{"id":270,"date":"2020-09-24T19:51:57","date_gmt":"2020-09-24T19:51:57","guid":{"rendered":"https:\/\/machine-learning.webcloning.com\/2020\/09\/24\/reduce-loss-run-reporting-costs-with-intelligent-automation\/"},"modified":"2020-09-24T19:51:57","modified_gmt":"2020-09-24T19:51:57","slug":"reduce-loss-run-reporting-costs-with-intelligent-automation","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2020\/09\/24\/reduce-loss-run-reporting-costs-with-intelligent-automation\/","title":{"rendered":"Reduce Loss Run Reporting Costs with Intelligent Automation"},"content":{"rendered":"
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One of the thorniest parts of the commercial insurance underwriting process is getting an accurate picture of the applicant\u2019s loss history, generally gleaned from loss run reports. But it can be a cumbersome process to collect all the reports and accurately extract data from them for input into the underwriting system \u2013 making it an excellent candidate for intelligent automation in insurance .\u00a0<\/p>\n

Part of the problem is the amount and variety of the data that comes from the reports, and the fact that reports from different insurance companies are not likely to use the same format. That makes it difficult to use templated approaches<\/a> or robotic process automation tools to automatically extract relevant data. Such tools only work well with highly structured documents and content.\u00a0<\/p>\n

Loss run reports, which are equivalent to credit scores in the insurance business, are anything but. The reports contain data including the business name, insurance company name, policy number, reason for the claim, type of claim, and various dates \u2013 loss report date, date of claim and date the claim was reported. The reports also include numerous monetary amounts, including any amounts paid by insurers in legal costs, property damage, medical costs and more.\u00a0<\/p>\n

Typically, a human has to examine each of the loss run reports looking for each piece of relevant information and enter it all into the company\u2019s underwriting system. It\u2019s labor-intensive work that\u2019s both time-consuming and error-prone.\u00a0<\/p>\n

Errors are the enemy in the insurance underwriting process. If loss run data is entered incorrectly, the underwriter may well come up with a policy price that\u2019s incorrect. A price that\u2019s too high for the actual level of risk puts the sale of the policy in jeopardy; too low and it puts the insurer at an unacceptable level of risk.\u00a0<\/p>\n

Related Article: How Intelligent Automation tames the Healthcare Document Beast<\/a><\/p>\n

Automating loss run report processing\u00a0<\/strong><\/h4>\n

Intelligent process automation (IPA)<\/a> offers a way to automate the processing of loss run reports while ensuring accuracy.\u00a0<\/p>\n

Unlike templated approaches to insurance automation<\/a>, with IPA it\u2019s not necessary to know exactly where in a given loss run report each piece of relevant data is located. That\u2019s because IPA uses technologies including natural language processing and transfer learning to \u201cread\u201d documents much like a human would. An effective automated document processing tool will be able to discern context, enabling it to find relevant data no matter where\u2019s it\u2019s located.\u00a0<\/p>\n

With IPA, an insurance company can fully automate the processing of loss run reports by training the model on just a few dozen actual reports. The IPA tool will then be able to \u201cread\u201d the reports and pull out relevant data for input into the underwriting system. A process that may take a human hours can be done in seconds.\u00a0<\/p>\n

What\u2019s more, an insurance process automation tool doesn\u2019t get tired and make mistakes such as keying in data incorrectly. The tool is highly accurate, able to handle some 95% of documents \u2013 leaving only a few for humans to handle.\u00a0<\/p>\n

Indico IPA: Automation for insurance and more<\/strong><\/h4>\n

At least, that\u2019s what Indico\u2019s IPA platform can do, because it\u2019s based on a model with more than 500 million labeled data points, enough to enable it to understand human language and context \u2013 enough to handle even complex loss run reports.<\/p>\n

To learn more about how Indico provides automation for insurance processes (and more), download this white paper from experts at the Everest Group,\u00a0\u201cUnstructured Data Process Automation<\/a>,\u201d or contact us<\/a> to arrange a demo.\u00a0<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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