{"id":837,"date":"2021-09-05T15:53:28","date_gmt":"2021-09-05T15:53:28","guid":{"rendered":"https:\/\/salarydistribution.com\/machine-learning\/2021\/09\/05\/3-industry-examples-of-how-unstructured-data-processing-is-key-to-automation-success\/"},"modified":"2021-09-05T15:53:28","modified_gmt":"2021-09-05T15:53:28","slug":"3-industry-examples-of-how-unstructured-data-processing-is-key-to-automation-success","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2021\/09\/05\/3-industry-examples-of-how-unstructured-data-processing-is-key-to-automation-success\/","title":{"rendered":"3 Industry Examples of How Unstructured Data Processing is Key to Automation Success"},"content":{"rendered":"
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All enterprises are interested in automating numerous processes as part of their digital transformation efforts. But they often settle for less than an actual \u201ctransformation\u201d when it comes to processes that involve unstructured content and data.\u00a0<\/span><\/p>\n

Technologies such as robotic process automation (RPA) are good at automating repetitive tasks that involve highly structured data sources and documents. Need to pull some data from a spreadsheet and put it into an ERP system? RPA can help you there. You\u2019ll certainly save some data entry clerks some time, but ask yourself, does that amount to digital transformation?\u00a0<\/span><\/p>\n

On the other hand, if you have an intelligent data processing tool that can automate processes involving unstructured content and data \u2013 things like contracts, invoices, lease agreements, insurance claims \u2013\u00a0now you\u2019re getting somewhere.\u00a0<\/span><\/p>\n

Let\u2019s go through a few examples that demonstrate how intelligent automation tools are crucial to digital transformation.\u00a0<\/span><\/p>\n

Intelligent automation in banking<\/b><\/h5>\n

If you visit a website for almost any major bank or financial organization, you\u2019ll be greeted by a \u201cvirtual assistant\u201d. This chatbot will try to help you find information you\u2019re looking for. This form of artificial intelligence saves the bank\u2019s call center resources time from fielding questions from customers or prospective customers.\u00a0<\/span><\/p>\n

While that\u2019s all well and good, it\u2019s not transforming the business. Yes, it saves money and means the bank likely needs fewer customer service representatives, but it\u2019s hardly game-changing technology.\u00a0<\/span><\/p>\n

Now consider the many hours that highly skilled legal analysts at banks must spend pouring over contracts, ensuring specific terms and legal requirements are met, and complying with various corporate policies and industry regulations, similar to the LIBOR transition. A good, properly trained intelligent document processing solution relieves them of manual review.. The tool could \u201cread\u201d those documents much like a human would, pull out specific\u00a0 terms, flag those that may be troublesome, and generally save the analyst many hours per contract. Now you\u2019re talking about expanding the capacity of highly paid professionals, who can now dedicate more of their time to other, more strategic projects. That\u2019s transformative.\u00a0<\/span><\/p>\n

Intelligent data processing in insurance\u00a0<\/b><\/h5>\n

For years, the insurance industry has been using RPA, optical character recognition, and templates to automate simple processes involving structured data.\u00a0 But they would hit a wall when it came to automating processes involving unstructured content.\u00a0<\/span><\/p>\n

In a<\/span> Q&A with <\/span><\/a>, Sean Nicolello, VP of Strategy and Planning for MetLife, explains some of their common challenges:\u00a0<\/span><\/p>\n

\u201cOne was, I have these contracts and I just want to find out \u2018if x then y\u2019 but I have to go through every word to figure it out. Or, two, I have all these invoices and we have to manually review them because they\u2019re different every time. Three, I have an email inbox with a small army of people that read hundreds of emails a day just so they can move the email to the right team to take action.\u201d<\/span><\/p>\n

MetLife is now using the\u00a0 <\/span>Intelligent Process Automation<\/span><\/a> (IPA) platform to automate all these processes. What\u2019s more, it can now unlock insights from documents that go back more than 100 years.\u00a0<\/span><\/p>\n

\u201cWe\u2019d like to be able to use that data to help us better predict mortality and morbidity,\u201d Nicolello said. \u201cA small adjustment to our actuarial models can result in billions of dollars of savings and revenue generation over the next 5 to 15 years.\u201d<\/span><\/p>\n

MetLife is\u00a0 realizing significant savings by automating processes involving unstructured data. \u201cWe have found about $90M in value through hours saved that we can unlock in the next five years for our businesses by using intelligent process automation on unstructured data,\u201d Nicolello remarked.\u00a0<\/span><\/p>\n

Automating commercial real estate rent rolls<\/b><\/h5>\n

Commercial real estate is another industry that\u2019s heavily document-dependent. From lease agreements to contracts, most of the documents are also unstructured, putting them out of reach for RPA and OCR-based approaches to automation.\u00a0<\/span><\/p>\n

One good example is the rent roll. Rent rolls contain all kinds of primary data on properties, including the address, square footage, lot size and amenities, data on the tenant, monthly rent, due dates, payment history, and more. In short, the rent roll paints a picture of a given property, including how much it\u2019s worth to the owner.\u00a0<\/span><\/p>\n

While examining a single rent roll is valuable, imagine if you could <\/span>automate rent roll processing<\/span><\/a> to pull data from hundreds of rent rolls and feed it to a data analysis engine to compare properties to one another. Now you can find properties where a rent increase is warranted, properties that are undervalued and worth investing in, and red flags that indicate attention is warranted.\u00a0<\/span><\/p>\n

That\u2019s precisely the kind of transformative capability that intelligent automation tools can provide for commercial real estate companies.<\/span><\/p>\n

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Achieve digital transformation with\u00a0<\/strong><\/h5>\n

RPA, OCR templates. and chatbots all have a place in the automation journey. But they are just a starting point; specifically a possible point solution. To achieve truly transformative results, you need\u00a0 to automate entire processes that involve unstructured documents\u00a0 and convert them into digitized, usable data.\u00a0<\/span><\/p>\n

That\u2019s the problem that we set out to address. With the\u00a0<\/span>IPA platform<\/span><\/a>, your business process experts \u2013 not data scientists \u2013 can build ingestion models that work with high accuracy. You\u2019ll dramatically increase the capacity of your business experts, enabling them to spend more time on strategic endeavors that further your digital transformation efforts.\u00a0<\/span><\/p>\n

Interested seeing how an intelligent process automation platform can automate your organization?\u00a0<\/span>Click here<\/span><\/a> to schedule your free demo. <\/span><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

https:\/\/indico.io\/blog\/3-industry-examples-of-how-unstructured-data-processing-is-key-to-automation-success\/<\/p>\n","protected":false},"author":0,"featured_media":838,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[3],"tags":[],"_links":{"self":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts\/837"}],"collection":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/comments?post=837"}],"version-history":[{"count":0,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts\/837\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/media\/838"}],"wp:attachment":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/media?parent=837"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/categories?post=837"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/tags?post=837"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}