AI is Reshaping Insurance: Is Your Organization Prepared for the Change?

By Nestor Lopez, CIO, ProSight Specialty Insurance

Nestor Lopez, CIO, ProSight Specialty Insurance

Artificial intelligence (AI) and Machine Learning (ML) has tremendous potential for the insurance industry, from driving operational efficiencies to improving the customer experience. Thus far, however, some organizations have been slow to adopt AI/ML, largely because of not having the expertize to manage data properly to avoid errors and bias, and reluctance to experiment with new technologies.

To pave the way for AI/ML, organizations need to focus on overcoming barriers to adoption such as having the right resources and partners, identifying the right use cases and building a culture that encourages innovation.

How are insurance companies already using AI/ML today?

For those organizations that can integrate AI/ML into their customer touchpoints, they will have a competitive advantage.

For example, voice recognition and speech analytics, (think Amazon’s Alexa and Apple’s Siri), have become and will continue to be, crucial tools for improving the customer experience for insurance companies.

“To keep pace, insurers must take appropriate measures now to pave the way for AI/ML adoption

At ProSight, we want to deliver a differentiated experience to our customers, so we are implementing speech analytics in our contact center to help identify and analyze sentiments during interactions with our policyholders. We will use this data to improve our training programs and to identify where our contact center employees can improve quickly. While the technology isn’t perfect yet, over time, it will be used to assist our contact center representatives in real-time during customer calls. By listening to the customer, the speech analytics solution will be able to process what information the customer needs and will deliver that data to the contact center representative immediately. In this way, we will be able to make the job of our employees more comfortable and ultimately deliver a better customer experience.

We are also testing voice recognition to assist our employees when they need support from other shared service functions such as the help desk and application production support and to perform routine tasks such as searching for specific policy and claim information contacting other employees, starting a video conference or learning more about their health benefits, to name a few. This will help ProSight to scale as we continue to grow our business.

Other areas are ripe with opportunity for improvement with the use of AI/ML are risk scoring, pre-underwriting, fraud identification, and cybersecurity. At ProSight, we are evaluating how to leverage our data and ML algorithms to build risk scores to assess accounts before they are up for renewal. These efforts, combined with our deep industry expertise, have enabled us to begin building express renewal workflows and identifying those accounts that may require more underwriting guidance.

Overcoming challenges to adopting these and other AI/ML technologies

Organizations typically fail at the adoption of new technologies such as AI/ML because they try to scale too quickly, do not have the right talent or put too little emphasis on gathering and understanding the data needed to fuel an AI/ML engine. Quite simply, AI/ML adoption shouldn’t be treated like a tactic; it’s a strategy, and leaders must be strategic in its implementation.

Before companies spend any resources on AI/ML adoption, they must get the right data and processes in place. AI/ML algorithms use data to learn and make decisions, so if it’s fed too little or incorrect data, it may draw false conclusions that hinder, rather than help organizations.

Hiring the right resources and finding the right partners in advance of AI adoption can help overcome the challenge of gathering, understanding, and cleansing internal and external data. Most insurance companies are solely relying on third-party partners to build their AI/ML models, and it’s causing more failures than successes. By building-house teams, in addition to third-party partners, organizations can improve their chances of successfully implementing AI/ML use cases since in-house employees will be better positioned to understand the organization’s challenges and opportunities.

Although AI/ML should be implemented with care, organizations shouldn’t be shy about embracing new technologies and even bringing it into the office space for experimental purposes. It’s hard to understand how technology can benefit an organization without first seeing and understanding how it works. Leaders must, therefore, adopt a culture that encourages experimentation and even failure. New technologies require trial and error; as long as the expectation is that the AI/ML journey is ongoing and will take time to test, implement, and scale.

Where do we go from here?

We’re at a significant inflection point in implementing and using AI in the insurance industry. Forward-thinking insurers have already differentiated themselves in the marketplace with AI adoption and will lead the way in transforming the industry.

To keep pace, insurers must take appropriate measures now to pave the way for AI/ML adoption. Meaningful change won’t happen overnight, but with a cohesive AI/ML strategy, a culture open to failing fast and a healthy dose of patience, insurance leaders have the opportunity to create a competitive advantage by enhancing the customer and employee experiences and introducing operational efficiencies.

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