AI in insurance is revolutionising the way work is done across the industry – and the gap between early adopters and those still reliant on legacy systems is widening fast.
Chances are your organisation already relies on AI to some degree, whether it’s automating underwriting processes, enhancing risk placement accuracy or freeing up time and internal resources previously spent on repetitive manual tasks.
The range of potential use cases, which we’ll be unpacking below, can be overwhelming at first, though there’s no doubt that AI technology offers significant benefits for those willing to embrace change.
A recent study of 50 business leaders in the European insurance market shows that more than half of organisations expect AI tools to lead to productivity gains of 10-20%, along with notable gains in premium growth and improvement in technical results.
Let’s take a closer look at how artificial intelligence is impacting the insurance industry.
Where is AI being used by insurance firms?
Underwriting processes
Most insurers are sitting on a wealth of data around customer behaviour, historical claims and medical records, as well as external factors like economic trends and weather conditions. This data can fuel predictive AI models to deliver more accurate risk assessments and pricing estimations, in turn speeding up policy issuance.
Claims handling and fraud detection
A survey led by KPMG revealed that fraud detection (76%) and risk management (68%) are now the most common areas of focus for AI investment in insurance, helping firms analyse patterns and anomalies in historical data to flag up anything suspicious. AI also streamlines claims processing by automating document verification, damage assessment and payout calculations, all of which reduces processing times.
Back-office workflows and client services
Virtual assistant tools like Copilot automate repetitive admin tasks such as data entry, customer onboarding, client invoicing and report generation, reducing manual errors and freeing up time to focus on higher value activities. Virtual assistants and chatbots can be used to handle customer queries and enhance their experience through intuitive personalisation.
Compliance
Insurance companies face a constant challenge to stay on top of evolving industry regulations and regulatory monitoring requirements. Specialist tools can be used to analyse vast datasets to flag risks, streamline reporting and reduce manual errors, while also bolstering anti-money laundering and fraud detection practices.
Forecasting and strategic planning
AI leverages historical and real-time data to predict future trends in claim volumes, market shifts and customer demand. Advanced algorithms analyse complex datasets quickly to provide actionable insights for strategic planning. This helps insurers anticipate challenges ahead of time and improve decision-making for long-term growth.
Understanding the risks of AI deployment
Every new technology comes with risks and challenges, and AI is no exception.
Insurance firms must be mindful of cybersecurity best practices at every stage of deployment, particularly around data protection given the vast amounts of sensitive information that they are responsible for collecting and storing. Poor handling of data or reliance on unprotected systems can lead to breaches of FCA and PRA guidelines, as well as regulatory fines for violating privacy standards.
Comprehensive assessment and scenario planning should be conducted to ensure a clear and quick response in the event of a system failure or downtime. What happens if an AI system underpinning critical operations fails? This should be dealt with proactively when integrating new systems into your core technical infrastructure.
There are also ethical challenges to consider. For instance, some AI models trained on historical data may have a tendency to bias, leading to unfair outcomes for certain demographic groups – based on characteristics like age, gender, ethnicity or socioeconomic status. Again, it’s important to test and resolve this before it can lead to discriminatory underwriting or claims handling.
Key tactics for AI success
While the exact nature of projects differs from firm to firm, successful insurance AI projects generally follow the same principles:
- Investment in data quality – AI’s effectiveness depends on robust, clean and relevant data. Many in the Lloyd’s market struggle with legacy systems and data silos. Consolidating data into accessible formats ensures AI tools have a strong foundation for analysis and insights. For more info on this, make sure to read our blog on data management.
- Start small and scale – It’s a good idea to temper expectations by conducting pilot projects targeting specific problems like claims processing, fraud prevention or underwriting. By proving value in contained scenarios, it’s much easier to justify scaling AI efforts across other processes and departments.
- Collaborate with the right partners – Building or integrating AI tools to match your specific needs requires significant time and resources. If you decide to outsource all or part of the responsibility, make sure you partner with technology providers or consultants who understand the nuances of the insurance market and have a proven track record in minimising implementation times and costs.
- Educate your people – Misunderstanding around AI in insurance is rife. It’s down to IT to equip teams with an understanding of AI’s capabilities and limitations. This helps align expectations, therefore reducing the risk of overhyped initiatives that fail to deliver on their promises.
The expert view
Steve Coldwell, Head of IT at Apollo
“It starts by being realistic about what AI can and cannot do. It’s not some silver bullet that will solve all our problems. Right now, AI tools can help accelerate decision-making and provide valuable data-driven insights. But it’s certainly not going to fully automate everything or replace human effort. We need to be clear-eyed about the productivity gains and efficiency improvements the tech can realistically deliver.
“My focus is on identifying specific use cases where AI can have a tangible, measurable impact: things like automating repetitive processes, providing decision support and generating insights from our data. But we have to be careful not to overhype AI’s capabilities. It’s about finding the right balance and applying it carefully to complement and elevate our human expertise. That’s still by far the most important factor in our growth.”
Navigating your own transition to AI
AI is already transforming the way work is done across the insurance industry, yet the shift to new technologies requires careful management to limit concerns and ensure a smooth transition. Clear communication, continuous learning and a culture of innovation are essential to overcoming resistance and maximising the potential of new investments.
As AI reshapes roles and processes, IT leaders should always be looking to align digital tools with organisational goals so that they deliver measurable value. Ongoing training, performance evaluation and iterative improvements are all crucial and, by doing so, insurance firms can drive productivity, reduce costs and unlock new revenue opportunities.
