Given the breadth and depth of information out there, it can be hard to navigate the implications of AI for insurance companies and understand what to prioritise when adapting your business.
In this blog, we explore what AI insurance use cases exist already, what the risks are in adopting AI technologies and the potential low-hanging fruit for an insurance company to exploit.
Use cases
The use of AI in insurance is not new. Companies are already using AI to model risk, in scenario predictions and data forecasts, in claims handling and contact centre operations. Here are a few examples of how AI is used in the insurance industry today:
- One of the most well-known uses of AI is the chat bot. For personal line insurance companies that must process huge amounts of contact centre queries and information, it’s estimated that bots can successfully take-on about 30% of the workload for around 8% of the cost.
- AI can be used to make insurance more personalised, based on customer habits. For example, pay-per-mile insurance is being enhanced by AI. Early models used basic telematics to track mileage. Today, AI analyses real-time data like a car’s acceleration, braking, and GPS to offer accurate risk assessments and dynamic pricing. This results in fairer premiums and instant feedback for safer driving habits.
- In the management of vast sets of data, AI is already redefining risk assessment to revolutionise insurance pricing strategies.
- Computer vision AI companies help speed up the claims process through a quicker assessment of any damage caused.
- If you’re a broker, AI can help you by analysing client data, market trends and historical policy performance and recommend the most suitable insurers and insurance products more efficiently and accurately. This improves client satisfaction and enhances the broker’s service with faster and more accurate policy recommendations.
- In the insurtech sector, AI is streamlining software development through AI-driven code testing. This accelerates the deployment of new features.
As we look further into the future, increased interconnectedness, especially through the internet of things, will only increase the amount of data that needs to be processed and allow insurance companies to understand their customers at an even deeper level.
Progress in robotics will also have a profound impact on our experiences and interactions with the world around us and a knock-on impact for how we are insured: the use of 3-D printing in building houses will have implications for an insurer’s risk assessment, and how enhanced surgical robots will affect health insurance are just a couple of examples.
AI can enhance existing products and processes like AI powered health insurance analysing how active your lifestyle is, or AI powered motor insurance to analyse driving habits. But what about the damage that can be caused to a customer at the hands of an erroneous AI algorithm or professional indemnity for AI developers? New insurance products to protect both the company developing the AI system and the customers and users of that system are being developed in response to the fast-changing regulatory and technological landscape.
Risks
Whilst the use of AI in insurance and the industry’s continued adoption and reliance on machine learning is inevitable, its use is not risk-free. Considerations and pitfalls include:
- Bias and the need for transparency in insurance AI algorithms.
- Data confidentiality and security.
- Inaccuracy and abuse (model drift and deepfakes).
- Over reliance on the technology and not maintaining the in-house, human skillsets required to monitor, assess and ultimately manage AI systems. Tied into this is the creation of a robust talent management strategy that incorporates the business needs for hiring, upskilling and outsourcing for AI systems. Attracting and retaining skilled professionals in areas like data analytics, AI, and cybersecurity is a persistent challenge. There is intense competition for talent with expertise in emerging technologies so it’s important to hire for tomorrow as well as for the needs of the business today.
- Many large insurers are still running legacy technology like AS400 systems from the 1980s and 90s and integrating new AI processes into legacy systems will always be difficult and costly.
- AI has not got the best reputation given how era defining it’s set to be. As such, it meets with a lot of resistance. AI experts have referred to those who resist business change as “Corporate Antibodies” as they fight against new processes and technologies and create barriers for them to be adopted and optimised.
- As AI moves from invention to innovation to market diffusion (see Schumpeter’s innovation cycle), the pace of change can seem overwhelming, with a lot of experimentation taking place. Unless you are willing to accept a certain amount of failure or a long-term view, companies may struggle to invest appropriately in the AI revolution and gain from the AI race.
- In-house governance of AI systems will be a future consideration for all companies depending on their business goals and needs. AI can touch all parts of a business but requires deep understanding of data science. This has implications not only for the role of the IT specialist but also for all functional roles from underwriting and claims to operations, compliance and finance.
To mitigate these risks and others, business owners, and the industry as a whole, must adopt both a human and data centric approach. Staff must be properly trained, a robust vendor selection process must be adopted, and human-led checks and balances must be established to ensure AI systems are as accurate, fair and transparent as possible. Winston Young of IBM Consulting has said, “Efficient and accurate AI requires fastidious data science”. As we have seen across multiple industries, if your inputs are inaccurate, your outputs will be too – and in the heavily regulated world of insurance, this will carry significant implications for any company adopting these new technologies.
Business readiness
There are many decisions to make and systems to create in preparing your business for the AI insurance revolution. Each business is different, so what you prioritise, and the needs of each business will be unique. However, here are a few starting points when considering your insurance business’ approach to AI adoption.
- Understand the technological landscape
– Start with purpose – what is it that you need to achieve through AI as a business?
– Be careful not to create silos. AI has business-wide implications for your company and should not be restricted to the IT department. - Create a long-term strategic plan
– Consider how AI will affect operations, talent and technology.
– Consider who you want to partner with or acquire.
– The plan should encompass data, people, change management and technology. - Take a deep dive into the data you have and the data you need
– The quality of your internal and external data sources is one of the most important factors in business success.
– Most AI works best when working with a variety of data sources, so a big challenge for most insurers will be determining how to access different data sets in a cost-efficient way. - Make sure you hire the right people for the challenges ahead
– Working with AI requires the right mindset, skills and operational knowledge. McKinsey have described the next generation of frontline insurance workers as being, “technologically adept, creative and willing to work at something that will not be a static process but rather a mix of semi-automated and machine-supported tasks that continually evolve.”
The insurers that win with AI adoption will be those who can identify and exploit the right technologies for their businesses, harness accurate and novel insight from both internal and external data sets, and exceed customer expectations with more accurate pricing, faster analysis and better service. The key element will be facing up to and embracing the AI revolution and better preparing your business for the era-defining changes to come.
At The Siena Partnership, we work with our clients to recruit insurance specialists and help their organisations innovate and deliver business success.If you’d like to continue the conversation with our insurance expert, Tom Goodyear, contact him here: tom@thesienapartnership.com / +44(0)7845 778689
Alternatively, to help identify how AI could transform your business, we have partnered with a leading AI consultancy. To find out more about their AI diagnostic service, please contact Tom Goodyear.