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Insurance — Feature Engineering Pipeline for Pricing Optimization

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This DAG constructs feature pipelines from historical data to enhance pricing models. It ensures data quality and optimizes model performance through systematic processing.

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Overview

The primary purpose of this DAG is to create robust feature pipelines that feed into pricing optimization models within the insurance industry. It ingests historical data from various sources, including policyholder information, claims data, and market trends. The ingestion pipeline processes these data sources, ensuring that they are clean and ready for feature extraction. The processing steps involve data preprocessing, where missing values are handled and categorical variables are encoded. Fe

The primary purpose of this DAG is to create robust feature pipelines that feed into pricing optimization models within the insurance industry. It ingests historical data from various sources, including policyholder information, claims data, and market trends. The ingestion pipeline processes these data sources, ensuring that they are clean and ready for feature extraction. The processing steps involve data preprocessing, where missing values are handled and categorical variables are encoded. Feature selection is then performed to identify the most relevant variables that influence pricing decisions. Quality controls are implemented throughout the pipeline to maintain data consistency and reliability, with checks on data integrity and completeness. The final outputs of this DAG include a set of engineered features ready for model training, as well as quality reports that summarize the data preparation process. Key performance indicators (KPIs) monitored include data preparation time and model performance metrics, such as accuracy and precision. By optimizing the feature engineering process, this DAG adds significant business value by enabling more accurate pricing strategies, ultimately leading to improved profitability and competitive advantage in the insurance market.

Part of the Pricing Optimization solution for the Insurance industry.

Use cases

  • Enhanced pricing accuracy leading to increased customer satisfaction
  • Improved competitive positioning through data-driven insights
  • Reduced time-to-market for new insurance products
  • Increased operational efficiency in data processing workflows
  • Higher profitability through optimized pricing strategies

Technical Specifications

Inputs

  • • Historical policyholder data
  • • Claims records and outcomes
  • • Market trend analysis reports
  • • Customer demographic information
  • • External economic indicators

Outputs

  • • Engineered feature set for model training
  • • Data quality assessment reports
  • • Feature importance rankings
  • • Prepared datasets for machine learning
  • • Performance metrics for pricing models

Processing Steps

  1. 1. Ingest historical data from multiple sources
  2. 2. Preprocess data to handle missing values
  3. 3. Encode categorical variables for analysis
  4. 4. Select relevant features impacting pricing
  5. 5. Conduct quality control checks on data
  6. 6. Generate feature sets and quality reports
  7. 7. Output prepared datasets for model training

Additional Information

DAG ID

WK-1125

Last Updated

2025-05-18

Downloads

15

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