case study

Insurers: Need AI and Predictive Models to Improve and Expedite Your Underwriting Processes?
We Got IT.

Case Study at a Glance

CLIENT

A major life insurance company

THE PROBLEM

Our client’s underwriting process had been slow and inefficient, often taking up to eight weeks to render a decision on a prospective applicant for life insurance. This company needed a way to quickly ascertain risk factors associated with health and mortality to categorize applicants without undue intrusion, and enable underwriters with the tools to make faster, effective decisions.

THE VERTEX SOLUTION

Vertex applied AI and predictive modeling to create a system that pulled data from trusted resources. We built a machine-learning, interactive system that quickly scores applicants based upon historic data along with information provided by applicants. The resulting system created a faster risk categorization approach that speeded up the policy underwriting process.

THE RESULTS

These new models generate accurate and reliable underwriting decisions, often reducing the process from weeks to days – and in some cases, minutes. Our client has saved considerable time and money, and has realized improved internal efficiencies.

Problem and Background

When it comes to assessing risks, life insurance companies 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’s profitability. The underwriting process is designed to help insurance companies and their clients find the sweet spot and strike the appropriate balance.

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 – or less risky – the prospect’s classification, the less the premium costs.

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 HIPPA, 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.

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.

Vertex Solution

Vertex studied how its 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 – even making it enjoyable for all participants. To do this, we targeted three areas:

  1. Offer best-in-class customer experience by simplifying the application process
  2. Reduce the time-to-delivery cycle from weeks to days or minutes by increasing operational effectiveness
  3. Lower costs of the process by mitigating risks

By helping our client leverage Artificial Intelligence (AI) tools and other predictive models, we were 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 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.

With Vertex’s 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, users were able to train the model to learn from past instances. The resulting system provided a baseline measurement that constantly improved.

Types of historic data Vertex’s AI tool collects on prospective life-insurance customers:

  • Board of Motor Vehicles (BMV) records
  • Medical records
  • Prescription database records
  • Credit bureaus (Experian, Equifax, TransUnion)
  • Insurer’s internal database (prior applications, claims profiles)
  • External resources including social media streams

Predictive models aggregated data from numerous sources to create correlations between historic evidence and mortality factors. Interactive dashboards showed analytical data and made it easy for viewers to interpret. Eliminating much of the probability for human error, AI tools provided all the material for people to review. Underwriting teams, armed with this data, were able to quickly verify and make decisions.

We reimagined the entire process to innovate quicker and more efficient methods for insurers to handle underwriting pre-analysis. This saved everyone time and money, not to mention the reduced hassles inherent in investigating personal information.

Results and Features

Our client now relies on accurate systems to pull data from trusted sources. The process is quick, efficient, and less intrusive into people’s personal lives. Decisions can be made quickly and with greater dependability. Underwriters still review all the information and can override the model. As users provide feedback, they improve the model’s accuracy.

  • New models can generate reliable and accurate underwriting decisions within 60–90 seconds for 30-50% of the applicants
  • An adaptive workflow-driven application process results in underwriting decisions within days for 50-70% of the applicants
  • The risks associated with underwriting decisions can remain at the current level or improve

Benefits

  • Faster underwriting decisions
  • Increased cost savings
  • Improved operational effectiveness

About Us

Vertex is a business and technology consulting firm that helps customers transform their business processes with leading edge technology. Learn more at www.vertexcs.com.

Inspire. Innovate. Implement.
We Got IT.

Vertex Computer Systems

[email protected]
Office: 330-963-0044
25700 Science Park Drive #280 / Beachwood, Ohio 44122

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