Artificial intelligence continues to revolutionise the insurance profession, bringing unprecedented changes to operational productivity, business models, customer experiences and client engagement. Over the past few months, the remarkable capabilities of ChatGPT and generative AI have captured the attention and imagination of the public worldwide, creating an urgency to act and an imperative for organisations to review their business models. Deloitte report that “65% of UK insurers use AI for risk evaluation, up from 48% in 2023” (Deloitte: Tech trends 2025) and the Geneva Association note that “the implementation of AI in underwriting has led to a 43% improvement in risk assessment accuracy and a 31% reduction in processing time for complex policies” (Geneva Association: Papers on Risk and Insurance).
The use of predictive analytics, automated claims processing, deep learning models and telematics (a method used to monitor and track vehicles) has allowed insurers to reduce processing time, improve fraud detection and claims accuracy, and offer clients more personalised rates.
Below we summarise several groundbreaking developments and tools that are re-shaping the industry.
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Generative AI for customer interaction
Advanced large language models (LLMs) have revolutionised customer service across the insurance market. LLMs are able to sift through vast amounts of unstructured data that is based on policyholder information, regulatory documents and claims reports, and analyse this data without building complex proprietary models. According to a c-suite insurance leader survey conducted by Conning, global investment management firm, 67% of insurers have implemented or are piloting LLMs to improve customer service and many have identified a 35% reduction in response time (Conning: Artificial Intelligence usage in insurance).
The latest generation of conversational AI tools can now handle complex policy questions with greater accuracy, generate personalised policy explanations and provide multilingual translations across international markets.
Some of these tools include:
InsurGPT– a generative AI tool that has been designed to solve unstructured data challenges
Expert AI– a premier artificial intelligence platform for language recognition
Eigen Technologies– an automated data extraction platform for regulated industries
Underwriting and risk assessment tools
AI-driven underwriting platforms have matured significantly, with McKinsey reporting that AI-enhanced underwriting significantly reduces processing time and improves accuracy (McKinsey: Insurance 2030 – the impact of AI on the future of insurance).
Insurers across the board are using these tools for a range of tasks. Key uses include the following:
Computer vision for property assessment
Property insurers are leveraging computer vision AI to assess property conditions from satellite imagery, drone footage, and customer-submitted photos. These tools are able to identify potential risks like roof damage, fire hazards and flood vulnerability without requiring physical inspections, at a much faster rate than manual methods.
Behavioural AI for life and health insurance
Life and health insurers are increasingly using AI to analyse data from wearable wellbeing devices, health records, and lifestyle indicators. In a study conducted by LIMRA, global research and consulting company, insurers using these types of technologies reported a 28% improvement in risk classification accuracy (LIMRA: The AI industry today).
Claims processing automation
Claims automation represents one of the most impactful areas of AI adoption. According to Willis Towers Watson, insurers implementing end-to-end AI claims solutions report faster claims processing, a significant reduction in processing costs and an increase in customer engagement and satisfaction (Willis Towers Watson: AI use cases 2024).
AI for damage assessment
Advanced image recognition tools are able to analyse photos from accidents with expert-level accuracy. These systems integrate with estimating platforms to generate repair estimates in minutes rather than days. Fixzy, an Edinburgh based remote visual assistance company, claims its technology enables insurers to assess and settle claims for any type of property damage or accidents instantly. Using hundreds of thousands of images and available data, the platform uses deep learning algorithms to automate damage detection, generate a comprehensive repair plan and calculate costs instantly.
Fraud detection systems
AI is being increasingly used for fraud detection to help insurers identify potentially fraudulent claims with remarkable precision. Generative AI paired with human management has taken fraud detection to the next level, helping insurers identify and prevent fraud. Tools help insurers to identify unusual patterns, recognise inconsistencies, and detect signs of ‘staged’ accidents or property damage that doesn’t marry up with the reported incident.
Predictive modelling
Modern insurance AI platforms are able to analyse vast datasets to offer predictive capabilities that were previously impossible. AI powered analytics enables insurers to predict customer behaviour, evaluate risk levels and optimise pricing models, enabling leaders to move from reactive decision-making to pre-emptive action.
A range of tools are used for different needs. A main one being; Guidewire predictive analytics – a platform which uses machine-learning algorithms such as simple and deep neural networks, decision trees, GLM/GAM, and text mining to help insurers make data-driven decisions. Insurers using predictive analytics have seen underwriting accuracy improve by up to 15%, leading to reduced claim frequencies and a more efficient underwriting process (Accenture: Why AI in Insurance Claims and Underwriting).
Implementation barriers and regulatory scrutiny
Despite the benefits, insurers are still experiencing a range of challenges for AI adoption. The most commonly cited barriers include integration challenges with legacy systems, data privacy concerns, a lack of internal controls and security, regulatory uncertainty, and talent and knowledge shortages for implementation. A 2025 global digital trust survey conducted by PWC report that “38% of respondents across various industries indicated inadequate internal controls and risk management around GenAI” (PWC: Responsible AI, how insurers can lay strong foundations to start their AI journey).
As AI adoption accelerates, we’re seeing increased scrutiny by regulators to drive ethical and risk adverse strategies. The Association of British Insurers (ABI) and National Association of Insurance Commissioners (NAIC) continue to publish a number of governance frameworks for the responsible adoption of AI, particularly around algorithmic transparency requirements, anti-discrimination protections and data security standards. Future guidance is expected with more detailed regulations, and model laws, which aim to strike a delicate balance between protecting consumers and encouraging innovation.
If you’re looking for specialist insurance professionals, need support with your hiring strategy or are looking for a new career opportunity contact one of our insurance consultants.
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