This article is by Barath Narayanan, Global BFSI and Europe Geo Head, Persistent Systems
In today’s fast-evolving insurance landscape, the claims process stands at the crossroads of innovation and customer expectations. In my experience, artificial intelligence is becoming a transformative force, reshaping how claims are managed, from initial reporting to settlement. By integrating advanced AI capabilities like machine learning, natural language processing, and predictive analytics into claims workflows, insurers are not only accelerating claim resolution and improving accuracy but also fundamentally reshaping how they engage with customers. This transformation is not just about efficiency; it is deeply personal for both insurers and policyholders: it’s about redefining trust through transparency, delivering faster outcomes, and making the claims experience more intuitive and responsive.
At Persistent, we believe in helping insurers track their impact in real time. We led the design and deployment of an AI-powered platform for claims self-administration and risk management, transforming how a third-party administrator (TPA) operated across the board. The TPA achieved a 22% cost reduction over three years, complete SLA adherence, and stronger forecasting with embedded analytics. These results aren’t just metrics—they’re a reflection of how strategic technology execution can move the needle on core performance.
From Paperwork to Personalization: The Human-Centric Role of AI
Too often, claims processing has been more about paperwork than people. Forms, back-and-forth emails, missing documentation—it’s a journey few policyholders look forward to.
Currently, Agentic AI and GenAI, are revolutionising claims from scratch, making the process faster, smarter, and more intuitive. They can comprehend context, learn through interactions, and act in real time. From First Notice of Loss (FNOL) to settlement, what once took days or weeks can now happen in minutes.
Across the claims value chain, GenAI and Agentic AI are enhancing operational efficiency through targeted, context-aware automation. Layout-aware NLP and computer vision models extract and validate FNOL data, loss descriptions, invoices, and damage imagery, reducing reliance on manual adjuster inputs. Intelligent claims orchestration engines use policy language, historical severity scores, and jurisdictional rules to route claims by complexity, improving adjuster bandwidth management. Dynamic risk-scoring models leverage geospatial data, behavioural signals, and third-party integrations to refine exposure assessment. In fraud analytics, anomaly detection models flag outliers across claim clusters, provider networks, and billing patterns. Predictive reserving engines use real-time loss development factors to adjust IBNR estimates more accurately.
These AI services are deployed via secure, scalable API layers, enabling straight-through processing across insurer, garage, TPA, and care provider networks.
But perhaps the most critical in claims is empathy.
Agentic AI walks customers through the process, step by step, without overwhelming them. It learns their history, understands their preferences, and adapts to the moment. No more repetitive questions. No more guessing whether your paperwork was received. These systems remember past interactions across channels and keep customers updated without them having to ask. We saw the power of this strategy during a recent partnership with a U.S. insurer. Faced with outdated legacy systems, they wanted to deliver a more responsive, customer-centric claims experience. We facilitated a move to an API-first, cloud-native architecture, infusing AI across their claims journey. We achieved a 20% decrease in operations cost, a 30% improvement in processing efficiency, and a quantifiable increase in customer satisfaction ratings. Customers were provided with proactive updates, had their queries settled in less time, and could file claims through simple, mobile-first experiences requiring minimal effort.
Smarter Claims Ecosystems with Smarter Data
For all the noise around AI, its impact hinges on data quality. Not just how much data, but how clean, consistent, and connected it is. Fragmented systems and inconsistent formats cripple AI’s potential, especially in claims, where speed and precision are non-negotiable. The real shift is in how insurers treat data, not as a passive repository, but as a living, intelligent layer that drives real-time decision-making.
The insurers are shifting from AI pilots as one-offs towards AI-driven data ecosystems. These are designed for ongoing data hygiene, enrichment, and integration. Incomplete claims data gaps are being completed in real time by self-learning models. AI-based data lineage traces and authenticates each step for audits and transparency. APIs integrate internal systems with third-party sources—claims, policy, risk—all into one source of truth.
Where Automation Ends and Human Insight Begins
AI is getting better at reading the room; still, not every decision can or should be made by algorithms. For adjusters, it’s an intelligent copilot, suggesting next steps, identifying anomalies, and surfacing insights. But it doesn’t replace human judgment. Instead, it gives adjusters time to focus on the cases that demand empathy, reasoning, and context, such as disputed claims, ambiguous policies, and emotionally sensitive situations.
As AI learns from those human decisions, it becomes more context-aware. What we’re seeing is not a future of AI-run claims, but AI-assisted, human-led systems that know when to take the wheel and when to hand it over.
Transformation means nothing if you can’t measure it. That’s where KPIs become critical, not just as checkboxes, but as levers for real business value.
Claim cycle time is one of the clearest indicators—AI automation compresses what used to take days into hours. The claim accuracy rate tells you how well the system assesses without human error. Leakage rate reveals how much is being lost to inefficiencies or overpayments, and AI analytics can spot and stop those leak points early. CSAT measures the human impact—are customers feeling the difference? Meanwhile, metrics like average cost per claim and adjuster productivity show whether AI is really moving the needle where it counts.
Claims are no longer back-office operations—they’re becoming the benchmark for how well insurers can modernize at scale. The competitive edge lies in building systems that are cloud-native, data-connected, and architected for adaptability.
It’s about durable infrastructure, clean data flows, and intelligent orchestration across the stack. The insurers who get this right won’t just handle claims faster—they’ll operate smarter, respond sharper, and scale with confidence.
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