Advancing AI across insurance

Advancing AI across insurance

Artificial intelligence is becoming a key priority as insurance organisations navigate complexity in a fast-paced world

It's time for insurers to take action

How can insurers adopt AI effectively and unlock its full potential?

Key findings

1. Insurance organisations 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 combined global average across all industries 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.
2. 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 organisations further develop their AI strategy. By undergoing an internal maturity assessment, organisations can have better clarity on current capabilities and identify areas to prioritise. KPMG firms’ tested maturity assessment framework enables organisations to do this effectively.
   
3. Successful organisations 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.

AI offers huge potential benefits for insurers, with an AI market size estimated to grow to US$79 billion by 2032^[1].

Leadership teams acknowledge that AI could completely transform their operating models and ultimately, the customer experience.

Many insurers have already started introducing machine learning (ML) or other AI technologies to help improve specific business processes, such as 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 analyse risk and predict outcomes, adding accuracy to risk models and pricing structures. Both traditional and Gen AI could empower organisations to enhance actuarial models, deliver personalised 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 adversaries^[2]. Organisations 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 organisational knowledge, cutting abilities to develop new products and processes, and the risk of being overtaken by more technologically confident peers.

Our solutions

• AI 360 assessment of internal capabilities and support to integrate AI into your strategy.
• Development of use cases, AI operating model and a robust prioritisation framework for value and ROI aligned with your business objectives.
• AI risk assessment and development of control and governance frameworks for AI.
• Creation of infrastructure, technology, security and data foundations 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, development of adoption strategies and workforce upskilling/training plan and analysis of the impact of transformation on the organisation.