Case study: INSURANCE

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 the 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 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.

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 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 – even making it enjoyable for all participants.

To do this, we targeted three areas:

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