The Evolution of AI in Insurance: Unlocking Potential Amidst Challenges

The evolution of artificial intelligence (AI), especially generative AI (Gen AI), is transforming industries at an unprecedented pace.

In the insurance sector, AI is already enhancing customer services and streamlining back-office processes. However, the journey is not without its hurdles.

Despite the immense potential of AI, with an estimated market value of $79bn by 2033, the pace of implementation remains slow. Concerns around trust, accuracy, and security are significant barriers. Yet, the enthusiasm is palpable—nearly three-quarters of insurance CEOs view Gen AI as a crucial investment opportunity.

To harness AI's full potential, insurance firms must develop robust AI strategies, balancing innovation with risk mitigation. This involves collaboration among Chief Technology Officers, Chief Finance Officers, and senior leadership teams to navigate these challenges effectively.

Key findings emerged:

• Insurance organizations are increasingly investing in this space, but projects are taking too long to get into production: Despite the natural risk-adverse approach, insurance businesses are ahead of the global average when it comes to investing in AI use cases across the business. However, the slow pace of implementation is creating significant delays in progress compared to other industries.

• A careful balance of innovation and navigating risks will be crucial: AI offers untapped potential for those that are willing to embrace change, but it also brings new and concerning risks that should be considered as organizations further develop their AI strategy. By undergoing an internal maturity assessment, organizations can have better clarity on current capabilities and identify areas to prioritize. Our tested maturity assessment framework enables organizations to do this effectively.

• Successful organizations will likely still be data-driven and people-led: Before starting on AI transformation, business leaders should have a clear and robust transformation plan in place, and focus on having a solid digital foundation and clean data to improve the output. Upskilling and empowering colleagues and teams to better understand the bridge between AI and data can support longer-term success, and provide additional value by leveraging AI as an assistant.

• Firms are starting to identify potential benefits associated with AI and are introducing initiatives to investigate how this could be better utilized across the business. Many insurers are also looking at Gen AI use cases to drive efficiencies and productivity across finance and IT functions.

• Insurance organizations have made early progress with the adoption of traditional AI and machine learning techniques to develop advanced processes across internal functions and customer-facing services.

• Despite being early adopters of AI in some areas, there is a divide between leaders that are committed to further investment in this space, compared to others that may be more reluctant to spread significant use of AI through the business.

Often times, it’s not the machine learning technologies that limits our client’s ability to predict outcomes, it’s often limitations in the quality of data platforms, master data management and data science that prevents them from gaining the full value of AI. As these factors improve, our clients can unlock new insights to better understand their business and predict the impact of underwriting decisions.

Many insurance firms have already started the introduction of AI solutions for bespoke challenges, such as actuarial or pricing models, and have expertise in data quality. These experiences can provide a foundation for a more comprehensive implementation of AI across the organization.

Confirm you have the right data quality foundations to support a successful implementation. Periodically assess the quality of AI models and potential improvements needed. Implement an AI governance model to help ensure transparency, accuracy, and compliance of algorithms. And look at how to drive a data culture across the organization through data literacy and sharing leading practices around data management.

Further Readings: KPMG Report

Author

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