How insurers can leverage AI to drive innovation within the policy lifecycle

By Rajeev Gupta

In today's technologically driven world, the strategic use of artificial intelligence and generative AI is revolutionizing industries. One area poised for significant transformation by the implementation of AI and GenAI, and the synergies between them, is the insurance industry. Cyber insurance is already the fastest-growing line of insurance, almost tripling in size over the past five years, and AI can drive efficiency and innovation within the market.

AI and GenAI have the potential to dramatically improve underwriting and claims processing, enhance risk management and drive organizational resilience in the face of evolving cyber threats. Understanding the current and prospective role of AI within the industry can enable insurance providers to capitalize on its transformative potential. As the cofounder of a cyber insurance provider that has leveraged AI since its founding, I’m eager to see the benefits of AI and GenAI applied across different areas of the policy lifecycle, and on a broader scale.

Improving each area of the policy lifecycle with AI and GenAI

Advanced technologies such as AI and GenAI will enhance the entire policy lifecycle and positively affect all pillars of insurance – distribution, underwriting and claims. These tech tools will help organizations lower their risk profile, stay ahead in today’s threat landscape, and help insurance companies assess and manage risk more accurately. AI’s ability to analyze vast and diverse datasets is unparalleled. Insurance, which is a data-driven industry, can benefit from AI because it has the power to analyze different types of data in a variety of formats, and find common threads or potential connections.

Claims – AI empowers insurers to respond promptly to incidents and mitigate losses more effectively. There is a vast amount of data in front of the claims team to make sense of what happened, why it happened, etc. With GenAI, the adjudicator is able to look at it from 360 degrees: each and every piece of data is reviewed.

Underwriting – GenAI and machine learning can transform risk signals into easily understandable and actionable insights, ultimately streamlining and improving the accuracy of the underwriting process. Leveraging GenAI helps underwriters do a better job faster by speeding up the contract review process, ensuring nothing is missed and ultimately improving decision-making. With more efficient quote generation and personalized premiums, the user experience is better for agents and ultimately policyholders.

Cyber risk assessment – Cyber attacks are a prominent threat to organizations and enterprises of all sizes and industries. AI-powered solutions can help evaluate an organization's cybersecurity posture, providing ongoing insights for informed decision-making and ensuring they can defend against cyberattacks and mitigate risks in an evolving threat landscape.

Client portfolio management – GenAI can play a critical role in client portfolio aggregation management. For example, insurers underwriting continuously may observe that a significant number of their customers use a particular SaaS provider. If that service provider gets attacked, the insurer is going to have an overwhelming amount of claims. With the help of AI, insurers can identify this accumulation or aggregation of certain risk items in real time.

Threat modeling – By leveraging AI, insurers can enhance risk assessment processes by gaining a deeper understanding of risks and vulnerabilities. This includes advanced threat modeling capabilities, which outline potential attack methods and simulate protection tactics. By leveraging large language models, insurance firms can accurately gauge risk ratings and identify vulnerabilities. This ultimately minimizes human error and speeding up risk assessment and threat modeling processes.

User experience – AI assistants can help improve the user experience by providing customers with quick answers to their questions about their cyber insurance policies and risk assessments, and support policyholder education. This helps both agents and policyholders understand nuances around risk, underwriting, basic policy language and more.

Addressing concerns of AI within insurance

Despite the benefits of incorporating AI and GenAI into current insurance processes, many remain apprehensive about the advanced technology. Hesitations around AI within insurance revolve around issues such as data privacy, bias and the black box nature of the AI decision making.

AI systems in insurance rely on large amounts of data to make predictions and decisions. There are concerns about how this data is collected, stored and used. Companies have more data that must be protected from any kind of breach. Compliance and privacy regulations are enforced to protect data, but there is always a risk of data breaches or misuse.

AI algorithms can “develop” bias because of incomplete or faulty datasets used for training. Many AI models, especially complex ones such as deep learning neural networks, are often considered "black boxes" because their decision-making processes are not easily understandable by humans. This lack of transparency makes it difficult to explain AI-driven decisions to regulators, customers or other stakeholders, leading to concerns about accountability and trust in AI systems.

Looking toward the future

AI usage is revolutionizing industries from insurance and health care to finance and manufacturing, and we’ve just begun to scratch the surface of its potential. Within insurance, there are endless opportunities for AI to streamline and improve processes. Since AI can automate responsibilities within underwriting, risk assessment and portfolio management, insurers will have more time for higher-value activities, such as strategic decision-making and innovation.

Insurance organizations that are not only open to this advanced technology but also offer upskilling programs for employees to learn AI best practices, will experience increased efficiency, continuously stay ahead of their competitors and improve the experience of their customers.

Rajeev Gupta is cofounder and chief product officer of Cowbell. Contact him at [email protected].