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Development of Advanced ML Model for Insurance Application Conversion Prediction

itransition.com

Insurance

Construction

Hospitality

Property

Entertainment

Freight Transportation

Challenges with Legacy ML Model

Existing ML model lacked scalability and accuracy in predicting user conversion likelihood, failed to identify users with positive purchasing intent, and was overly dependent on historical client data, limiting effectiveness with new clients and dynamic market conditions

About the Client

Award-winning insurance MGA providing 40+ specialty insurance programs and leveraging AI underwriting solutions for improved distribution speed and profitability

Key Project Goals

Develop a more accurate predictive model for insurance application conversion
Implement sophisticated feature engineering to capture dynamic market conditions
Create a scalable solution adaptable to new clients
Improve ROI through targeted pricing policies based on prediction accuracy
Establish model drift monitoring for sustained performance

Core System Requirements

Exploratory Data Analysis (EDA) framework
Automated feature engineering pipeline with 100+ features
CatBoost-based ML model training and optimization
Microservice architecture deployment
Real-time model drift monitoring dashboard
Integration with existing insurance platform infrastructure

Technology Stack

CatBoost

Streamlit

Flask

NumPy

PandasAI

Matplotlib/Seaborn

Population Stability Index (PSI)

System Integrations

Insurance platform infrastructure
Client data systems
SaaS provider platforms

Non-Functional Requirements

Scalable architecture for new client onboarding
Real-time prediction processing
Model explainability and auditability
Data security compliance
High availability monitoring

Expected Business Impact

Anticipated 25% improvement in prediction accuracy (from 60% to 75%) enabling targeted pricing strategies, increased conversion rates, and enhanced customer loyalty through personalized insurance offerings, with ongoing model optimization capabilities