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Industry: Insurance

Predictive ML for Indian insurance companies

Underwriting risk scores that ship with SHAP-based explanations. Claims fraud detection that combines supervised learning on your history with unsupervised anomaly catching. Renewal probability models that tell you which policies to intervene on 60 days ahead. All deployed inside your VPC or on-prem, with IRDAI-aware audit trails.

65-80%
Fraud recall
SHAP
Explainable by default
IRDAI
Audit-ready
On-prem
Deployment option
The Insurance ML Problem

Your underwriters see the same 20 patterns every day. A model handles those. Your humans handle everything else.

Most Indian insurers still price policies with rule engines built in 2015. Rules that were tuned once, drift over time, and can't handle the new signals available today (bureau data, transaction patterns, medical history depth, telematics).

A properly-built ML pipeline scores every application in milliseconds, cites the exact features that drove the score, and keeps a full IRDAI-defensible audit log. Your underwriters still make the call on borderline cases. Your straight-through processing handles the 60-70% that are clear-cut, freeing your team for high-value work.

IRDAI-aware from day one

  • SHAP explanations attached to every prediction
  • Full audit log of model version, input features, and score at decision time
  • Bias monitoring across protected attributes (age, gender, region)
  • Version-controlled model registry with sign-off workflow
  • Deployable in AWS Mumbai, Azure India, or on-prem inside your DC
  • Zero external LLM calls for underwriting decisions (only tabular ML)
  • PII masking in training data with re-identification controls
  • Quarterly model performance reviews aligned to IRDAI expectations
ML Patterns We Ship for Insurers

Six models that move business metrics

Underwriting Risk Scoring

Every new application gets a probability score plus SHAP feature attribution. Straight-through processing on high-confidence cases. Human review on borderline. Consistent pricing across every branch and agent.

60-70% STP rate typical

Claims Fraud Detection

Supervised learning on historical flagged claims plus unsupervised anomaly detection for new fraud patterns. Score-plus-flag output goes into your investigator queue. Reduces investigation load on clean claims.

65-80% fraud recall

Renewal Probability

Predicts which policies will lapse 60-90 days ahead. Your retention team gets a ranked list, focuses effort on savable customers. Cuts churn on renewal windows meaningfully.

15-25% renewal lift on interventions

Agent Productivity Modeling

Which agents are likely to hit target, which need coaching, which should be re-onboarded. Uses onboarding-time features plus 90-day activity to predict 12-month producers.

Faster agent activation cycles

NPS & Complaint Prediction

Predicts which policyholders are trending toward a complaint or low NPS 30-60 days ahead. Proactive service outreach avoids escalations to IRDAI and social media.

Reduces IRDAI complaint volume

Claim Document Extraction (Bonus AI layer)

Not strictly predictive ML, but often bundled: OCR plus LLM extraction on claim documents (hospital bills, discharge summaries, FIR PDFs) into structured fields. Speeds up claim intake substantially.

70-80% intake time reduction

Want to see what your data can predict?

15-min call. Tell us the line (life, motor, health, specialty), volume, current pricing/claims workflow, and what decision you'd like to score. We come back with a data audit checklist and prototype scope.

Book Free 15-min Call
Insurance Stack Integrations

Fits into your policy admin and claims systems

Policy Admin

Guidewire · Duck Creek · Sapiens · Custom PAS · IIB systems · Legacy AS/400 (via middleware)

Claims Systems

Guidewire ClaimCenter · Duck Creek Claims · Custom claims workflow · Investigator queues · TPA portals

Data & Bureau

CIBIL · Experian · Equifax · CRIF · IIB · Perfios · Own transactional data warehouses

Deployment

AWS Mumbai · Azure India · On-prem DC · Kubernetes · Docker · FastAPI serving
Process & Pricing

6-10 weeks. Fixed pricing per phase.

Week 1
Discovery

Line of business, decision to score, data availability, IRDAI constraints. Written feasibility doc.

Free
Weeks 2-3
Prototype

Baseline model on your data. Accuracy report, SHAP explanation samples, false-positive analysis. Your underwriting or claims team reviews.

₹2-3 L
Weeks 4-10
Production Build

Feature store, retraining pipeline, IRDAI-aligned audit trail, SHAP explainer, API integration into your PAS or claims system, security review, deployment.

₹8-20 L
Ongoing
Managed Ops

Drift detection, quarterly retraining, IRDAI-audit-log maintenance, bias monitoring, model performance reviews.

From ₹50k/mo
FAQ

Questions insurance leaders ask on every call

No, and we don't recommend trying. Predictive ML augments underwriters by scoring risk consistently across every application, flagging borderline cases, and freeing underwriters to focus judgement calls on the highest-value applications. Underwriters approve, reject, or adjust pricing based on the model score plus their expertise. The model handles volume, the humans handle nuance.

Yes. Every prediction ships with a SHAP-based explanation: which features drove the score, in what direction, by how much. This satisfies IRDAI's expectations around algorithmic decisions and gives your compliance team an audit trail. Black-box neural nets are avoided for anything that touches pricing or claim denial.

12-24 months of historical applications with outcomes (approved / declined / policy issued) plus 24-36 months of claims data linked to those policies. Minimum 10,000-20,000 historical applications for a stable model. If you have less, we start with rule-based scoring and add ML once volume grows.

Yes. Fraud detection combines supervised learning on your historical fraud-flagged claims with unsupervised anomaly detection to catch new patterns. Typical output: fraud probability score per claim plus a flag for the investigator queue. Realistic performance: 65-80% recall on known fraud patterns with an acceptable false positive rate (usually under 5%).

Your choice. Deployable in AWS Mumbai, Azure India, on-prem inside your data centre, or hybrid. For insurers with sensitive PII, we deploy fully self-hosted with no external LLM calls. Model training and inference both stay within your infrastructure. Zero-data-retention agreements are signed when any cloud service is used.

The core ML approach applies across life, general (motor, health, home), and specialty lines. We've built and scoped for underwriting scoring, claims fraud, renewal probability, and agent productivity across all major lines. Life insurance and motor insurance are the two lines where we see the highest ROI on ML today in the Indian market.

Prototype: ₹2-3 lakh (3 weeks). Full production build: ₹8-20 lakh depending on integration complexity (6-10 weeks). Ongoing ops: ₹50,000/month including model monitoring, quarterly retraining, drift detection, and IRDAI-audit-log maintenance. Free feasibility call to scope your specific line and data availability.
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Ready to put your data to work?

Book a 15-min call. Tell us the line, volume, current workflow. We come back with a data-audit checklist plus prototype scope in 48 hours.

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