- Insurance & Financial Services
- Service Pillar: Data & AI → Data & Analytics
Executive Summary
A global insurer struggled with inconsistent policy data and lengthy actuarial runs, resulting in delayed insight generation for product pricing and retention strategies. Mindmap Technologies implemented a unified data foundation with a modern analytics pipeline to deliver predictive insights for lapse risk and dynamic pricing decisions.
Problem Overview
- Inconsistent policy and lapse datasets across multiple geographies
- Slow actuarial model execution and manual data preparation steps
- Limited predictive insight into customer churn and lapse probability
- Difficulty in visualizing pricing sensitivity and profitability impact
Our Approach
- Consolidated policy and claims data into a centralized feature store
- Implemented MLOps pipelines for model versioning and automated retraining
- Developed predictive models for lapse probability and pricing elasticity
- Deployed dashboards for actuaries and underwriters with explainable model outputs
Technology Used
Python, Azure Machine Learning, Databricks, Power BI, MLflow, Delta Lake
Key Outcomes
01
Actuarial runs completed 4x faster with automated data preparation
02
Retention uplift of 2.1% through proactive policyholder interventions
03
Enhanced decision-making through explainable AI outputs for pricing and lapse models
04
Data lineage and compliance auditability achieved across all regional datasets