\/em><\/p>\n<p><span data-contrast=\"auto\">Right now, banking, capital markets, and insurance businesses are finding new uses for artificial intelligence.\u00a0<\/span><span data-contrast=\"auto\">AI is helping to transform and keep\u00a0<\/span><span data-contrast=\"auto\">ahead of\u00a0<\/span><span data-contrast=\"auto\">a number of critical\u00a0<\/span><span data-contrast=\"auto\">vectors,\u00a0<\/span><span data-contrast=\"auto\">including customer experience, regulatory compliance, new markets and business models, and security.<\/span><span data-contrast=\"auto\">\u00a0It\u2019s no longer a \u201cnice to have\u201d innovation technology, it\u2019s becoming a CX-level imperative. In this very moment, financial service organizations are making space for AI through financial resourcing, culture adaptation, and workforce skilling.\u00a0<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">To get ready for what\u2019s coming in 2020 for AI in the financial services industry, let\u2019s look at the top emerging trends:<\/span><\/p>\n<p>&nbsp;<\/p>\n<p><strong>Trend 1:\u00a0Enterprise adoption of the Data Science Lifecycle Process<\/strong><\/p>\n<p><span data-contrast=\"auto\">One enormous problem we\u2019ve seen, particularly in financial services, is the challenge of achieving ROI on data science. Eighty-seven percent of data science projects fail, mostly because of a misalignment on process.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">One of the points of misalignment is the shape of the process. IT and business departments expect the data science process to be linear, when it really isn\u2019t. As a result, data scientists try to conform to a linear process and leverage existing tools, and that can slow things down. Many data scientists aren\u2019t even ready to get down to brass tacks until three months into their role. Since IT doesn\u2019t have a great understanding of data science, a lot of the work doesn\u2019t make it into production. When there is no way to measure the impact of the data scientist\u2019s work, it can be terribly frustrating to the scientist. However, because data scientists are such a hot commodity, there\u2019s always another, better paying job for them to leap to. In fact, the average data scientist stays at a company for only 10 months, unfortunately bringing all of their\u00a0<\/span><span data-contrast=\"auto\">learnings with them<\/span><span data-contrast=\"auto\">. This leaves the company out a good amount of money for very little reward in the end.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Microsoft has recently partnered with GitHub to create some brand-new programming to layer the linear DevOps model over the non-linear data science model, allowing for a smoother process and greater democratization of valuable kernels of learning along the way. Stay tuned to this blog; we\u2019ll be rolling out more information on this new solution as it comes.<\/span><span data-contrast=\"auto\">\u00a0I<\/span><span data-contrast=\"auto\">n the\u00a0<\/span><span data-contrast=\"auto\">meantime, if you are interested\u00a0<\/span><span data-contrast=\"auto\">in<\/span><span data-contrast=\"auto\">\u00a0knowing more about this topic, feel free to\u00a0<\/span><span data-contrast=\"auto\">get in touch<\/span><span data-contrast=\"auto\">: <a class=\"notrap\" href=\"mailto:dslp@microsoft.com\" rel=\"noreferrer noopener\">mailto:dslp@microsoft.com<\/a><\/span><\/p>\n<p>&nbsp;<\/p>\n<p><strong>Trend 2: AI\u00a0is\u00a0creating the \u2018superworker\u2019\u00a0<\/strong><\/p>\n<p><span data-contrast=\"auto\">Initially, we in the tech industry thought that<\/span><span data-contrast=\"auto\">\u00a0AI was going to automate a big swath of jobs, but that\u2019s\u00a0<\/span><span data-contrast=\"auto\">actually\u00a0<\/span><span data-contrast=\"auto\">not what we\u2019re seeing<\/span><span data-contrast=\"auto\">\u00a0emerge today.\u00a0<\/span><span data-contrast=\"auto\">Instead, we\u2019re\u00a0<\/span><span data-contrast=\"auto\">seeing companies working on\u00a0<\/span><span data-contrast=\"auto\">augmenting the human worker with a suite of technology that makes them a \u201c<\/span><span data-contrast=\"auto\">superworker<\/span><span data-contrast=\"auto\">,<\/span><span data-contrast=\"auto\">\u201d<\/span><span data-contrast=\"auto\"> giving one worker the power of multiple workers.\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Why you don\u2019t want to take the human out of the \u201cMech Suit\u201d just yet: AI models are really good at predicting what was going to happen yesterday, so you need to have humans engaged\u00a0 to be able to give the kind of insights that only humans can provide. Things change; the ground-level truth is going to shift. If you give humans the ability to interact with those models and input the ground-level information back into the systems, you go from a static, limited data model, to a more accurate, living model.<\/span><br \/>\n<span data-contrast=\"auto\">While much of the training of the human workforce will be on the job,\u00a0<\/span><span data-contrast=\"auto\">organizations<\/span><span data-contrast=\"auto\">\u00a0can help\u00a0<\/span><span data-contrast=\"auto\">employees<\/span><span data-contrast=\"auto\">\u00a0develop their skillsets through<\/span><span data-contrast=\"auto\">\u00a0directing them to<\/span><span data-contrast=\"auto\">\u00a0the\u00a0<\/span><a href=\"https:\/\/www.microsoft.com\/en-us\/ai\/ai-business-school\"><span data-contrast=\"none\">Microsoft AI Business School<\/span><\/a><span data-contrast=\"auto\">, a no-cost, holistic resource and hands-on learning to get them more comfortable in the business world of AI.<\/span><\/p>\n<p>&nbsp;<\/p>\n<p><strong>Trend 3:\u00a0The rise of living models\u00a0<\/strong><\/p>\n<p><span data-contrast=\"auto\">Static models are the way of the past. In a static model, the AI might passively predict that the next best product to sell to Audience Y is Product A. However, the human salesperson on the ground level might disagree with this model and leverage their expertise to recommend selling Product B. Through inputting the expertise back into the model, workers become involved in retraining the AI and creating a living model.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Workers can help train models through\u00a0<\/span><span data-contrast=\"auto\">s<\/span><span data-contrast=\"auto\">imple\u00a0<\/span><span data-contrast=\"auto\">i<\/span><span data-contrast=\"auto\">nteraction,\u00a0<\/span><span data-contrast=\"auto\">which<\/span><span data-contrast=\"auto\">\u00a0could be a basic, \u201cthumbs up,\u201d or \u201cthumbs down,\u201d or checking a box; or through\u00a0<\/span><span data-contrast=\"auto\">e<\/span><span data-contrast=\"auto\">xplicit\u00a0<\/span><span data-contrast=\"auto\">i<\/span><span data-contrast=\"auto\">nteraction, which involves employees dictating to the model exactly what information it got wrong, and what it got right.\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Let\u2019s put this into practice: an<\/span><span data-contrast=\"auto\">\u00a0insurance<\/span><span data-contrast=\"auto\">\u00a0<\/span><span data-contrast=\"auto\">worker reviewing claims can leverage an AI model to help predict if a claim is fraudulent. The model might flag a claim with 80 percent certainty that the claim is fraudulent, however, the human with years of experience handling these kinds of claims might review the model\u2019s findings and determine that, in fact, the claim is not fraud. The human worker feeds<\/span><span data-contrast=\"auto\">\u00a0their knowledge<\/span><span data-contrast=\"auto\">\u00a0back into the model, helping train the model for greater\u00a0<\/span><span data-contrast=\"auto\">accuracy.\u00a0<\/span><\/p>\n<p>&nbsp;<\/p>\n<p><strong>Trend 4:\u00a0The birth of\u00a0Accountable AI\u00a0<\/strong><\/p>\n<p><span data-contrast=\"auto\">A huge objective in financial services is to be able to build<\/span><span data-contrast=\"auto\">\u00a0models\u00a0<\/span><span data-contrast=\"auto\">that are\u00a0<\/span><span data-contrast=\"auto\">free of\u00a0<\/span><span data-contrast=\"auto\">unwanted\u00a0<\/span><span data-contrast=\"auto\">bias<\/span><span data-contrast=\"auto\">. For example, you don\u2019t want a married couple with the same financial standing to apply for a credit card, only to discover that the wife has been denied based on apparent gender bias in the model. You n<\/span><span data-contrast=\"auto\">eed to be able to leave bias variables out\u00a0<\/span><span data-contrast=\"auto\">with accuracy,<\/span><span data-contrast=\"auto\">\u00a0which is why we need Accountable AI<\/span><span data-contrast=\"auto\">.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Financial services has been held back by regulations from using more accurate \u201cblack box\u201d models that have lacked transparency in favor of models that are less accurate but with high explain-ability. Unfortunately, less accuracy means they\u2019re leaving money on the table. Microsoft has invented Explainable Boosting Machines (EBMs) that can deliver greater accuracy as well as transparency, allowing companies to measure the exact impact of each variable in a model and manually edit for biases behind the scenes.\u00a0<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Visit the GitHub link to get more information on, or to try the new explainable algorithm:\u00a0<\/span><a href=\"https:\/\/github.com\/interpretml\/interpret\"><span data-contrast=\"none\">https:\/\/github.com\/interpretml\/interpret<\/span><\/a><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p>&nbsp;<\/p>\n<p><strong>Trend 5:\u00a0It\u2019s time to reskill the workforce\u00a0<\/strong><\/p>\n<p><span data-contrast=\"auto\">A lot of organizations are putting\u00a0<\/span><span data-contrast=\"auto\">considerable\u00a0<\/span><span data-contrast=\"auto\">thought into reskilling the<\/span><span data-contrast=\"auto\">ir<\/span><span data-contrast=\"auto\">\u00a0workforce. With\u00a0<\/span><span data-contrast=\"auto\">data\u00a0<\/span><span data-contrast=\"auto\">models being fed back by humans,\u00a0<\/span><span data-contrast=\"auto\">they\u00a0<\/span><span data-contrast=\"auto\">have to make sure the workforce has an understanding of how machine learning works.\u00a0<\/span><span data-contrast=\"auto\">They also need<\/span><span data-contrast=\"auto\">\u00a0<\/span><span data-contrast=\"auto\">to train existing skilled workers<\/span><span data-contrast=\"auto\"> to do their job slightly differently or to take on different roles.\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">One NGO,\u00a0<\/span><a href=\"https:\/\/www.npower.org\/\"><span data-contrast=\"none\">NPower<\/span><\/a><span data-contrast=\"auto\">\u00a0is working to reskill the workforce through helping<\/span><span data-contrast=\"auto\">\u00a0young adults and veterans obtain skills in IT<\/span><span data-contrast=\"auto\">\u00a0and placing them in internships in industries like finance<\/span><span data-contrast=\"auto\">. Microsoft is creating a<\/span><span data-contrast=\"auto\">n AI<\/span><span data-contrast=\"auto\">\u00a0curriculum for the program to\u00a0<\/span><span data-contrast=\"auto\">teach students how<\/span><span data-contrast=\"auto\">\u00a0to be a liaison between IT and business\u00a0<\/span><span data-contrast=\"auto\">and\u00a0<\/span><span data-contrast=\"auto\">data science and machine learning.\u00a0<\/span><span data-contrast=\"auto\">Eventually, we plan\u00a0<\/span><span data-contrast=\"auto\">to deploy\u00a0<\/span><span data-contrast=\"auto\">this\u00a0<\/span><span data-contrast=\"auto\">curriculum across universities\u00a0<\/span><span data-contrast=\"auto\">so that we can<\/span><span data-contrast=\"auto\">\u00a0train as many people as\u00a0<\/span><span data-contrast=\"auto\">possible<\/span><span data-contrast=\"auto\">\u00a0on AI.<\/span><\/p>\n<p>&nbsp;<\/p>\n<p><span data-contrast=\"auto\">Stay tuned to this blog for more\u00a0<\/span><span data-contrast=\"auto\">thoughts<\/span><span data-contrast=\"auto\">\u00a0on what\u2019s next for AI in the financial services industry. More\u00a0<\/span><span data-contrast=\"auto\">usable information and helpful insights<\/span><span data-contrast=\"auto\">\u00a0are coming your way.\u00a0<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Follow Pascal Belaud on LinkedIn Right now, banking, capital markets, and insurance businesses are finding new uses for artificial intelligence.\u00a0AI is helping to transform and keep\u00a0ahead of\u00a0a number of critical\u00a0vectors,\u00a0including customer experience, regulatory compliance, new markets and business models, and security.\u00a0It\u2019s no longer a \u201cnice to have\u201d innovation technology, it\u2019s becoming a CX-level imperative.<\/p>\n","protected":false},"author":182,"featured_media":44493,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"ep_exclude_from_search":false,"_classifai_error":"","_classifai_text_to_speech_error":"","footnotes":""},"categories":[1536,1733],"post_tag":[1662,1671,1599],"content-type":[],"coauthors":[1191],"class_list":["post-44472","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-business-transformation","category-era-of-ai","tag-ai","tag-data-science","tag-financial-services"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>What\u2019s Next for AI in Financial Services in 2020? The 5 Top Trends - Microsoft in Business Blogs<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.microsoft.com\/en-us\/industry\/microsoft-in-business\/business-transformation\/2020\/01\/17\/whats-next-for-ai-in-financial-services-in-2020-the-5-top-trends\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"What\u2019s Next for AI in Financial Services in 2020? 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