Can predictive ML replace insurance underwriters?
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.
Is the model explainable for IRDAI audits?
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.
What data do we need to build an underwriting model?
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.
Can it detect claims fraud?
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%).
Where does the data stay?
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.
Which insurance lines have you worked with?
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.
How much does it cost?
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.
