Utilizing AI to transform the life insurance industry

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The limitless number of opportunities presented by artificial intelligence (AI) means that many businesses are now examining how they can incorporate it into their operations. However, before life insurance companies rush to determine their AI strategy, governance and implementation, they need to take an introspective look at their organizations and determine whether they’re properly prepared to take advantage of AI’s many potential benefits.

Life insurance is transforming
The life insurance industry is in the middle of a technology overhaul. Companies have recognized that their legacy technology, both front-office and back-office, can’t keep up with modern demands and is becoming too expensive to maintain. The dated systems also aren’t compatible with new technology, like AI, and can’t fully and effectively leverage the large volumes of data insurers have accumulated.

As a result, life insurers are embarking on digital transformation journeys in an effort to bring their operations into the 21st century. However, most of these transformations have yet to be completed. According to a poll conducted by Equisoft, 42% of respondents said they were mid-implementation, while 37% said they were in early-stage implementation. Additionally, 21% said they were only in the planning stage of their digital transformation, and none of the respondents had completed the full journey.

This isn’t surprising. For a life insurance company to fully undergo a digital transformation, it could take years, decades even, depending on the number of systems that need to be updated, the expertise of the team, the total resources available, and the complexity of the organization. Even after a portion of a digital transformation is complete, organizations still have a long way to go before they’re fully ready to implement AI. Part of this is because of data.

If you want to leverage AI, you need to make data migration or integration a central pillar of your digital transformation, this includes ensuring that data is properly formatted and up to date. If you’re already 10 steps behind in your ability to access and utilize data, then you won’t be able to take advantage of the next-level opportunities that are right around the corner.

Is my data AI ready?
For life insurance companies looking to implement AI, but aren’t sure if their business is ready, here’s a list of questions to help guide you:

·       What does your data look like? Good data is marked by quality, quantity and diversity. This means that all of your data should be accurate, complete and consistent. Have you, or are you able, to cleanse and transform all legacy data, in its many variations, so that it is usable in modern applications? (This includes data that was originally not machine-readable.)

·       Where is your data located? Data that’s siloed in different departments or systems is much harder to access, manage and aggregate than data located in a centralized location. If you can’t easily reach your data, that means AI systems can’t either.

·       How do you collect data? What’s your policy for data collection? Where do you source it from? If your data sources aren’t abundant and broad, then you may be introducing the possibility of data bias, which could be exacerbated by AI.

·       How do you use data? The most mature data organizations use data across their organization to improve internal operations, underwriting, customer experience and product innovation. If you only consider the use of your data in one-off situations, then it’s likely you won’t be able to extract the full value that AI will unlock. Instead, companies who are able to leverage their data as a true strategic asset have robust data governance practices for all data uses.

How to prepare your organization for AI
If you’ve determined that your organization isn’t ready for AI just yet, some next steps you can take include:

·       Assess your data: This is a great opportunity to look at how your organization stores, structures, collects, and uses its data and determine what can be done to improve it. This may consist of figuring out where your data is siloed and moving it to a more central location or simply ensuring that all of your data is complete and properly formatted.

·       Develop a data and AI strategy: Before jumping into AI, it’s a good idea to determine how it will fit into your organization’s overall business strategy and vision, as well as set guidelines for how it will be used. This could include guardrails to ensure the technology isn’t aggravating or creating data bias.

·       Determine primary business drivers: Technology without an application is a hammer in search of a nail. It’s vital for life insurers to understand the key opportunities and use cases for AI within their organizations, whether it be for marketing, underwriting, claims, customer service or general policy servicing. And while these areas are good starting points, a deeper organizational analysis is required to understand the true company-specific use cases. As always, keeping your customers as the primary focus is the best funnel to effective decision-making.

·       Work with experts: If you don’t have the expertise in-house, consider bringing in a third-party organization that specializes in data and data migration. They can help ensure that while your organization is undergoing a digital transformation, your data isn’t left behind.

Empowering the future
AI is the biggest “business empowerment” of the next decade, and the first technology in history where businesses can control the rate of adoption — it has no limits. Companies who are slow to react or don’t have a strategy will lose the battle to their competition.

While AI can help life insurance companies improve their internal processes, underwriting and products, the technology will only be as good as the quality of the data and the data practices that drive it. If your organization wants to leverage AI, you need to build a solid data foundation first, which will enable you to turn your data into rich sources of customer insight that enhances customer experience and overall operations.