How Insurers Use Data as a Differentiator

The Insurance Industry Is at an Inflection Point – and AI Is Leading the Charge From outdated systems and unstructured data to rising customer expectations and talent shortages — insurers are under immense pressure. But with Generative AI, there’s finally a real way out. What’s Changing? 1. 60% of operational costs are still manual – AI can slash that. 2. 80% of data is untapped – GenAI reads, learns, and leverages it. 3. Only 18% of insurers currently use AI – but that’s about to change. Key Impact Areas: ✅ Underwriting: 90% data accuracy + new product models. ✅ Claims: 70% of simple claims can be auto-resolved + up to 50% faster processing ✅ Customer Experience: 48% higher NPS, 85% faster resolutions ✅ Fraud Detection: AI flags 75% of fraudulent claims in real time ✅ Sales & Distribution: AI agents, personalized funnels, smarter upsells ✅ Policy Admin: Real-time compliance, automated changes, predictive lapse alerts ✅ New Products: From behavior-based insurance to once “uninsurable” tech like drones & autonomy It’s not just about automating workflows. It’s about rethinking the very DNA of insurance using AI-first foundations. And those who don’t adapt — risk becoming obsolete. Whether you're transforming an incumbent or building the next vertical AI unicorn — the time is now.

The auto insurer of the future will apply extensively AI to a constant flow of #telematics data‼️ Do you agree with our assessment of the impact? 🔴 game changer 🟢 impacted 🔵 marginal impact 🔴Product management “The design and maintenance of a telematics product that provides more frequent interaction with policyholders is completely new compared to the traditional insurance model, which uses static rating features. The days of the “one-policy-fits-all” approach to auto insurance are over.” 🔴 Marketing “As these programs become more innovative, shifting how we market the value proposition to customers will be vital. Marketing activities need to focus on customer engagement through improved communication and transparency.” 🔴Policy acquisition and servicing “Telematics data is changing the entire customer journey from issuing a quote to the policy contract, how the policy is serviced, including billing, and finally, the impact on renewals.” 🔴 Underwriting and risk management “Risk analysis, inspection, monitoring and loss control—typically core and addressed at the policy level in middle and large commercial risks—can be performed at scale on the personal auto book, applying algorithms to the telematics data” 🔴 Sales and distribution “telematics offers new ways to acquire customers, such as using the driving score at point of sale.”“Pre-existing data allows companies to offer the most accurate rating/discount upfront, replacing the need to capture driving data during the introductory period. The insight collected about policyholders and their risks has the potential to unlock further opportunities for upselling and cross-selling.” 🔴 Claims management “Claims activity is ripe for a deep redesign fueled by using telematics-based insights to detect crashes and proactively reach out to policyholders, assessing the crash dynamic and the overall anti-fraud process.” 🔴 Support functions. “From an IT, organizational and data management perspective, the amount of data received with telematics is new for most insurance companies, and the skills required will be broader than the traditional insurance skillset. Investing in the right infrastructure, data foundation and people is vital because nothing happens in telematics without data. The better a carrier is at managing this dataset throughout the customer value chain, the greater their chances of success—as this fuels the pricing models that determine if a discount is warranted, powers the customer experiences, impacts future strategies and innovations, and ultimately unlocks the larger benefits.” #iotinsobs #insurtech

Real-World AI in Insurance—What’s Working, What’s Next AI is no longer experimental in insurance; it’s embedded. From underwriting to claims, CX to finance, we’re seeing tangible impact: ✅ Underwriting: Sub-24-hour quotes in mid-market commercial through intelligent submission intake ✅ Claims: AI-assisted damage assessments, express claims, and GenAI-driven summarization ✅ CX: Virtual agents accelerating resolution and boosting satisfaction ✅ Finance: Agentic dashboards + automated AP—turning operations into insight engines GenAI is also moving beyond the buzz: - Enhancing call containment and multilingual CX - Auto-generating claims correspondence and litigation summaries - Turning unstructured docs into structured data with HITL oversight - LLMs reviewing policies for gaps and compliance risks But challenges remain: Autonomous AI in adjudication and synthetic data for pricing need stronger trust, transparency, and regulatory clarity. What’s enabling responsible scale? Robust AI governance, model classification, bias reviews, explainability, traceability and a growing focus on fairness audits and consent frameworks. 👉 The real differentiator? Making AI embedded in the workflow #InsuranceAI #GenAI #AgenticAI #Underwriting #ClaimsAutomation #CustomerExperience #AIGovernance #SyntheticData #DigitalTransformation #ResponsibleAI #EXL #InsurTech #DataDrivenInsurance #AIatScale #FutureofInsurance Rohit Kapoor Vikas Bhalla Vivek Jetley Anand Logani Vishal Chhibbar Mohit Manchanda Vikas Kapoor Raghav Jaggi Raghav Maheshwari Sarika Gupta Gaurav Iyer Shashank Verma Vikram Machado Vikrant Saraswat Saurabh Mittal Naval Khanna Mustafa Karmalawala Abhay B. Abhimanyu Bhola Puneet Mehra Navneet Anand Deeksha Sethi Malay Davé Yogesh Sarkhot Akash Bhatnagar Makarand Karmarkar