Advancing AI across insurance

The evolution of artificial intelligence (AI), including the new wave of generative AI (Gen AI), is transforming numerous industries. Whether it’s automated claims, assessing risk, personalization of products and services, or fighting cybercrime, insurance organizations are increasingly looking towards AI to help tackle complex and time-intensive tasks, and to streamline processes. However, there is an urgent need to pick up the pace in adoption of the technology. Despite the use of AI in a handful of areas and pilots taking place elsewhere, insurance organizations are not gaining an advantage on competitors by using this technology more widely. Doing so could enable businesses to work faster, more flexibly and develop more sophisticated models in response to the evolving market.

So, how can insurers adopt AI effectively and unlock its full potential?

This report is intended to support insurance leadership teams in using AI to transform their organizations. It also brings together insights from KPMG professionals and industry leaders from Generali Italia, PassportCard, Prudential and Zurich Australia who share their perspectives on how to unlock the technology's full potential.

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 are expected to 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. KPMG firms’ 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 help 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.

of insurance organizations see AI as the most important technology for achieving their ambitions over the next over the next three years.¹  

of insurance CEOs interviewed said it would take three to five years for generative AI to provide a return on investment with increased profitability, fraud detection and cyber-attack listed as the top benefits.²   

AI offers huge potential benefits for insurers, with the AI market size estimated to grow to US$79 billion by 2032.³

Leadership teams acknowledge that AI could completely transform their operating models and ultimately, the customer experience. However, insurance organizations appear to be approaching the technology strategically and with cautious optimism.

Many insurers have already started introducing machine learning (ML) or other AI technologies to help improve specific business processes, such actuarial models and fraud prevention processes. In customer service, AI is being leveraged to confirm identity through voice recognition, to provide a timely response to online queries through use of chatbots, and to generate more sophisticated ‘next best actions’ for customer service agents.

With enough training data, algorithms can better analyze risk and predict outcomes, adding accuracy to risk models and pricing structures. Both traditional and Gen AI could empower organizations to enhance actuarial models, deliver personalized insurance cover, or even increase the pace of insurance claims. But the process of doing so appears to be slow, with testing and implementation processes often taking several months to complete.

There is also growing recognition among insurers that a successful AI journey will likely be intrinsically linked to the maturity of their digital transformation. AI thrives on quality data and is best supported by cloud-based infrastructure and agile operating models; firms that are yet to fully embrace this are becoming aware of the urgency to do so.

While the opportunities aligned with AI are significant, the associated risks are also increasing. Eighty-five percent of insurance CEOs believe that generative AI is a double-edge sword, in that it may not only aid in the detection of cyber-attacks but also provide new attack strategies for adversaries4. Organizations should be mindful of this when developing and integrating AI solutions:

• Confidentiality risks
• Generative AI services ‘hallucinating’ or creating inaccurate outputs
•  Data poisoning
• ESG regulatory and reputational risks, given AI’s power demands
• Cultural risks over whether staff are happy to work alongside AI systems

However, avoiding AI altogether may also expose insurers to the risk of missing out on potential opportunities and benefits, and losing competitive advantage. Doing so presents opportunity risks through reducing organizational knowledge, cutting abilities to develop new products and processes, and the risk of being overtaken by more technologically confident peers.

At KPMG, we can help you translate your business strategy into a Digital strategy and an AI strategy. Together, we can prepare your organization to identify, develop, and embed relevant AI use cases. With our AI methodology, compliance and risk approach, expertise, and experience in worldwide realizations, we are happy to support you in unlocking the AI potential for your organization.

Specifically, we can offer:

• AI 360 assessment of internal capabilities and support to integrate AI into your strategy.
• Development of use cases and AI operating model aligned with your business objectives.
• AI risk assessment and development of control and governance frameworks for AI.
• Creation of a data foundation for AI integration, and support with developing models and tuning.
• AI concept development with our global alliance partners to develop a scalable solution.
• Workforce impact assessment and analysis on the impact of transformation on the organization.