By Samannay Biswas
The insurance industry鈥攐nce a bastion of tradition and manual workflows鈥攊s undergoing a radical transformation, thanks to artificial intelligence (AI). No longer confined to automated underwriting or rudimentary chatbots, AI now powers predictive analytics, fraud detection, dynamic risk modelling, and hyper-personalised policy issuance. As Phani Tangirala, Managing Director & CEO of Expleo Solutions, puts it, “AI-enabled analytics and machine learning are automating repetitive tasks, uncovering hidden risk patterns, and detecting fraud faster and more accurately than a human could across the insurance organisation.” With the global AI in insurance market expected to soar to $39.55 billion by 2034, growing at a CAGR of 33.06 per cent, it’s clear that AI is not just enhancing insurtech鈥攊t is restructuring its very backbone. Precision Underwriting and Risk Management Traditionally, underwriting decisions hinged on limited, often outdated datasets. Today, AI ingests vast amounts of structured and unstructured data鈥攔anging from financial histories to geolocation analytics鈥攅nabling underwriters to make decisions with unmatched speed and accuracy. 鈥淢achine learning algorithms analyse thousands of risk indicators, resulting in more accurate pricing, predictive claims modelling, and dynamic risk evaluation,鈥 notes Mohak Marwah, Senior Manager – Growth Advisory at Aranca. AI has reduced underwriting timelines from weeks to mere minutes, while increasing accuracy by nearly 10 per cent, drastically improving customer onboarding and internal productivity. Reinventing Claims ProcessingAI’s most tangible impact is seen in claims management, where delays, fraud, and paperwork traditionally marred customer experience and insurer margins. AI systems now automate the damage assessment process鈥攅specially in auto insurance鈥攊dentifying vehicle damage, estimating costs, and processing claims without human intervention. Moreover, generative AI combined with retrieval-augmented generation (RAG) frameworks delivers policy-specific responses and actionable guidance to customers in real-time. 鈥淎I-powered virtual assistants now manage routine queries, guide users through claims submissions, and offer personalised product recommendations,鈥 says Marwah. The result: reduced human error, faster resolutions, and elevated customer satisfaction. Personalisation at Scale AI allows insurers to escape the rigid mould of one-size-fits-all policies. By analysing behavioural data, lifestyle choices, and purchase history, AI systems can recommend tailor-made coverage and dynamic premium pricing. This level of personalisation, once unimaginable, is now the standard expectation. “The essential role of AI in insurance is offering personalised experiences. These include identifying user needs, tailoring premiums, and customising coverage terms,鈥 notes a Zopper report. Enhancing Operational Efficiency Manual processing of data, compliance verification, and document handling is notoriously time-consuming. AI鈥檚 robotic process automation (RPA) and intelligent document processing (IDP) free up thousands of man-hours. As Tangirala points out, 鈥淒ocument processing is more efficient, and decisions are made in real-time鈥 lowering operational costs and generating new momentum for the insurance value chain.鈥 Next-Gen Customer Support IVR systems are being replaced with intelligent chatbots and virtual assistants, trained on LLMs (Large Language Models), offering empathetic and informed responses. AI ensures round-the-clock assistance, with the ability to escalate complex issues to human agents when needed. With RAG systems in place, customer queries like “Does my policy cover windscreen replacement?” are answered with personalised, policy-specific responses, not generic redirects. Real-Time Risk Monitoring via IoT Integrating AI with IoT devices is revolutionising live risk assessment. Vehicle telematics, wearable health trackers, and smart home sensors feed real-time data into AI models, allowing insurers to: Predict and prevent risks Assess claims with live data Dynamically adjust policy terms This is particularly impactful in sectors like mobility and health insurance, where behaviour directly correlates with risk exposure. Fraud Detection and ComplianceFraud costs the global insurance industry billions annually. AI systems flag anomalies in real-time, identifying discrepancies across claims history, customer behaviour, and third-party databases. As Marwah highlights: 鈥淎dvanced algorithms detect anomalies and flag suspicious activity in real time鈥 strengthening compliance and minimising human error.鈥 AI compliance tools also keep insurers abreast of evolving regulatory frameworks across regions, enabling seamless global expansion. Barriers to Adoption Despite its advantages, AI adoption in insurance faces challenges. Legacy systems, regulatory complexity, data privacy concerns, and a lack of skilled talent hinder implementation. IBM research reveals: 71 per cent of executives cite high maintenance costs of legacy systems. 52 per cent struggle with data limitations that stifle innovation. Synthetic data generation, trusted tech partners with API integrations, and AI governance frameworks are emerging as key enablers in overcoming these hurdles. A Strategic Imperative According to recent surveys, 77 per cent of C-suite executives in the insurance sector have already approved AI integration into their business value chains. The most significant transformations are occurring in: Sales and underwriting Claims processing Operations and pricing 鈥淎I equips insurers to deliver faster, more accurate, and customer-focused services, while enhancing risk assessment, streamlining operations, and driving profitability across the value chain,鈥 asserts Marwah. AI is not just a tool; it鈥檚 becoming the strategic core of insurance innovation. As demand for personalised, efficient, and transparent services grows, AI is enabling insurers to meet鈥攁nd exceed鈥攖hese expectations. To remain competitive in this data-rich, real-time world, embracing AI isn鈥檛 just an option鈥攊t鈥檚 a business imperative.