With Artificial Intelligence (AI) being one of the most talked-about topics today, many members of the InsurTech community are wondering, ‘How is AI used in insurance – and how could it benefit my team in this field?’.
At NashTech, we are fortunate to work with numerous insurance providers and brokers worldwide. Almost all of them are using AI to make buying, owning, and claiming on insurance faster, fairer, and more efficient. Their AI models analyse risk, spot fraudulent claims, enhance customer experience and improve customer retention.
With the pressures the insurance industry is under, many are turning to AI and other emerging technologies in search of smarter, more efficient ways of working. But what does that actually look like? In this article, we outline just some of the ways that AI can be used in the insurance sector. Let’s consider a few examples.
AI and insurance fraud detection
One of the most compelling examples is insurance fraud detection. NashTech’s friends in fraud analytics (Synectics Solutions) are using AI to detect manipulated images and fake documents in claims. AI image forensics detects doctored receipts or stock photos recycled from the internet. Behavioural anomaly models look at digital body language, time-of-day patterns, repeated quote tweaks, or bot-like keystrokes to flag risky submissions. It’s a fascinating twist: fraudsters are using AI to create convincing false claims, while insurers are using AI to spot and stop them. In other words, it’s AI versus AI. The same technology that created the problem is also becoming the solution, and in that tension lies a huge opportunity for the industry.
Improving the customer experience using AI
Modern customer journeys are increasingly using AI-powered quote and buy assistants across the web, mobile apps, and contact centres. These systems interpret user intent expressed in natural language, pre-populate forms with existing data, and present policy details in plain English. In the claims process, guided filing lets customers upload photos or short videos, with AI flagging the likely damage category and policy eligibility in seconds.
Operationally, voice and chat analytics support customer service agents by providing real-time coaching on tone, empathy, and compliance, as well as generating immediate post-call summaries, which help prevent customers from needing to repeat information. Additionally, personalised context-relevant messages displayed throughout the quoting process are customised by AI models to suit each customer’s situation, enhancing conversion rates while avoiding overt sales tactics.
Technologies such as AI chatbots and quote assistants are streamlining data collection and form-filling and delivering swift, indicative quotes. But it doesn’t stop there. Looking ahead over the next 12–24 months, the evolution of AI will drive a transition from traditional chatbot frameworks to fully AI-powered workflows capable of resolving processes from start to finish. Furthermore, AI can guide customers through the claims journey, offering timely updates and tailored communication to ensure an engaging and high-quality experience throughout their interaction.
Increasing customer loyalty with AI
Repeat business from customers is key to any insurer’s growth, so making the renewal process feel rewarding and effortless to the customer is paramount. Using data and AI, insurers can better understand and predict a customer’s propensity to churn and offer the best retention offers with personalised reason codes (i.e. price sensitivity vs cover fit) that drive targeted campaigns or tweaks to a customer's cover to make it more appealing to them. AI can also be used in customer comms to boost engagement rate, prevent losses and ensure that the customer feels truly part of an insurer's brand. For example, AI could be used to predict weather patterns and warn customers to take preventative action, i.e. when there’s a flood warning or a storm incoming, customers are proactively encouraged to move their car or take preventative measures to withstand the impact.
Underwriting and pricing with less paperwork
In commercial insurance lines, AI can be used in the claim submission triage process by extracting key facts from multiple sources, such as broker emails, schedules, and PDFs, and normalising them to rating factors so underwriters can focus on judgment rather than sifting. In personal insurance lines, AI models can draw on telematics like driving behaviours and smart-home sensors to improve risk assessment and reduce severity.
Claims and supplier management—where moments of truth happen
When it comes to claims and supplier management, AI can do the heavy lifting. AI’s computer vision models estimate damage in motor and property claims, suggest repair vs. total loss, and even predict the parts needed to fix the problem. AI-driven vendor optimisation then picks the best repairer or builder based on quality, distance, cost, and risk, then benchmarks outcomes. Document AI can then process invoices, notes, and reports to spot missing information or inaccuracies. Meanwhile, generative AI-powered adjudication assistants support claims handlers by aggregating case data (photos, statements, policy details) and generating clear, explainable rationales for claim acceptance or rejection. It can then also provide customer-friendly explanations to the customer, so they understand what’s happening and what comes next.
How is AI used in insurance? Maybe it’s up to us
AI’s impact continues to grow. NashTech’s combined expertise in technology and experience in the insurance industry make us an ideal partner for any insurer seeking to explore AI opportunities. For more information, please visit our website or read our article on agentic AI in insurance.
.jpg)
