Blog | Feb 28, 2022

Four Real-time Insurance Underwriting Use Cases for Intelligent Automation

Four Real-time Insurance Underwriting Use Cases for Intelligent Automation

Insurtech competition is on the rise and traditional insurance companies are finding it difficult to match the agility of these new entrants. Customers are expecting faster and more personalized services to meet their buying habits for other goods and services. While insurance products differ from a pair of Bluetooth headphones that can be purchased with a click of a button, that doesn’t change the “get it now” experience that customers desire.

The Deloitte Center for Financial Services indicated that underwriting can be split into several key stages and, in four of them, we can find real-time underwriting use cases for intelligent automation. So how can insurance companies deliver on these expectations and where does intelligent automation fit?

Automation is key

According to the U.S. Bureau of Labor Statistics, the decline in insurance underwriters is expected to continue between 2020 and 2030 as current underwriters exit the labor force for retirement or another industry. Systems and automation will need to fill this void to offset the losses.

Building an intelligent automation solution that offers real-time insurance underwriting will allow the remaining underwriters to leverage their expertise and experience to develop the rules and training for these automation solutions rather than spending their work efforts collecting and managing data. These activities can be broadly grouped into several categories as defined by Deloitte: intake, triaging, risk assessment, pricing, and processing.

Analysis by The Deloitte Center for Financial Services and Deloitte Consulting LLP


Intelligent automation can be applied in these areas to provide insurance companies with greatly improved responsiveness within the underwriting process.

Intake

Real-time insurance underwriting requires building on the strengths of traditional RPA and intelligent automation. The task of gathering data from multiple sources can be removed from the human insurance underwriter and engaged fully by the intelligent digital worker. This transition of data management to an automated process not only offloads the mundane work, but also provides a more rapid and accurate collection of this key information. While not all information may be in digital format to start with, enabling the automation with smart technologies such as optical character recognition (OCR) and intelligent document processing (IDP) will give it the capability to read the documents, extract the important data, and, ultimately, deliver the feed information into the underwriting process.

Triaging and Risk Assessment

Bulk data needs to be evaluated and refined to provide the insights into the risk characteristics that will be considered during the underwriting process. Intelligent automation supports the triage of this data through rules and artificial intelligence (AI). Rules developed through the experience of underwriters can categorize information and help direct the customer to the right product set for their needs. Leveraging AI to complete many of the evaluations for lower-value policy submissions will reduce the human underwriters’ workload to the higher-value or more complex policies. The expertise of underwriters to train the AI on these decisions will help the intelligent automation solution move the process along more quickly towards real-time insurance underwriting.

Pricing

Pricing models for each policy built on risk factors and customer characteristics can be modeled through AI and machine learning to advise the policy pricing that will offer the best return. The models to create this pricing can leverage the thousands of policies that were built using traditional underwriting. This process of building pricing models through historical analysis will need to rely heavily on data analytics capabilities and the intelligent automation solution.

Processing

Traditional RPA and intelligent automation platforms excel at the administration functions within the insurance underwriting process. Data in the various back-end platforms that is used to track and manage policies and claims can be pulled into the formats required for compliance and governance functions, as well as to manage the overall workflow for the policy underwriting. This allows the underwriting actions to be completed more rapidly for simple policies, allowing a better customer experience.

What’s next for underwriting?

While intelligent automation can build a foundation for real-time insurance underwriting, preparing for this solution requires several prerequisites:

  1. Creating a vision and strategy to support real-time insurance underwriting
  2. Educating current underwriters about the possibilities with intelligent automation and AI
  3. Aligning the workflows for policy underwriting to the correct resources to perform the underwriting, whether human or digital workers
  4. Exposing machine learning systems to the thousands of current policies
  5. Engaging the right people to support the intelligent automation solutions that deliver on real-time insurance underwriting

The insurance industry is evolving, and the workforce is shrinking quickly. There is a need for traditional insurance companies to engage these new resources now before their businesses are overtaken by native, born-digital Insurtech firms.