Life, health and general insurance · examples checked October 2026
Top 10 AI Use Cases in Insurance in India (2026-27)
The shortlist
Ten AI use cases in insurance, at a glance
Every use case below is tied to an insurer, intermediary or vendor that has described the work publicly. We opened each source in October 2026 and quote the figures as the organisation reported them; we have not audited them.
- Health-claim adjudication copilots: reading 20-page claim files for the assessor
- Straight-through cashless approvals: routine requests cleared without manual touch
- Fraud, waste and abuse detection: unusual billing and duplicate claims flagged
- Motor claims from photos: small dents and scratches settled over chat
- Underwriting, pricing and risk inspection: faster quotes and survey reports
- Onboarding and identity checks: video verification and liveness before issuance
- Advisor and agent copilots: product suggestions and answers mid-conversation
- Customer assistants in Indian languages: text and voice help on the website
- Voice bots for renewals and service: persistency calls and routine queries
- Generative AI copilots for staff: drafting, summarising and searching policies
This list is published on Skillopedia’s own site. Skillopedia is a training firm: we sell no insurance software, hold no affiliate links and are not paid by any insurer named here. Nothing on this page is legal, actuarial or regulatory advice.
Comparison table
The ten insurance use cases side by side
| Use case | Line of business | Main team | Data it reads | Customer impact if wrong |
|---|---|---|---|---|
| Health-claim copilot | Health | Claims adjudication | Discharge summaries, bills, lab reports | High |
| Straight-through cashless | Health | Cashless desk, TPA | Pre-authorisation requests, policy terms | High |
| Fraud, waste and abuse | Health, motor | Investigation, SIU | Claim history, hospital billing patterns | Medium to high |
| Motor claims from photos | Motor | Motor claims | Damage photos, policy record | Medium |
| Underwriting and pricing | Group health, commercial, motor | Underwriting, risk engineering | Proposals, inspections, past losses | Medium to high |
| Onboarding verification | Life | New business, operations | Video, ID documents, application data | Medium |
| Advisor copilots | Life | Agency, bancassurance | Product rules, customer segment | Medium |
| Vernacular assistants | All | Digital, customer service | FAQs, product content | Low to medium |
| Renewal voice bots | Life, health | Persistency, service | Policy status, due dates | Low to medium |
| Employee copilots | All | Every function | Emails, documents, internal policies | Low |
“Customer impact if wrong” is our own judgement of how much a policyholder could be harmed by an error. It is not a regulatory classification.
The ten use cases
What each use case does, with a named example
Each entry covers what the AI does, how the workflow runs, a public example, what can go wrong and which people need new skills. In every example a human still makes the call on exceptions.
1. Health-claim adjudication copilots
What it is
A generative AI assistant that reads a reimbursement or cashless claim file and lays out the diagnosis, history, treatment and lab results for the adjudicator, then checks the treatment against standard guidelines.
How it works
Optical character recognition turns scanned discharge summaries, bills and reports into text. A language model extracts the medical facts and compares the treatment with the relevant guidelines and policy terms. The adjudicator sees a structured summary with points to check, then decides to pay, query or reject.
Real example
ICICI Lombard. Microsoft’s customer story says the insurer built a claims copilot on Azure OpenAI, Azure AI Document Intelligence and Azure OCR, because adjudicators had to read 20+ pages per claim. Extracted diagnoses are compared with National Health Authority guidelines. Microsoft reports that time to process a single health claim fell by over 50% and that adjudicator productivity more than doubled.
Risks to manage
Misread handwriting or drug names; summaries that leave out a line the policy excludes; and adjudicators who stop reading the source pages. Health data is sensitive personal data, so access and retention need tight control.
Skills the team needs
Claims adjudicators and medical officers who verify extracted facts against the file; quality teams who sample decisions; and IT teams who own document pipelines and access logs.
- Line of business
- Health (retail and group)
- Team
- Claims adjudication, medical officers
- Data
- Discharge summaries, bills, lab reports
- Typical tools
- OCR, document AI, a language model
Sources: Microsoft AI First Movers: ICICI Lombard, checked October 2026.
2. Straight-through cashless approvals
What it is
Automating the routine part of cashless pre-authorisation and discharge approval, so simple, well-documented requests clear without a person and staff focus on the exceptions.
How it works
Rules and models check the hospital’s request against the policy, sum insured, waiting periods and package rates. Clean, low-value cases are approved automatically within the turnaround the insurer has committed to. Anything unusual, expensive or suspicious is routed to a human.
Real example
Star Health. In an interview reported by Digital Health News, CEO Anand Roy said cashless claims make up about 85% of claim value and roughly 70% by number, and that around 20% of claims are processed straight through using AI with no human involvement. He said the aim is to raise that to over 50% in two years, with people focusing on exceptions, high-value claims and suspected fraud.
A June 2025 PTI report also said Star Health was adopting Medi Assist’s AI-based claims platform.
Risks to manage
Wrong auto-approvals that are hard to recover; wrong auto-rejections that hit patients at the hospital desk; and hospitals learning to game the rules. Every automated rule needs an owner and an audit trail.
Skills the team needs
Cashless-desk staff who handle escalations quickly; product and claims leaders who decide what may be automated; and analysts who monitor approval patterns by hospital.
- Line of business
- Health
- Team
- Cashless desk, TPAs, claims operations
- Data
- Pre-authorisation requests, policy terms, tariffs
- Typical tools
- Rules engines, claim-scoring models
Sources: Digital Health News on Star Health; PTI via The Week (25 Jun 2025), checked October 2026.
3. Fraud, waste and abuse detection
What it is
Models that look across thousands of claims for patterns a single assessor cannot see: inflated bills, duplicate claims, unusual utilisation at one hospital or links between parties.
How it works
Each claim gets a risk score from its own features and from network patterns across providers, agents and policyholders. High scores go to an investigation unit with the reasons listed. Investigators confirm or clear the case, and their findings retrain the model.
Real example
Star Health and Amplify Health. A press release dated 30 June 2026 announced a three-year partnership to deploy an AI platform that identifies patterns linked to fraud, waste and abuse in Star Health’s claims, with real-time anomaly detection. No outcome figures were published.
The rulebook. IRDAI’s Insurance Fraud Monitoring Framework Guidelines, 2025, applicable from 1 April 2026, require a board-approved anti-fraud policy, a fraud monitoring committee and unit, and red-flag indicators.
Risks to manage
Honest customers or hospitals wrongly flagged; scores nobody can explain to a complainant; and bias against regions or provider types. Explainability matters because IRDAI’s working group is looking at claims and fraud specifically.
Skills the team needs
Investigators who interpret scores and document decisions; fraud-control managers who keep red-flag indicators current; and data teams who check for false-positive patterns.
- Line of business
- Health, motor, also life
- Team
- Special investigation unit, fraud control
- Data
- Claim histories, provider billing, network links
- Typical tools
- Anomaly detection, graph analytics
Sources: Star Health and Amplify Health release (30 Jun 2026); Cafemutual on IRDAI fraud rules (14 Oct 2025), checked October 2026.
4. Motor claims from photos
What it is
Computer vision that assesses minor vehicle damage from photos the customer uploads, so small claims can be settled without waiting for a surveyor.
How it works
The customer starts a claim in an app or chat and photographs the damaged panels. The model identifies the part and the type and extent of damage and estimates repair cost. If the claim is small and the policy is valid, an offer is made; anything bigger goes to a surveyor.
Real example
HDFC ERGO. Outlook Money reported in 2024 that HDFC ERGO embedded AI-enabled inspection technology in its WhatsApp chatbot. Customers upload photos of minor damage such as dents and scratches, and if the insurer accepts the claim, settlement of up to Rs 20,000 is paid to the customer’s account.
The same article notes that ICICI Lombard lets customers raise motor claims over WhatsApp.
Risks to manage
Edited or recycled photos; poor lighting that hides damage; and customers who feel pushed into a low offer. A clear route to a human surveyor protects trust.
Skills the team needs
Motor claims handlers who review edge cases; surveyors who calibrate estimates against real repairs; and service staff who can explain the offer to the customer.
- Line of business
- Motor (private cars, two-wheelers)
- Team
- Motor claims, surveyors
- Data
- Damage photos, policy and vehicle records
- Typical tools
- Computer vision damage assessment, chatbots
Sources: Outlook Money (updated 6 Nov 2024), checked October 2026.
5. Underwriting, pricing and risk inspection
What it is
AI that speeds up the work before a policy is priced: reading proposals and past claims, guiding vehicle inspections and drafting risk-engineering reports.
How it works
Underwriters get a summary of the proposal, loss history and inspection findings, plus a suggested rate or flags. For motor, a guided smartphone app captures the vehicle before cover starts. For commercial risks, models draft the survey report that engineers check.
Real example
Tata AIG. A Tata group newsroom feature describes an AI-first approach that includes Inspectify, an AI-guided vehicle inspection app, and a GenAI-driven rating engine for group health that aims to deliver corporate quotes within 24 hours. It says AI has cut risk-engineering inspection report turnaround from five days to two hours, and that 99% of policies and claims are processed digitally.
Risks to manage
Pricing models that reproduce past bias; rates that cannot be explained to a broker or the regulator; and over-trust in automated inspection on unusual vehicles. Underwriting authority must stay with named people.
Skills the team needs
Underwriters who challenge model suggestions; actuaries who validate pricing logic; risk engineers who edit drafted reports; and sales teams who can explain the quote.
- Line of business
- Group health, commercial, motor
- Team
- Underwriting, actuarial, risk engineering
- Data
- Proposals, inspection data, loss history
- Typical tools
- Rating engines, guided inspection apps, report drafting
Sources: Tata.com newsroom: Tata AIG, checked October 2026.
6. Onboarding and identity checks
What it is
AI checks during policy issuance that confirm the buyer is who they say they are, understood what they bought and disclosed what they should, reducing mis-selling and early-claim fraud.
How it works
Before a life policy is issued, the customer completes a recorded video verification. Facial matching and liveness detection confirm identity; the system checks answers and flags inconsistencies for the underwriting or operations team.
Real example
ICICI Prudential Life. Its Partner Stack 2.0 release (11 September 2026) says AI-enabled pre-issuance video verification supports underwriting by strengthening risk and fraud checks, and that database integration pre-fills up to 70% of the application form.
HDFC Life. At its InsurAI 3.0 showcase in September 2026 the insurer listed facial verification and liveness detection among 33 AI-led solutions.
Risks to manage
Deepfake videos; customers with poor connectivity or disabilities who struggle with the process; and storing video and biometric data longer than needed.
Skills the team needs
New-business and operations staff who review flagged cases; compliance teams who set consent and retention rules; and distribution partners who prepare customers for the check.
- Line of business
- Life, also health
- Team
- New business, underwriting operations
- Data
- Video, ID documents, application data
- Typical tools
- Face match, liveness detection, form pre-fill
Sources: ICICI Prudential Life release via UNI (11 Sep 2026); FinTech BizNews on HDFC Life InsurAI 3.0, checked October 2026.
7. Advisor and agent copilots
What it is
Tools that help agents, bank staff and other distribution partners suggest suitable products and answer product questions accurately while they are with a customer.
How it works
The advisor enters the customer’s segment and life stage; a recommender suggests products and builds a quotation. During the conversation, a chatbot answers questions about features, riders and premiums from approved product material.
Real example
ICICI Prudential Life. The same Partner Stack 2.0 release describes the eQuote app, which uses an AI-based product recommender by customer segment and life stage, and LIVA, an AI-enabled chatbot that helps advisors access product features and quotations while talking to customers.
Risks to manage
Recommendations that drift towards higher-commission products; answers that are out of date after a product change; and advisors who skip the suitability conversation. Mis-selling risk does not disappear because software suggested the product.
Skills the team needs
Agents and bank partners trained to treat suggestions as a starting point; sales managers who review suitability; and product teams who keep the knowledge base current.
- Line of business
- Life, health
- Team
- Agency, bancassurance, partner channels
- Data
- Product rules, customer segment, quotes
- Typical tools
- Recommenders, retrieval chatbots
Sources: ICICI Prudential Life release via UNI (11 Sep 2026), checked October 2026.
8. Customer assistants in Indian languages
What it is
Text and voice assistants that explain products and answer questions in English and Indian languages, including outside office hours.
How it works
The customer types or speaks in their language. The assistant translates where needed, retrieves an answer from approved content and replies in the same language, handing over to a person for advice or complaints.
Real example
Policybazaar. A web.dev case study (May 2025) describes Finova AI, a text and voice assistant that answers in English and some Indic languages. In a limited pilot, 73% of users who opened it had quality conversations and the new call-to-action got a 2x higher click-through rate than the previous one.
Risks to manage
Translation errors in policy terms; the assistant drifting into advice it should not give; and customers unable to reach a person. Disclosures must stay accurate in every language.
Skills the team needs
Digital teams who curate the content the assistant may use; service staff who take over escalated chats; and compliance reviewers fluent in the languages offered.
- Line of business
- All lines
- Team
- Digital, customer service
- Data
- Product content, FAQs
- Typical tools
- Language models, translation, speech
Sources: web.dev case study (21 May 2025), checked October 2026.
9. Voice bots for renewals and service
What it is
Generative AI voice agents that call customers about renewals and due premiums, answer routine service questions and pass interested or unhappy customers to a person.
How it works
The bot calls from a list of policies nearing their due date, explains what is due and how to pay, and records the outcome. Complaints, cancellations and advice requests go to a human, and the conversation is summarised for them.
Real example
HDFC Life. FinTech BizNews reported (September 2026) that HDFC Life’s InsurAI 3.0 showcase included GenAI-powered voice bots supporting customer service, cross-sell and persistency, alongside complaint prediction and AI-assisted response drafting.
Risks to manage
Calls that feel pushy or misleading; customers who do not know they are speaking with a bot; and calling outside permitted hours or without consent. Scripts need compliance sign-off.
Skills the team needs
Persistency and service teams who design scripts and review recordings; agents who handle hand-offs; and compliance staff who audit samples.
- Line of business
- Life, health
- Team
- Persistency, customer service
- Data
- Policy status, premium due dates
- Typical tools
- Voice AI, call summarisation
Sources: FinTech BizNews (Sep 2026), checked October 2026.
10. Generative AI copilots for staff
What it is
General-purpose assistants inside email, documents and meetings that help every insurance employee draft, summarise and find information faster, within approved data boundaries.
How it works
Staff use an approved tool, either a vendor copilot or an in-house secure chat, to summarise long documents, draft letters and replies, prepare meeting notes and search internal policies. Confidential customer data stays inside the company’s environment.
Real example
AXA. Insurance Business reported in July 2026 that AXA has begun a large-scale global deployment of Microsoft 365 Copilot, three years after launching Secure GPT, its internal generative AI service, which the group previously said was rolled out to roughly 140,000 employees.
We could not verify a comparable public rollout figure for an Indian insurer, so we use this global example.
Risks to manage
Customer data pasted into unapproved tools; confident drafts with wrong policy details; and licences bought but rarely used. An AI usage policy and training matter more than the licence.
Skills the team needs
Every employee needs AI literacy and the company’s rules; managers need to redesign routine work; IT and risk teams need to set data boundaries and monitor use.
- Line of business
- All lines and functions
- Team
- Everyone
- Data
- Emails, documents, internal policies
- Typical tools
- Microsoft 365 Copilot, ChatGPT Enterprise, Gemini, Claude, in-house chat
Sources: Insurance Business (21 Jul 2026), checked October 2026.
Choosing where to begin
Which insurance use case should come first?
Score each idea on three things.
Who carries the risk if it is wrong? Drafting, summarising and internal search put little at stake for policyholders. Automatic approvals, rejections and pricing affect customers directly and need tighter controls.
How much volume is there? Health claims, cashless requests and service queries arrive in thousands every day, so small time savings add up.
Can you explain the decision? If a complaint reaches the grievance desk or the Insurance Ombudsman, someone must be able to say why the claim was paid, queried or refused.
Our guide to identifying AI use cases includes a scoring template. If you would like a facilitated session with claims, underwriting and distribution heads, get in touch.
Quick wins
- Claim-file summaries for assessors
- Drafted customer letters and replies
- Policy and SOP search for staff
- Call and meeting summaries
Bigger programmes
- Straight-through cashless rules
- Fraud scoring
- Pricing and rating engines
- Photo-based motor settlement
Questions for the business case
- Who signs off each automated decision?
- How will a complaint be answered?
- What is the manual fallback?
An adoption path
From first pilot to business as usual at an insurer
Ownership, clean data and confident staff decide whether a pilot sticks.
Which insurance teams need which AI skills
| Team | What to learn | Where to start |
|---|---|---|
| Board and leadership | Choosing use cases, governance, vendor due diligence | AI for CXOs and business leaders |
| Claims and operations | Reviewing AI summaries, handling exceptions, documenting reasons | AI training for operations teams |
| Underwriting and finance | Checking model suggestions, analysis and reporting with AI | AI training for finance teams |
| Agency and bancassurance sales | Using copilots responsibly, suitability, follow-ups | AI training for sales teams |
| Customer service and grievance | Drafting replies, escalation, tone and accuracy | AI training for customer service teams |
| IT, data and security | Building and governing AI workflows and agents | AI training for IT and engineering teams |
| HR and L&D | Mapping AI skills gaps across branches and functions | AI skills gap analysis |
Where Skillopedia fits
Software changes little until the claims desk changes how it works
Skillopedia is not an insurtech vendor. We are a corporate AI training firm, Skillopedia LLP, founded in Mumbai in 2017, and we have trained teams at 335+ organisations across 13 sectors. In live, in-company sessions, insurance staff practise with the AI tools your company has approved on their own day-to-day work: claim-file notes, customer letters, product comparisons, MIS reports and SOPs.
Each programme follows our FLUENT method, and your AI policy and data rules are built into the exercises. Across surveyed participants (300+), programmes average 4.8 out of 5. For a company-wide foundation, see AI literacy training for employees.
Governance
IRDAI, data protection and fair treatment
IRDAI is building an AI framework. The working group announced on 19 June 2026 was asked to map current AI use and propose principles for ethical, transparent and explainable AI, with claims and fraud named as priorities. Expect questions on audits and explainability.
Fraud controls are formalised. The Insurance Fraud Monitoring Framework Guidelines, 2025 require board-approved anti-fraud policies, a fraud monitoring committee and unit, and red-flag indicators. AI scoring should feed this structure, not sit outside it.
Customer data is personal data. The Digital Personal Data Protection Act, 2023 and the Rules notified in November 2025 apply to policyholder data used in AI. In general terms: a valid basis or consent, purpose limits, security safeguards and breach notification. IRDAI’s information and cyber security guidelines also apply.
Someone must answer for each decision. Customers can complain to the insurer and escalate to the Insurance Ombudsman, so every automated outcome needs a person who can explain it.
Proving value
How to measure an insurance AI use case without fooling yourself
The results quoted above belong to the companies that reported them. Record your own starting point, change one thing, and compare like with like.
| Use case | What to track | Where the number comes from |
|---|---|---|
| Claim summaries and drafts | Handling time per claim, rework rate | Claims system timestamps, QA samples |
| Cashless automation | Turnaround against committed times, wrong decisions reversed | Cashless desk logs, grievance data |
| Fraud scoring | Confirmed fraud per alert, false-positive rate | Investigation outcomes |
| Sales and service bots | Resolution without hand-off, complaints, persistency | Contact-centre and policy data |
| Any use case | Active users after 90 days | Usage logs by team, not licences bought |
For insurance leaders, HR and L&D
Planning AI training for your insurance teams?
Share the teams involved, approximate headcount and the AI tools your company allows. We respond within one working day and then send a written proposal that sets out:
- role-based sessions, from the board to the cashless desk
- hands-on labs on your own claim notes, letters and reports
- a follow-up at day 30 on the workflows your teams adopt
Ask for an insurance training proposal
Reply within one working day. No obligation.
FAQ
AI use cases in insurance: frequently asked questions
What are the top AI use cases in insurance?
Health-claim adjudication copilots, straight-through cashless approvals, fraud detection, motor claims from photos, underwriting and pricing, onboarding verification, advisor copilots, assistants in Indian languages, renewal voice bots and generative AI copilots for staff.
How is AI used in insurance in India?
Public examples include ICICI Lombard’s health-claims copilot, Star Health’s AI-processed cashless claims and fraud platform, HDFC ERGO’s photo-based motor claims on WhatsApp, Tata AIG’s inspection app and rating engine, and ICICI Prudential Life’s advisor tools and video verification.
How does AI help with insurance claims?
It reads claim documents, summarises the medical or damage facts, checks them against policy terms and guidelines, approves simple cases automatically and flags unusual ones for a person to review.
How does AI detect insurance fraud?
Models score each claim for anomalies such as inflated bills, duplicates or unusual patterns at a provider, and investigators review the high-risk cases. Insurers must also follow IRDAI’s fraud monitoring framework.
Will AI replace insurance agents?
The examples here support agents rather than replace them: product recommenders, chatbots for quotes and features, and automated paperwork. Suitability and the customer relationship remain the advisor’s job.
Does IRDAI regulate AI in insurance?
IRDAI formed a working group on AI in June 2026 to propose a framework for ethical, transparent and explainable use, with attention to claims and fraud. Existing rules on fraud, cyber security and fair treatment already apply to AI-driven processes.
How does the DPDP Act affect AI in insurance?
In general terms, the Digital Personal Data Protection Act, 2023 and its 2025 Rules govern how policyholder data is collected, used, secured and retained, including inside AI tools. Your legal team should confirm specifics.
How do you measure ROI from AI in insurance?
Measure a baseline and then the same metric afterwards: handling time per claim, turnaround against committed timelines, confirmed fraud per alert or complaint rates. Do not borrow another insurer’s figures as your target.
What AI skills do insurance employees need?
Everyone needs AI literacy and the company’s usage rules. Claims staff need to verify AI summaries; underwriters need to challenge model suggestions; sales and service teams need to use copilots without mis-selling; leaders need to govern use cases.
Does Skillopedia train insurance teams?
Yes. We run live, in-company AI training for business teams on the tools your company has approved, using their own documents and workflows. We do not build insurance software or give regulatory advice.
Where does Skillopedia run training?
On-site in Indian cities and live online. See corporate AI training in Mumbai, Pune or Delhi NCR, or contact us about other locations.
Next step
Help your insurance teams work well with AI
Tell us which teams, which approved tools and which processes you want to improve. We come back within one working day with a few questions and then a written proposal at a fixed fee.
- Sessions designed for claims, underwriting, sales and service
- Practice on your own files and templates
- Your AI usage and data rules in every exercise
Ask for an insurance training proposal
Reply within one working day.