Using AI in Insurance Underwriting for Accelerated Time-to-Value

For insurance companies, balancing customer expectations with the rigorous requirements necessary to mitigate risks poses a challenge. Especially when you’re using manual underwriting processes. By turning to artificial intelligence (AI) in insurance underwriting, you avoid costly delays, streamline your employees’ processes, improve accuracy, and create an optimal customer experience.

In this blog post, you’ll learn how AI facilitates greater efficiency in underwriting. We’ll discuss four ways to use AI to improve underwriting processes and give you an overview of best practices to avoid risks.

AI helps accelerate the insurance underwriting process by eliminating repetitive tasks and giving you a way to analyze vast data sets for improved insights and better underwriting decisions.

AI-powered underwriting gives you a competitive edge by:

• Reducing data entry errors
• Decreasing time to quote
• Eliminating manual processes and mundane tasks
• Improving your ability to determine risk
• Increasing quote-to-bind ratio
• Boosting customer satisfaction
• Reducing customer churn

Insurance customers expect a personalized experience that's fast, affordable, and transparent. Your underwriting department needs to minimize risk factors, which means having access to massive volumes of data and using it to gain insights for fast, better-informed decisions.

With the advantage of AI solutions, your underwriters can assess risks, reduce errors that are common to data entry, and keep track of a wealth of information to improve every aspect of the process.

As new advances and trends emerge for AI-powered software, insurance underwriters can use updated technologies to streamline their workflows, gain more time for high value tasks, get comprehensive risk insights faster, and ultimately improve the experience for customers.

Here are four specific ways to use AI in the underwriting process:

1. Create, monitor, and process customer applications
2. Streamline risk assessment and mitigation
3. Improve casework management for underwriters
4. Improve communications with policyholders

1. Application processing

From creation through processing, automation makes the entire application process a lot easier. AI also allows the underwriter to extract information from data sources to automate the collection and digestion of data to speed time to quote.

2. Risk assessment and mitigation

With an intense amount of documentation and data to keep track of, AI-driven underwriting delivers crucial support to help underwriters assess and mitigate potential risks. Through automation, your team gains access to information from every relevant data source and gets the support necessary to deliver precise risk evaluations.

AI plays a central role in intelligence gathering––from analyzing past claims to medical histories and beyond. While it ultimately falls to the underwriter to make final decisions, AI offers greater precision for more efficient insurance underwriting.

3. Case management

AI-powered underwriting solutions improve case workflows, including initial review, case priority, and logic-based assignment of underwriting. Underwriters gain tools to improve collaboration with colleagues resulting in a faster claims process.

From using AI to analyze data for better insights to automating data extraction from documentation, AI tools benefit case management and speed claims processing. 

4. Policyholder communication

More support for your team also means a better experience for customers. Tools such as online applications, chatbots, and automatic claims processing help boost communication and allow clients to serve themselves 24/7. This frees the underwriter from having to complete simple routine tasks and gives them the bandwidth to deliver a more personalized customer experience and faster underwriting decisions faster.

It’s no secret that AI comes with some risks—we’ve all seen errors from generative AI, like ChatGPT, wreak havoc. In a highly regulated field like insurance, it’s important to develop approaches that not only identify but mitigate potential risk exposure in the solutions we use.  

With the right tactics, your organization can use AI safely and effectively.

Personal and proprietary data

Organizations need to consider the types of information they can trust to AI––particularly open or public AI. Large language models (LLMs) often train their models on user information, which puts your data at risk.

To mitigate risks:

Make sure your vendor has strong privacy and security policies, like the ability to offer private AI, and that your data is not being shared or used to train models. And develop an AI policy for your employees so they don’t inadvertently share sensitive information.

Regulatory compliance

In a heavily regulated industry, AI adds another layer of complexity. Depending on where you do business, your organization must maintain compliance with several government entities.

To mitigate risks:

Put governance and control systems in place, like an AI risk committee. Add AI experts to your compliance team and investigate frameworks to help manage your AI risk.

AI bias

Garbage in, garbage out. AI models only produce results based on the information they’ve been trained with, and that often includes human error. Bias has been a problem in many AI models, so it’s important to guard against issues such as gender or ethnic bias leading to denials of customer applications and claims.

To mitigate risks:

Maintain human intervention and oversight of your AI-assisted underwriting processes. Review AI models regularly to discover and address bias.

There’s no doubt that AI simplifies the process for underwriters, increasing employee satisfaction and having a marked impact on performance. These tools can also improve the experience for current and potential policyholders, making it far easier and more transparent to get the services they want and need.