AI’s Growing Role in Insurance: Opportunities and Obstacles
The insurance industry is rapidly embracing artificial intelligence (AI), yet significant hurdles remain, according to a recent study conducted by global professional services firm KPMG. The report indicates that while insurers have high expectations for AI, they face pressure from shareholders to demonstrate a quick return on investment (ROI).
High Hopes, High Expectations
The KPMG study revealed that a majority of respondents (66%) anticipate a moderate to very high ROI from their AI investments. Furthermore, the survey showed unanimous agreement among participants that companies adopting AI will gain a competitive edge in the marketplace. This optimism is reflected in increased spending, with all respondents planning to allocate a larger portion of their budgets to AI initiatives. Of these, 66% expect to spend up to 20% of their global budgets on AI, while 34% are prepared to invest even more, allocating over 20% of their finances to AI.
Trust and Reliability Concerns
Despite the enthusiasm surrounding AI, trust remains a significant concern. The study found that 46% of insurance leaders express reservations about the reliability of AI, and a mere 25% fully trust AI within their companies. Recognizing the vital need for safeguards, 82% of respondents acknowledged the importance of robust frameworks, policies, and processes to ensure both regulatory compliance and responsible AI implementation.
AI’s Impact on Insurance Processes
The study identified several areas where AI is currently impacting insurance processes:
- Underwriting: AI is automating risk segmentation and using predictive analytics to refine policy pricing and assessment. It also incorporates electronic health records.
- Life Insurance: Life insurers are integrating wearable data and wellness tracking to personalize policy pricing and encourage healthy behaviors.
- Claims Automation: AI is improving claims processes by detecting fraud, analyzing documents like death certificates, and streamlining payouts using machine learning.
- Longevity Modeling and Compliance: AI helps assess life expectancy, detect disease onset, and optimize risk stratification, ultimately allowing insurers to refine pricing structures and payout models.
KPMG’s Three Phases of AI Value
To guide insurers through the implementation process, KPMG has outlined a three-phase model for maximizing AI value:
- Enable: This initial phase focuses on laying the groundwork for AI adoption by establishing responsible leadership, creating a comprehensive AI strategy, identifying high-value use cases, increasing AI literacy among staff, and ensuring regulatory compliance. This phase also includes launching pilot projects, leveraging cloud platforms, and utilizing pre-trained models.
- Embed: In the second phase, AI is integrated into existing business workflows. This stage involves a senior leader driving enterprise-wide workforce redesign, including re-skilling and change initiatives, while embedding AI into the company’s operating models, with the need for ethics, trust, and security concerns at the forefront.
- Evolve: The final phase centers on transforming business models and ecosystems by incorporating AI alongside emerging technologies such as quantum computing and blockchain. The overall goal is to address large-scale challenges, increase enterprise value, and prioritize ethics, trust, and security. Workforce training is emphasized to promote ongoing innovation.
Focus on Efficiency
Current leadership goals for AI adoption are focused on operational efficiency as the primary aim, rather than broader, strategic value. However, as the technology matures, and the issues of trust and regulatory compliance are addressed, the strategic benefits of AI are expected to become more prominent.
