{"id":726,"date":"2020-09-19T16:14:38","date_gmt":"2020-09-19T20:14:38","guid":{"rendered":"https:\/\/poiseddevelopers.com\/vertex-old\/?post_type=case_study&p=726"},"modified":"2023-02-28T11:01:08","modified_gmt":"2023-02-28T16:01:08","slug":"expedite-underwriting-processes","status":"publish","type":"case_study","link":"https:\/\/poiseddevelopers.com\/vertex-old\/resources\/case-studies\/expedite-underwriting-processes\/","title":{"rendered":"Insurers: Need AI and Predictive Models to Improve and Expedite Your Underwriting Processes?
We Got IT."},"content":{"rendered":"

\"life<\/h3>\n

Problem and Background<\/h3>\n

When it comes to assessing risks, life insurance companies<\/a> walk a tightrope. On one hand, they need to offer competitive rates to prospective clients, but on the other, they need to ensure their underwriting risks are managed well and ensure the company\u2019s profitability. The underwriting process is designed to help insurance companies and their clients find the sweet spot and strike the appropriate balance.<\/p>\n

The nature of underwriting is to collect and examine large quantities of personal data and determine risk levels of potential clients. Prospects are classified into parameters defined by numerous factors including medical and health records, prescription medicines, credit scores, income levels, driving records, hobbies, and other elements. Historic data helps predict future behavior and risk. The better \u2013 or less risky \u2013 the prospect\u2019s classification, the less the premium costs.<\/p>\n

Gathering this much data takes time. A lot of it. This is a huge challenge for life insurance underwriters. The information is sensitive, much of it is protected by HIPAA, and it needs to be verified. Prospects need to sign waivers to allow insurers to pull this data. The process can take anywhere from two to eight weeks.<\/p>\n

Our client was looking for a way to expedite and streamline the decision-making process. Ideally, they wanted a way to categorize and rate data from several sources, enabling underwriters to render optimal decisions. This tool would need to help underwriters arrive at quicker conclusions based on calculated risk projections.<\/p>\n

Vertex Solution<\/h3>\n

Vertex Computer Systems studied how our client approached the process, noting opportunities to expedite areas wherever possible. As it existed, the process was intrusive, slow, and expensive. The goal was to reimagine underwriting in such a way that the process would be completely transformed \u2013 even making it enjoyable for all participants. To do this, we targeted three areas:<\/p>\n

    \n
  1. Offer best-in-class customer experience by simplifying the application process<\/li>\n
  2. Reduce the time-to-delivery cycle from weeks to days or minutes by increasing operational effectiveness<\/li>\n
  3. Lower costs of the process by mitigating risks<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

    Discover how AI and predictive models can expedite the underwriting process and improve internal efficiencies. <\/p>\n","protected":false},"featured_media":1024,"parent":0,"template":"","categories":[112,116,121,122,123],"acf":{"is_a_flyer":false,"additional_information":"

    By helping our client leverage Artificial Intelligence<\/a> (AI) tools and other predictive models, Vertex Computer Systems was able to create an adaptive-workflow process with detailed tracking that built an ongoing history from which our systems could continuously draw upon. Hence, this type of machine learning<\/a> provided a smart, constantly evolving collection of data that helped our client accurately score prospects into risk categories. By combining existing client sources of information with new market data, we created powerful predictive models that mitigated risk.<\/p>\n

    With Vertex\u2019s AI tool, underwriters were able to survey the influencing data points surrounding a potential client. And, because the tool employed machine learning and predictive modeling<\/a>, users were able to train the model to learn from past instances. The resulting system provided a baseline measurement that constantly improved.<\/p>\n

    Types of historic data Vertex\u2019s AI tool collects on prospective life-insurance customers:<\/p>\n