{"id":48555,"date":"2020-08-03T15:35:08","date_gmt":"2020-08-03T22:35:08","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/industry\/microsoft-in-business\/?p=48555"},"modified":"2023-06-16T08:46:52","modified_gmt":"2023-06-16T15:46:52","slug":"bringing-ai-to-life-in-financial-services","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/industry\/microsoft-in-business\/business-transformation\/2020\/08\/03\/bringing-ai-to-life-in-financial-services\/","title":{"rendered":"Bringing AI to Life in Financial Services"},"content":{"rendered":"<p>Change is no easy task. As with most new technologies, AI adoption brings its share of cost and risk as enterprise leaders and department leads work together to build out their experience. Merely identifying an entry point has brought plenty of AI projects to a halt. Since early 2019, I\u2019ve worked with dozens of IT innovation teams and business leaders at banking, capital markets, and insurance organizations to help guide their AI adoption efforts. These financial services organizations eagerly want to use AI to augment their productivity, reduce costs and minimize human error, but frequently struggle with how to start their implementation. In that time, I\u2019ve identified three AI scenarios that are most frequently requested by these business leaders:<\/p>\n<ul>\n<li>Forms recognition\/OCR<\/li>\n<li>Call center automation<\/li>\n<li>Text analytics<\/li>\n<\/ul>\n<p><strong>Reduce repetitive work with forms recognition<br \/>\n<\/strong>The first and most popular use case is for large enterprises inundated with documents that rely on human labor to extract and process the information in those forms. These documents come in all varieties \u2014 customer account forms, invoices, PDF\u2019s, or even insurance claim documents. As a result, they require an AI solution that:<\/p>\n<ul>\n<li>Performs optical character recognition (OCR) on the document for both printed and handwritten text<\/li>\n<li>Extracts specific fields such as name, address, account number, etc.<\/li>\n<li>Takes that extracted information and feeds it into some back office system for further processing<\/li>\n<\/ul>\n<p>The prescriptive AI solution we\u2019ve built for document management consists of OCR and other \u201cvision\u201d related AI products that scan the documents and convert those pixels into text. This process is followed by another AI tool that extracts the required fields \u2014 typically a \u201cbox\u201d that reads name, address, account number, etc. \u2013 from the form itself. The extracted information is then fed to a Robotic Process Automation (RPA) system that sends the information to some back office system. Finally, the back office system performs an action on this new data like issuing a check for an insurance claim.<\/p>\n<p><strong>Obtain valuable insight with call center automation and text analytics<\/strong><\/p>\n<p>Much like the need for documentation, call centers play a huge role in corporate accountability and maintaining a positive customer experience. Every day, thousands of service representatives engage millions of customers to ensure their expectations are being met. For most organizations, this means recording and logging conversations and tracking resolutions. Call center automation uses AI to go beyond prerecorded service lines and helps enterprises:<\/p>\n<ul>\n<li>Transcribe recordings into text<\/li>\n<li>Perform sentiment analysis<\/li>\n<li>Translate language as necessary<\/li>\n<li>Analyze recordings<\/li>\n<\/ul>\n<p>Text analytics is an excellent partner for automated call center transcriptions. The option to use AI as a resource for developing insights from documents and legal papers gives growing organizations a considerable productivity advantage. Instead of digging through call logs, analysts can focus their efforts on making more valuable recommendations for the business.<\/p>\n<p>For call centers, text analytics are mostly being used to observe agent performance and analyze customer sentiment around an organization\u2019s products and services.\u00a0We\u2019ve also seen text analytics being used to extract information from call center recordings to spotlight trending issues happening on a certain day, week, month, etc.<\/p>\n<p>This immediate access to translation, sentiment, and analysis delivers incredible value for finance houses. By extending the knowledge capture abilities of the call center beyond the traditional CRM, call center automation generates valuable data that organizations can use to redefine their product offerings, adjust messaging, and more.<\/p>\n<p><strong>Start your AI journey<\/strong><\/p>\n<p>Don\u2019t let AI feel like an inaccessible undertaking. At Microsoft, we\u2019ve developed pre-built AI models for forms recognition, call center transcriptions, and text analytics to provide accessible automation solutions without the need for a large team of data scientists.<\/p>\n<p>The road to automation is far from insurmountable. While a handful of companies have used custom AI models for over a decade, the introduction of these pre-built models for vision, speech, and text analytics show an attractive ROI and are a great way for enterprises to get started on their AI journey.\u00a0You can find these solutions and more at\u00a0<a href=\"https:\/\/azure.microsoft.com\/en-us\/services\/cognitive-services\/\">Microsoft\u2019s Cognitive Services Suite<\/a>, or reach out to me on <a href=\"https:\/\/www.linkedin.com\/in\/akivashmuelov\/\">LinkedIn<\/a> to start a conversation about how we can bring AI to your organization.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Change is no easy task. As with most new technologies, AI adoption brings its share of cost and risk as enterprise leaders and department leads work together to build out their experience. Merely identifying an entry point has brought plenty of AI projects to a halt.<\/p>\n","protected":false},"author":182,"featured_media":48558,"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":[1662,1599],"content-type":[],"coauthors":[1260],"class_list":["post-48555","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-business-transformation","category-era-of-ai","tag-ai","tag-financial-services"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Bringing AI to Life in Financial Services - 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\/08\/03\/bringing-ai-to-life-in-financial-services\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Bringing AI to Life in Financial Services - Microsoft in Business Blogs\" \/>\n<meta property=\"og:description\" content=\"Change is no easy task. As with most new technologies, AI adoption brings its share of cost and risk as enterprise leaders and department leads work together to build out their experience. 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