What challenges did Edelweiss Tokio Life Insurance face before AI implementation?

Improve cross-sell recommendations in life insurance. Reduce irrelevant or unsolicited offers from rule-based cross-sell. Adapt recommendations to changing customer behavior and product features.

What was the impact of the AI solution?

Cross-sell rate increased by 200% compared with the previous financial year. Achieved 75% conversion from the high-propensity segment. Recall exceeded 80% and precision exceeded 40% after feature engineering and tuning. The first three deciles captured 88% of cross-sell cases, improving prioritization of prospects. SageMaker standardized and accelerated model development, inference, and maintenance workstreams.

Source

ProductionEvidence: Medium50/100

Edelweiss improves cross-sell using machine learning on Amazon SageMaker

Use case typePricing optimizationUpdated Jun 13, 2026

Edelweiss Tokio Life Insurance Company Ltd, a life insurance company in India, built a data-driven cross-sell solution to replace rule-based recommendations that were producing irrelevant offers and missing conversion opportunities. The implementation used Amazon SageMaker to train and tune a CatBoost-based cross-sell propensity model, used a separate SVD-based policy recommendation model, and added frequent pattern mining to validate popular policy bundles. The team processed about 100,000 records with more than 200 attributes, standardized the training and batch inference workflow with SageMaker managed training, automatic model tuning, ECR-hosted custom containers, notebooks, and batch transform, and deployed the model in production for batch scoring.

Organization: Edelweiss Tokio Life Insurance
Industry: Insurance
Location: India
Published: June 2021

Reported outcomes

+200%

cross-sell rate increaseRevenue & growth

+75%conversion from high-propensity segment+80%model recall+40%model precision+88%top-decile cumulative cross-sell coverage

Strategic outcomes

Customer experience & trustReduced irrelevant cross-sell offersNew product / capabilityBuilt machine-learning cross-sell recommendationsNew product / capabilityCreated policy ranking recommendation modelSpeed & agilityStandardized model development workflow

Catalog median for revenue & growth deployments: +40% across 68 reported metrics. Compare benchmarks →

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Cross-sell rate increase: 200% increase

AWS Machine Learning BlogJun 2, 2021Blog postExplicit claimMedium evidence strength

we have been able to increase our cross-sell rate by 200% compared to the previous financial year

Normalized claim

Conversion from high-propensity segment: 75% increase

AWS Machine Learning BlogJun 2, 2021Blog postExplicit claimMedium evidence strength

with 75% conversion from the high propensity segment

Normalized claim

Model recall: 80% increase

AWS Machine Learning BlogJun 2, 2021Blog postExplicit claimMedium evidence strength

we achieved the acceptable performance metrics (recall of over 80% with a precision of over 40%)

Normalized claim

Model precision: 40% increase

AWS Machine Learning BlogJun 2, 2021Blog postExplicit claimMedium evidence strength

recall of over 80% with a precision of over 40%

Normalized claim

Top-decile cumulative cross-sell coverage: 88% increase

AWS Machine Learning BlogJun 2, 2021Blog postExplicit claimMedium evidence strength

By the second and third deciles, 80% and 88% cases are covered, respectively

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Edelweiss Tokio Life Insurance
Provider: AWS
Maturity: Production
Linked source: AWS Machine Learning Blog

The team processed about 100,000 records with more than 200 attributes, standardized the training and batch inference workflow with SageMaker managed training, automatic model tuning, ECR-hosted custom containers, notebooks, and batch transform, and deployed the model in production for batch scoring

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 3

1Cross-sell optimization
2Recommendation system
3Predictive modeling

Improve cross-sell recommendations in life insurance.
Reduce irrelevant or unsolicited offers from rule-based cross-sell.
Adapt recommendations to changing customer behavior and product features.

Built a supervised cross-sell propensity model in Amazon SageMaker using CatBoost.
Built a separate policy recommendation model using SVD to rank the top three policies for each prospect.
Used frequent pattern extraction with Apriori and FP-Growth to identify common policy bundles and validate recommendations.
Standardized model development and inference with SageMaker managed training, automatic model tuning, ECR-hosted custom training containers, notebooks, and batch transform.

Technologies

Amazon SageMaker Amazon Elastic Container Registry CatBoost SVD Apriori FP-Growth Batch Transform Automatic Model Tuning

Cross-sell rate increased by 200% compared with the previous financial year.
Achieved 75% conversion from the high-propensity segment.
Recall exceeded 80% and precision exceeded 40% after feature engineering and tuning.
The first three deciles captured 88% of cross-sell cases, improving prioritization of prospects.
SageMaker standardized and accelerated model development, inference, and maintenance workstreams.

Architecture

Two-pipeline ML architecture: a cross-sell propensity classification model built with CatBoost on Amazon SageMaker for decile ranking and batch inference, plus a separate SVD-based recommendation model for top-policy ranking. The solution also used ECR-hosted custom training containers, SageMaker automatic model tuning, batch transform, and frequent pattern mining (Apriori and FP-Growth) for validation of recommendations.

Sources & evidence1

Evidence: Medium50/100Evidence strength