Insurance — Insurance Policy Pricing Model Optimization

Overview

The purpose of this DAG is to optimize insurance policy pricing models by leveraging historical claims data, market trends, and customer behavior analytics. The architecture consists of a data ingestion pipeline that collects various data sources, including historical claims records, market analysis reports, and customer interaction logs. The processing steps include data cleansing, feature extraction, model training, pricing adjustment, and quality control reviews. The data cleansing step ensur

The purpose of this DAG is to optimize insurance policy pricing models by leveraging historical claims data, market trends, and customer behavior analytics. The architecture consists of a data ingestion pipeline that collects various data sources, including historical claims records, market analysis reports, and customer interaction logs. The processing steps include data cleansing, feature extraction, model training, pricing adjustment, and quality control reviews. The data cleansing step ensures that the input data is accurate and consistent, while feature extraction identifies key variables that influence pricing. The model training phase employs machine learning algorithms to develop predictive pricing models based on the extracted features. Following model training, pricing adjustments are made to align with the latest market trends and customer behaviors. Quality controls are implemented through regular model reviews to ensure ongoing accuracy and reliability. The outputs of this DAG include optimized pricing models, interactive dashboards for visualization, and alerts for significant deviations in pricing trends. Monitoring KPIs such as model accuracy, pricing competitiveness, and customer satisfaction are essential to gauge the effectiveness of the pricing strategy. The business value of this DAG lies in its ability to enhance pricing strategies, improve customer retention, and increase profitability by aligning pricing with market dynamics and customer expectations.

Part of the Fraud & Anomaly Analytics solution for the Insurance industry.

Use cases

• Improves pricing accuracy and competitiveness
• Enhances customer satisfaction through tailored pricing
• Increases profitability by optimizing pricing strategies
• Reduces risk of underpricing or overpricing policies
• Facilitates data-driven decision-making in pricing