As health insurance penetration rises and customer expectations shift toward faster settlements, traditional manual workflows are becoming increasingly unsustainable. Industry estimates suggest that intelligent document processing and AI-led automation can reduce claims handling time by over 70%, while also improving fraud detection, operational efficiency, and claim accuracy. However, in insurance, speed alone is not enough. Systems must also deliver auditability, governance, consistency, and regulatory readiness at scale.
Against this backdrop, Digit Insurance is building AI systems specifically designed around the realities of Indian health insurance workflows rather than relying on generic automation models. The company is using layered AI pipelines that combine contextual understanding, document intelligence, cloud-native scalability, and real-time validation to process complex claims environments more efficiently.
In this interaction, Vishal Shah, Head – Data Science at Digit Insurance, discusses how the company is approaching AI-led claims processing, handling multilingual and inconsistent medical documents, strengthening fraud prevention, and preparing for a future where agentic AI systems could autonomously manage parts of the insurance claims lifecycle while maintaining governance and trust
Some edited excerpts:
How is your company uniquely approaching AI-led document processing in health claims, and what problem are you solving that others are not?
At Digit, our approach to AI-led document processing is fundamentally use case. Instead of applying generic document automation to insurance, we have built a claims-focused AI system tailored specifically to the realities of Indian health insurance.
Indian medical documents are extremely diverse with handwritten prescriptions, multilingual hospital bills, scanned forms, mobile photographs, and inconsistent formats. Many solutions handle parts of this complexity. However, our focus has been to design an end to end system that can reliably handle the entire spectrum, page by page, at production scale.
What differentiates us is not a single model or tool, but a layered intelligence pipeline that understands document intent, adapts to quality variations, and routes information through specialized processing paths. This allows us to move beyond simple digitization and move towards the true interpretation of medical and insurance context.
The problem we are solving is reliability at scale. In insurance, accuracy, auditability, and consistency matter as much as speed. Our systems are designed to deliver all three together.
Can you break down how your AI models process a typical health claim—from document ingestion to final validation?
At a concept level, the flow is designed around automation with control. A claim enters the system through a secure API and is validated for completeness. Documents are then processed page-wise, normalized for quality, and analysed using a combination of visual and textual intelligence.
Each document is automatically understood both in terms of what it is and how it should be handled. Based on this understanding, it is routed through specialized extraction paths such as bill summaries, detailed bill entries, or complex medical reports.
Throughout the journey, the system continuously checks for quality issues and exceptions. The final structured output is securely stored, fully traceable, and shared back with downstream systems in real time.
From the outside, it looks simple. Under the hood, the system is engineered to ensure that every decision is deterministic, auditable, and consistent with insurance workflows.
What measurable improvements has your company seen in claim processing time after deploying AI?
The impact has been most visible in turnaround time and operational efficiency. Tasks that previously required manual reading, interpretation, and data entry often taking tens of minutes per document are now handled automatically and in parallel. Claims teams no longer need to wait for sequential processing or rework previously seen documents.
Another major multiplier is the system’s ability to recognize documents it has already processed. When the same medical records reappear as they often do in insurance, the system intelligently reuses existing results rather than starting from scratch.
The combined effect is a substantial reduction in processing time, faster claim progression, lower operational cost, and more consistent outcomes. Exact Service Level Agreements (SLAs)—such as document processing time of under 2 seconds—are tracked by operations, while the architectural gains are structural and long term.
India’s medical documents are often handwritten, multilingual, and inconsistent. How has your company trained its models to handle this?
We designed our AI around the assumption that data quality would be imperfect. Our models are trained on insurance specific data and exposed to wide variations in language, handwriting, and formatting. The system is built to tolerate noise, partial information, and mixed scripts rather than expecting clean, templated inputs.
We also place strong emphasis on contextual understanding. For example, recognizing medical terminology even when spelling, structure, or presentation varies widely. Instead of rigid rules, we rely on probabilistic matching and contextual signals that reflect how documents behave in the real world.
This design philosophy building systems that are resilient to real world variability rather than idealized inputs has been central to achieving consistent performance across hospitals, regions, and submission channels.
How is your cloud architecture designed to handle large volumes of claims without compromising speed or accuracy?
Our architecture is built for scale by design. We operate a microservices-driven, cloud-native platform where each major function classification, extraction and validation scales independently. This ensures that spikes in one area do not slow down the entire system.
Workloads are decoupled using asynchronous processing, allowing the platform to absorb large volumes safely while maintaining predictable performance. At the same time, we preserve accuracy by isolating failures and having retries at a granular level rather than reprocessing entire claims.
This balance of speed, resilience, and precision is essential in insurance, where volume fluctuations are common, but service expectations remain high.
Is your company exploring agentic AI systems that can autonomously manage parts of the claims lifecycle?
Yes, we are exploring it, but carefully and responsibly. We already use AI systems that make autonomous decisions within clearly defined boundaries, such as routing documents, determining processing paths, and identifying risk indicators.
Over the next few years, we see strong potential for AI agents to assist in areas like simple claim approvals, fraud triage, document completeness checks, and intelligent escalation. These are tasks where rules are clear, outcomes are measurable, and human oversight can be applied selectively.
However, autonomy in insurance must always be paired with explainability and governance. Our approach is not to replace human judgment, but to amplify it, freeing people to focus on complex and high-impact decisions.
How will your company ensure governance, auditability, and regulatory compliance as AI becomes more autonomous?
Governance is foundational to how we build AI at Digit, and it is not something we add as an afterthought. Every claim, every document, and every system decision is logged with a full audit trail. We retain immutable records of inputs, intermediate outputs, confidence levels, and final decisions. This ensures that any outcome can be traced back to its source.
We also maintain strong separation between different AI components, making it clear which system acted, when, and on what basis. This is critical for regulatory reviews, internal audits, and continuous improvement. As autonomy increases, these controls ensure that transparency is preserved rather than diminished.
What ROI has your company achieved so far from AI-led claims processing, and how has this impacted customer experience?
ROI comes from multiple dimensions working together. Operationally, the reduction in manual data entry and repeated processing translates directly into time and cost savings. Intelligently prioritizing urgent cases improves service levels without additional staffing.
From a customer standpoint, faster turnaround times and clearer status visibility have a meaningful impact on trust. Claims move forward more predictably, and customers experience fewer delays caused by manual bottlenecks.
AI, when applied correctly, improves both efficiency and experience, and that balance is where sustainable ROI lies.
Are you seeing a measurable reduction in fraud or leakage?
We have embedded preventive controls directly into the claims pipeline. By identifying duplicate submissions, inconsistencies, and risk patterns early, the system reduces common sources of leakage before payouts occur. Intelligent risk scoring allows suspicious cases to be flagged for deeper review, while straightforward cases proceed without friction.
This proactive approach shifts fraud management from reactive investigation to early intervention, which is more effective as well as less disruptive to genuine customers.
In a market where many players are adopting AI, what will truly differentiate your approach over the next 2–3 years?
Our differentiation lies in depth and not novelty. We are focused on owning the full intelligence stack behind claims processing, rather than depending on one-size-fits-all platforms. This enables us to continuously embed deep domain learning across medical, insurance, and operational areas into the system. From a medical perspective, as health insurers, we have a strong understanding of diseases, ICD codes, hospital ecosystems (Tier I and Tier II), and treatment cost structures. On the insurance front, our experience gives us clear insight into customer behaviour, market patterns, and risk dynamics.
Operationally, we understand how claims unfold in real time—the urgency and criticality involved, peak load management during high claim frequency periods, and the need to deliver consistent service quality across time zones.
We also take a hybrid approach, combining deterministic systems where reliability is critical with more flexible AI where interpretation matters. Over time, this positions us to expand responsibly into areas like automated adjudication and intelligent claim summarization. Most importantly, our foundation around governance, scale, and auditability ensures that as AI capabilities grow, trust grows with it.