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# Health Underwriting 2026 in India and Why It Bears No Resemblance to 2020

In 2020, a health underwriter in India started the day by opening a paper file or a scanned PDF. She read through the proposal form, turned to the lab reports, and spent 45 minutes manually extracting data points from 12-18 pages of medical documentation. She made calculations by hand. She cross-referenced values from memory. She reached a decision based on what she personally noticed in a sequential read-through. She processed 15-20 cases and went home.

In 2026, the same underwriter starts the day with a queue of pre-analyzed decision briefs. Each brief has already been through 62 parallel checks. Risk signals are extracted and ranked. Anomalies are flagged with severity ratings. Missing documents are identified from the clinical trail. The underwriter's job is no longer to find information. It is to make decisions on information that has already been found. She processes 50 cases with better quality on each one. The profession did not die. It evolved.

Health underwriting 2026 in India is not an incremental improvement over 2020. It is a structural transformation driven by three forces: AI capability, regulatory mandate, and market mathematics.

## What Changed Between 2020 and 2026 That Made the Old Model Obsolete?

Three simultaneous shifts made the 2020 underwriting model obsolete: AI technology matured to production-grade reliability, the IRDAI mandated proactive fraud prevention, and the economics of manual review became unsustainable at current volumes.

### 1. AI Technology Matured

In 2020, AI in insurance was a conference topic. In 2025, the global AI in insurance market exceeded USD 10.36 billion, growing at 32.8% annually. AI-powered underwriting achieved 99.3% accuracy in risk assessment. Document intelligence systems could read handwritten Indian medical prescriptions, parse varied lab report formats, and cross-reference multiple documents in seconds. The technology moved from "interesting pilot" to "production-ready infrastructure."

### 2. Regulatory Mandate Changed

IRDAI's Insurance Fraud Monitoring Framework Guidelines 2025, effective April 2026, explicitly require proactive fraud prevention mechanisms. The regulatory expectation shifted from "investigate fraud after claims" to "prevent fraud at pre-issuance." This is not guidance. It is a framework with compliance implications. An insurer still relying on manual NSTP review faces a [regulatory compliance gap](/blog/irdai-audit-trail) that technology adoption closes.

### 3. Volume Economics Broke the Manual Model

Health insurance premiums in India crossed Rs. 1,17,505 crore in FY 2024-25, with health contributing 41.42% of gross direct non-life premiums. NSTP rates on retail portfolios run at 18-25%. A mid-size health insurer processing 10,000 proposals monthly faces 2,000-2,500 NSTP cases. At 45 minutes each, that requires 1,500-1,875 underwriter-hours monthly just for data extraction. India does not have enough experienced health underwriters to sustain this model at these volumes.

| Dimension | 2020 Model | 2026 Model |
| --- | --- | --- |
| Document analysis | Sequential, manual | Simultaneous, AI-powered |
| Risk checks per case | 8-12 (manual capacity) | 62 (35 risk + 27 anomaly) |
| Processing time | 45-60 minutes | Under 3 minutes (AI) + 8-12 min (UW review) |
| Signal detection rate | 60-75% | 95%+ |
| Daily throughput | 15-25 cases | 40-60 cases |
| Fraud detection | Reactive (post-claims) | Proactive (pre-issuance) |
| Regulatory alignment | Adequate for era | Framework-compliant |

> **Transition to the 2026 Underwriting Model**
>
> [Talk to Our Specialists](https://insurnest.com/solutions/#lets-get-in-touch)
>
> Visit [InsurNest](https://insurnest.com) to learn how Underwriting Risk Intelligence delivers the 2026 health underwriting capability in 5-6 weeks.

## What Does the 2026 Underwriting Operating Model Look Like?

Health underwriting 2026 in India operates on an intelligence-first model where AI handles data extraction, signal detection, and evidence assembly, and the human underwriter handles interpretation, judgment, and decision-making.

### 1. AI Layer: Universal Document Intelligence

Every NSTP case is processed through Underwriting Risk Intelligence with 62 parallel checks. The AI layer is universal: no case skips it. The output is a structured [decision brief](/blog/underwriting-decision-brief) containing risk signals, anomaly alerts, missing document flags, and evidence-backed recommendations. This layer replaces 80% of the work that consumed the 2020 underwriter's day.

### 2. Human Layer: Selective Judgment

The underwriter reviews the decision brief, applies professional judgment, and makes the risk decision. [Selective underwriting](/blog/selective-underwriting-india) means that clean cases receive confirmation review (8-12 minutes) while complex cases receive deep judgment review (25-35 minutes). The underwriter's expertise, the ability to interpret edge cases, weigh competing signals, and apply market context, is now the primary activity, not a 5-minute afterthought at the end of a 45-minute extraction exercise.

### 3. Feedback Layer: Continuous Learning

Claims outcomes are mapped back to underwriting decisions continuously. Signal weights are adjusted based on which patterns correlate most strongly with adverse outcomes. [Actuarial insights](/blog/actuary-underwriter-visibility-india) flow into the AI layer in near-real-time, not through quarterly memos. The system gets smarter with every case and every claim.

### 4. Oversight Layer: Automated Quality Monitoring

The [CUO](/blog/cuo-priorities-india) receives real-time quality dashboards showing signal detection rates, anomaly catch rates, throughput metrics, and decision consistency across the team. The 6-week manual audit costing Rs. 11-14 lakhs is replaced by continuous automated monitoring.

## How Does the Underwriter's Role Change in 2026?

The underwriter's role does not diminish. It elevates. The data extraction clerk who happened to make risk decisions in 2020 becomes a pure risk decision-maker in 2026. The expertise built over years of experience is finally applied full-time to the work it was designed for.

### 1. From Data Extractor to Decision Maker

In 2020, 80% of underwriter time went to data extraction. In 2026, that ratio inverts. 80% of time goes to decision-making, mentoring, and portfolio-level thinking. The [senior underwriter's time](/blog/senior-underwriter-time-india) is protected for high-value activities.

### 2. From Individual Reviewer to Team Leader

With routine extraction automated, senior underwriters have bandwidth to mentor juniors. The [health underwriter career path](/blog/health-underwriter-career-india) accelerates: a junior underwriter learning from structured AI-generated briefs and senior mentorship reaches NSTP competency in 12-18 months instead of 3-5 years.

### 3. From Reactive Processor to Strategic Analyst

Underwriters with freed capacity can contribute to portfolio-level risk analysis, [renewal strategy](/blog/underwriting-roi-india), product design input, and channel quality assessment. The role shifts from operational processing to strategic contribution.

| Role Dimension | 2020 | 2026 |
| --- | --- | --- |
| Primary activity | Data extraction | Risk decision-making |
| Time on extraction | 80% | Under 20% |
| Time on judgment | 20% | 60%+ |
| Mentoring capacity | Minimal | 3-4 hours daily |
| Portfolio contribution | Rare | Regular |
| Career progression | 3-5 years to NSTP competency | 12-18 months (AI-assisted) |

## What Happens to Insurers Who Do Not Make the Shift?

Insurers who do not transition to the 2026 model face compounding disadvantages: regulatory gaps, portfolio quality deterioration, and competitive adverse selection as the market bifurcates between AI-enabled and manual-only operations.

### 1. Regulatory Risk

The IRDAI framework creates compliance obligations. Insurers without proactive [pre-issuance fraud detection](/blog/pre-issuance-fraud-detection) capabilities face regulatory scrutiny. The framework is not a suggestion. It is a requirement effective April 2026.

### 2. Portfolio Quality Deterioration

Manual-only operations continue to miss 25-40% of risk signals. Each missed signal accumulates as unpriced risk in the portfolio. Over 12-24 months, the [loss ratio gap](/blog/health-insurance-loss-ratio-india) between AI-enabled and manual-only portfolios widens by 4-8 percentage points.

### 3. Competitive Adverse Selection

As AI-enabled insurers issue faster decisions with better risk assessment, good risks migrate to these carriers. Applicants with clean profiles prefer faster issuance. Applicants with complex histories, who benefit from slower, less thorough manual review, remain with manual-only carriers. The [adverse selection spiral](/blog/adverse-selection-india) accelerates portfolio quality decline.

### 4. Talent Retention

Young actuaries and underwriters want to work with modern tools. Insurers offering 2020-era manual processes will struggle to attract and retain the talent needed for competitive health underwriting 2026 in India.

> **Do Not Let 2026 Arrive Without Your Team Being Ready**
>
> [Talk to Our Specialists](https://insurnest.com/solutions/#lets-get-in-touch)
>
> Visit [InsurNest](https://insurnest.com) to learn how Underwriting Risk Intelligence prepares your underwriting operation for the 2026 reality.

## What Should Indian Health Insurers Do Right Now?

The transition from 2020-era underwriting to health underwriting 2026 in India is not a multi-year transformation project. It is a 5-6 week deployment that delivers measurable results in the first quarter.

### 1. Deploy Underwriting Risk Intelligence (Weeks 1-6)

Connect the system to your existing DMS. Run parallel validation for 2 weeks. Go live from week 5. Begin receiving [decision briefs](/blog/underwriting-decision-brief) for every NSTP case within 6 weeks of the decision to proceed.

### 2. Measure the Gap (Month 2)

Run a [retroactive review](/blog/retroactive-underwriting-review-india) on last quarter's NSTP approvals. Quantify how many cases had missed signals. This baseline number justifies the investment and identifies systematic improvement priorities.

### 3. Calibrate and Optimize (Month 3+)

Adjust signal weights based on your portfolio's claims patterns. Implement [selective underwriting](/blog/selective-underwriting-india) tracks based on AI risk scores. Begin continuous quality monitoring. The system improves with every case.

The ROI is clear: Rs. 4-6 Cr in annual savings against Rs. 20-35 lakhs investment. The deployment timeline is 5-6 weeks. The first results appear within the first quarter. The question is not whether to modernize. It is how quickly.

## Frequently Asked Questions

**How is health underwriting in India different in 2026 compared to 2020?**
In 2020, underwriting was manual, sequential, declaration-dependent, and processed 15-25 NSTP cases daily. In 2026, AI-powered systems run 62 parallel checks in under 3 minutes, detect 95%+ risk signals, and enable 40-60 cases daily per underwriter.

**What regulatory changes affect health underwriting in India in 2026?**
IRDAI's Insurance Fraud Monitoring Framework Guidelines 2025, effective April 2026, mandate proactive fraud prevention, structured monitoring mechanisms, and real-time data exchange, shifting expectations from reactive to preventive underwriting.

**Will AI replace health underwriters in India by 2026?**
No. AI replaces the data extraction and signal detection work (80% of current underwriter time). The risk judgment, contextual interpretation, and final decision remain with human underwriters whose expertise is now applied to decision-making, not transcription.

**What skills will health underwriters need in 2026?**
Underwriters will need skills in interpreting AI-generated decision briefs, understanding signal patterns, exercising judgment on complex edge cases, and mentoring junior team members using structured AI outputs.

**How does the 2026 underwriting model affect loss ratios?**
Early-adopting insurers report 4-8 percentage point loss ratio improvements within 12-18 months through better signal detection, evidence-based loading, and pre-issuance fraud prevention.

**What is the cost of not modernizing underwriting by 2026?**
Insurers who do not modernize face regulatory non-compliance risk, continued 7-15% fraud losses, competitive disadvantage from slower processing, and adverse selection as better risks flow to faster, more accurate insurers.

**How quickly can an Indian insurer modernize its underwriting?**
Underwriting Risk Intelligence deploys in 5-6 weeks with measurable results in the first quarter, enabling a rapid transition from manual to AI-assisted underwriting without disrupting existing workflows.

**What does the 2026 health underwriting operating model look like?**
The 2026 model features AI-powered document analysis for every case, structured decision briefs for underwriters, intelligence-guided selective review, continuous actuarial feedback integration, and real-time portfolio quality monitoring.

## Sources

- [AI in Insurance Statistics 2026: $10.24B Market Redefining Risk & Claims](https://www.allaboutai.com/resources/ai-statistics/ai-in-insurance/)
- [10 Insurance AI Predictions for 2026](https://www.roots.ai/blog/10-insurance-ai-predictions-2026-forecasting-shift-from-promise-performance)
- [Insurtech Trends 2026: How AI Is Transforming Claims and Underwriting](https://vantagepoint.io/blog/sf/insights/insurtech-trends-2026-ai-claims-underwriting)
- [Playbook to Unlocking the Power of IRDAI's 2025 Insurance Fraud Monitoring Framework](https://ankura.com/insights/playbook-to-unlocking-the-power-of-irdais-2025-insurance-fraud-monitoring-framework)
- [IRDAI Annual Reports](https://irdai.gov.in/annual-reports)
- [AI in Insurance Industry Statistics 2025](https://coinlaw.io/ai-in-insurance-industry-statistics/)
- [Rebuilding Trust: Combating Fraud, Waste, and Abuse in India's Health Insurance Ecosystem - BCG](https://www.bcg.com/publications/2025/india-rebuilding-trust-combating-fraud-waste-and-abuse-in-indias-health-insurance-ecosystem)

Health Underwriting 2026 India: Rs 10,000 Cr Fraud Changes Everything

Underwriting Risk Intelligence processes the full NSTP file in under 3 minutes, so your underwriters start with answers, not questions.

Let AI Read Every Document Before You Do

# How NSTP Automation Transforms Document Reading Into Decision Intelligence

The question most underwriting leaders ask about nstp automation is deceptively simple: can AI actually read a medical file the way an underwriter does? The answer is no, it does not read the way an underwriter does. It reads better in specific, measurable ways, processing 30-80 pages in 90 seconds, running 62 parallel checks that no human can execute simultaneously, and producing a structured output that turns raw documents into decision-ready intelligence.

The global AI in insurance market was valued at USD 10.36 billion in 2025 and is projected to reach USD 154.39 billion by 2034, with the underwriting segment growing at the highest CAGR of 41.6%. In India, where health insurers process thousands of NSTP cases daily across teams of 8-20 underwriters, automation of the document reading phase represents the single largest efficiency opportunity in the underwriting workflow.

## What Does NSTP Automation Actually Automate?

NSTP automation automates the document reading, data extraction, cross-referencing, and anomaly detection phases of underwriting review, reducing the underwriter's role from full-file reader to decision-brief reviewer.

The word "automation" in insurance often triggers concerns about removing human judgment. In nstp automation, the scope is precise: automate the information processing, preserve the human decision.

### 1. Document Intake and Parsing

The system receives the complete NSTP case file, typically 8-15 documents in scanned PDF or image format. It identifies document types (lab report, discharge summary, prescription, proposal form), extracts structured data from each, and creates a unified case profile.

This is the work that takes an underwriter 5-7 minutes of sorting and 15-20 minutes of reading. The AI completes it in under 90 seconds.

### 2. Parallel Check Execution

Once documents are parsed, the system runs 62 checks simultaneously:

| Check Category | Number of Checks | Examples |
| --- | --- | --- |
| Medical Risk Signals | 12 | BMI verification, HbA1c trends, lipid panel analysis |
| Lifestyle Risk Signals | 5 | Tobacco markers, alcohol indicators, occupation risk |
| Hereditary Risk Signals | 3 | Family cardiac history, diabetes lineage |
| Clinical Consistency | 8 | Cross-document value matching, diagnosis-treatment alignment |
| Document Fraud Signals | 15 | Stamp verification, format analysis, reference range validation |
| Identity Integrity | 7 | Name matching, age consistency, blood group verification |
| Missing Document Flags | 7 | Ordered tests not submitted, referrals without follow-up |
| Prescription Continuity | 5 | Drug holiday detection, dosage progression gaps |
| **Total** | **62** | **Parallel execution in under 3 minutes** |

### 3. Decision Brief Generation

The output is not a score or a flag. It is a structured [Underwriting Decision Brief](/blog/underwriting-decision-brief) that presents every finding with evidence citations, source document references, and a pre-filled risk assessment framework.

## How Does AI Read Medical Documents Differently From a Human Underwriter?

AI reads medical documents comparatively rather than sequentially, checking every data point against every other data point across all documents simultaneously, which is physically impossible for a human reviewer processing one page at a time.

### 1. Sequential vs. Comparative Reading

A human underwriter reads the lab report, then the discharge summary, then the prescription history. Each document is processed in sequence. By the time the underwriter reaches the third document, specific values from the first document may no longer be in active working memory.

AI reads all documents simultaneously and compares every data point against every other. A creatinine value in the lab report is instantly compared against the diagnosis in the discharge summary, the medication in the prescription record, and the declaration in the proposal form.

This comparative reading is what enables [clinical inconsistency detection](/blog/clinical-inconsistency-detection) at scale. A blood group listed as O+ on the proposal form and A+ on the lab report, a discrepancy caught in one documented UAE case, requires simultaneous comparison of two documents that a sequential reader might process 15 minutes apart.

### 2. Arithmetic Verification

Human underwriters rarely recalculate derived values. If a lab report states BMI as 24.8, the underwriter accepts that number. The AI recalculates BMI from the raw height and weight values. In one Indian case, this recalculation revealed an actual BMI of 33.4 (Class I Obesity) versus the reported 24.8 (normal weight).

[Medical document fraud detection](/blog/medical-document-fraud-india) at the arithmetic level is tedious for humans but trivial for AI. Every derived value in every document gets recalculated and verified.

### 3. Reference Range Validation

Lab reports contain reference ranges that define "normal" boundaries for each test. In a documented US case, the reference range for creatinine was listed as 0.5-1.8 mg/dL instead of the standard 0.7-1.3 mg/dL. Using the wider range, an abnormal value appeared normal.

The AI maintains a database of accredited [lab report reference ranges](/blog/lab-report-anomalies-india) and flags any deviation. No human underwriter can memorize reference ranges for every test across every laboratory standard.

> **62 Checks, Every Case, Under 3 Minutes**
>
> [Talk to Our Specialists](https://insurnest.com/solutions/#lets-get-in-touch)
>
> Visit [InsurNest](https://insurnest.com) to learn how Underwriting Risk Intelligence helps insurers detect hidden NSTP risk before policy issuance.

## What Happens When NSTP Automation Encounters Fraud Patterns?

When NSTP automation encounters fraud patterns, it flags them with evidence citations across all 27 anomaly checks, including patterns like batch stamp fraud that are invisible in single-file manual review.

### 1. Single-Case Fraud Detection

Within a single NSTP case, the AI detects:

- Stamp inconsistencies (different styles, placements, or registration numbers)
- Format anomalies (lab report templates that do not match the stated laboratory)
- Signature inconsistencies across documents
- Date sequence anomalies ([date sequence checks](/blog/date-sequence-anomalies) where a test result precedes the test order)
- [Impossible lab values](/blog/impossible-lab-values-india) that fall outside biological possibility

### 2. Batch-Level Fraud Detection

The documented Indian case of 22 applications from the same agent, all carrying lab reports from three "doctors" whose registrations could not be verified, illustrates a fraud pattern that single-file review cannot detect. The AI flags patterns across cases submitted from the same source.

[Health insurance fraud ring detection](/blog/health-insurance-fraud-ring-india) requires analyzing agent-level submission patterns, laboratory report formatting consistency, and doctor registration verification across batches, capabilities that [nstp automation](/blog/nstp-automation) delivers inherently.

### 3. Non-Disclosure Detection

The system compares medical evidence in submitted documents against proposal form declarations. If lab reports show statin therapy but the proposal form does not disclose hypercholesterolemia, that is a [non-disclosure at proposal stage](/blog/non-disclosure-at-proposal) that AI flags before the underwriter reviews the brief.

[Silent non-disclosure detection](/blog/silent-non-disclosure) works because the AI reads every document and cross-references every clinical finding against every declaration.

## How Does NSTP Automation Integrate With Existing Underwriting Workflows?

NSTP automation integrates as an upstream layer in the existing workflow, processing cases before they reach the underwriter's queue and delivering a decision brief alongside the original documents without requiring any change to the insurer's core systems.

The [NSTP workflow integration](/blog/nstp-workflow-india) model is designed as a non-disruptive addition, not a system replacement.

### 1. Integration Architecture

The AI system sits between the document intake stage and the underwriter queue. Cases flow in from the existing document management system. The AI processes them and attaches a decision brief. The underwriter sees the brief alongside the original documents in their existing workflow tool.

### 2. No Rip-and-Replace Required

The system does not require replacing the underwriting management platform, the document management system, or the policy administration system. It reads from existing document repositories and writes the decision brief back to the same environment.

### 3. Deployment Timeline

| Phase | Duration | Activity |
| --- | --- | --- |
| Shadow Mode | Weeks 1-4 | AI runs in parallel; outputs compared against manual decisions |
| Co-Pilot Mode | Weeks 5-8 | Underwriters receive briefs; review brief-first, files-second |
| Full Integration | Weeks 9-12 | Brief becomes primary review document; files for reference |
| **Total** | **8-12 weeks** | **Full production** |

> **Deploy Without Disrupting Your Workflow**
>
> [Talk to Our Specialists](https://insurnest.com/solutions/#lets-get-in-touch)
>
> Visit [InsurNest](https://insurnest.com) to learn how Underwriting Risk Intelligence helps insurers detect hidden NSTP risk before policy issuance.

## What Results Does NSTP Automation Deliver in Production?

In production, NSTP automation delivers 80% reduction in review time, 2-3x increase in underwriter throughput, and 25-35 percentage point improvement in fraud signal detection rates.

### 1. Measurable Outcomes

| Metric | Before Automation | After Automation |
| --- | --- | --- |
| Per-case review time | 45-60 min | 8-12 min |
| Cases per underwriter/day | 15-25 | 40-60 |
| Risk signal detection rate | 60-75% | 92-97% |
| Missing document detection | 40-55% | 92-97% |
| Rework rate | 12-18% | 3-5% |
| Annual ROI | Baseline | Rs. 4-6 crore savings |

### 2. The ROI Equation

The investment is Rs. 20-35 lakhs annually. The return comes from three sources: reduced [NSTP leakage cost](/blog/nstp-leakage-cost-india) through better risk detection, improved [underwriter capacity](/blog/underwriter-capacity-india) without hiring, and lower [underwriting rework](/blog/underwriting-rework-india) from AI-consistent analysis.

NSTP automation is not about removing underwriters from the process. It is about removing document reading from the underwriter's job description so they can focus on what their training, experience, and judgment are actually designed for: making risk decisions.

## Frequently Asked Questions

### Can AI really read an entire NSTP medical file?
Yes. AI-powered Underwriting Risk Intelligence processes 30-80 pages of scanned medical documents, including lab reports, discharge summaries, prescriptions, and proposal forms, in under 90 seconds.

### What types of documents does NSTP automation process?
The system processes lab reports, pathology results, radiology findings, discharge summaries, prescription records, specialist referral letters, ECG reports, proposal forms, and identity documents.

### How many checks does the AI run on each NSTP case?
The AI runs 62 parallel checks per case: 35 risk checks covering medical, lifestyle, and hereditary signals, plus 27 anomaly checks covering document fraud and inconsistency signals.

### Does NSTP automation replace the underwriter?
No. NSTP automation functions as a co-pilot that pre-reads and structures the case. The underwriter reviews the AI-generated decision brief and makes the final risk decision.

### How accurate is AI-powered NSTP document reading?
The AI achieves 92-97% accuracy in risk signal detection and anomaly identification, compared to 60-75% in manual review, because it runs all 62 checks on every case without fatigue.

### What happens when the AI encounters a document it cannot read?
The system flags unreadable documents with a confidence score, routes them for manual review, and still processes all other documents in the case to provide a partial brief.

### How does NSTP automation handle different document formats?
The system processes scanned PDFs, digital PDFs, images (JPEG, PNG), and handwritten documents using OCR with format-specific extraction models trained on Indian medical document layouts.

### What is the implementation timeline for NSTP automation?
NSTP automation deploys in 8-12 weeks through three phases: shadow mode validation (weeks 1-4), co-pilot integration (weeks 5-8), and full production (weeks 9-12).

## Sources

- [Fortune Business Insights: AI in Insurance Market Size 2034](https://www.fortunebusinessinsights.com/ai-in-insurance-market-114760)
- [Precedence Research: AI in Insurance Market Size 2035](https://www.precedenceresearch.com/artificial-intelligence-in-insurance-market)
- [Market.us: AI-Powered Insurance Underwriting Market Size](https://market.us/report/ai-powered-insurance-underwriting-market/)
- [Ankura: IRDAI 2025 Insurance Fraud Monitoring Framework](https://ankura.com/insights/playbook-to-unlocking-the-power-of-irdais-2025-insurance-fraud-monitoring-framework)
- [CoinLaw: AI in Insurance Industry Statistics 2025](https://coinlaw.io/ai-in-insurance-industry-statistics/)
- [BizTech Magazine: How AI Is Transforming Insurance Underwriting 2025](https://biztechmagazine.com/article/2025/03/how-artificial-intelligence-transforming-insurance-underwriting-process)

Posts tagged with#NSTP Automation

Health Underwriting 2026 India: Rs 10,000 Cr Fraud Changes Everything

NSTP Automation in India: 62 Checks in 3 Minutes Per Case