Artificial intelligence has become ubiquitous. When ChatGPT first launched, attention quickly focused on how far the technology had come. The AI boom ignited our imaginations, and people began to dream of all the possibilities (and worry about potential negative impacts).
AI isn’t anything new for insurance companies. Many have embraced AI and intelligent automation for years. But the heightened focus around generative AI and large language models spurred increased investments across the insurance sector. Organizations saw AI’s potential for streamlining the customer experience, introducing new insurance products, improving risk and policy setting, and offloading back-office repetitive tasks.
So, if 2023 was the year AI captured headlines and demonstrated potential, what can we expect in the next year? What will be the impacts of this exciting new technology? This post will cover the trends we can expect to see around AI in the insurance business over the course of 2024 (and beyond).
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1. Compliance burdens will increase.
Organizations already shoulder a heavy burden from insurance regulators. Beyond the standard industry regulations, AI-specific laws, directives, and guidelines have started cropping up that could add to an already hefty load of compliance tasks. In particular, pay attention to:
The European Union’s Artificial Intelligence Act, which sets strict standards for AI applications. It classifies applications into low- and high-risk and sets strict standards for transparency, data quality, and human oversight.
In the United States, the National Association of Insurance Commissioners (NAIC) published standards for insurers to ensure ethical and fair practices in the use of AI systems.
Other countries such as Australia have already been releasing frameworks around the responsible use of both AI and digital technologies.
Most of these are driven by weaknesses in AI models. First, regulators are concerned about bias. Because AI operates quickly and often semi (or fully) independently from humans, we could unknowingly entrench unchecked human bias into AI systems. This might cause systems to, for example, unduly raise insurance rates for people living in a specific region of a city that heavily overlaps with a protected class. Regulators will seek to reduce biases like these—and it’s critical for your organization to work to reduce your potential risk exposure.
Another problem is runaway AI models. Runaway models can be either overly stringent or loose in their decision-making. We’ve seen this in the United States with stories of rampant health insurance claim denials that have led to multiple lawsuits. To combat this, the AI Act will require insurers to show proof of how their AI system reached a specific decision.
As the news continues focusing on the negatives of AI, expect regulators to continue releasing new guidelines and laws.
2. Data privacy will grow even more complex (and important).
Insurers build their businesses on years of data acquisition, actuarial calculations, and customer data. Keeping this data private is critical for both compliance and maintaining a competitive advantage.
Unfortunately, many AI providers fall short on privacy. For instance, large public cloud providers often use customer data for their own analytics or even for training AI algorithms they provide to other insurers. This means your competition benefits from your years of hard work. Instead, look for AI and automation providers who espouse a private AI philosophy to ensure your data remains within your control.
Related to privacy, insurers must also pay attention to their software vendors’ security postures. Check their trust centers to get a good sense of the practices they employ to keep data safe from malicious actors (or non-malicious data leaks). It’s also worth checking their compliance certifications beyond just their insurance certifications—passing stringent frameworks and regulations like PCI DSS, FEDRAMP, UK Cyber Essentials, or DISA is an excellent indicator of a company that takes security extremely seriously.
[Learn more about protecting your data when using AI with the eBook: Implementing Private AI: A Practical Guide.]
3. Insurers will see tangible results from AI.
In the past year, businesses across industries have experimented with newer forms of AI. This year, expect insurance organizations to hit pay dirt. As pragmatic use cases emerge, more insurers will leverage AI throughout their business operations to increase efficiency, boost profits, and better manage their processes.
Some common use cases we may see include:
- Underwriting: AI can analyze vast data sets rapidly. This can help provide intelligence and insights to analysts and decision-makers that enable them to make more accurate risk assessments and set better policies and pricing during the underwriting process.
- Claims processing: AI tools can be used to make processing incoming claims much faster. AI can hasten the process by generating email responses or enabling claims processors to quickly find answers via chatbot.
- Customer experience: AI can be embedded into applications or websites. Organizations can train their chabots on their own knowledge base, enabling customers to self-serve for questions surrounding policies, pricing, or claims. This can greatly improve customer satisfaction.
- Document management: Insurance organizations face a deluge of incoming documents and emails. AI can be used to automatically route emails, classify documents, and extract critical data from these documents.
4. Insurers will recognize the importance of cross-process automation.
Beyond the use cases mentioned above, in 2024 , insurers will expand their AI usage more broadly across the enterprise, targeting enterprise-wide, critical processes like fraud prevention. But this requires some additional elements beyond just AI. It requires a firm foundation of strong data and process automation.
Data is foundational to any AI effort. Too often, data silos prevent the creation of AI models. Scattered data yields poor results. But you can solve this with a data fabric. Data fabric connects data from across the enterprise in a virtual layer, making data more accessible and preventing a lengthy data migration effort.
Process also plays a critical role. AI is one tool among many. The right AI process automation platform offers multiple automation tools that complement each other. Beyond that, humans still play an integral role. A good platform offers a process orchestration layer that enables you to easily route work between humans and digital workers.
Artificial intelligence, real results.
In 2023, AI received a lot of hype. 2024 will be the year of results. While the insurance industry already invested in AI prior to its ascendance in 2023, we can expect usage to expand. But more importantly, insurers will start to reap the benefits of effective AI deployments and see bottom-line results.
What will drive these results? The combination of AI, data, and process. These three elements together create cascading effects across any enterprise. Insurers with all three in place will outperform their competition by seeing outsized returns on their digital transformations.
With so much opportunity at hand, insurers can’t afford to slow down on innovation. That’s why it’s critical to stay on top of the latest trends and tips. Get the 8 AI Experts on the Future of Enterprise AI for advice from top AI consultants working in the field.
