Banking — Feature Engineering for Scoring Model Enhancement

Overview

The purpose of this DAG is to automate the feature engineering process, leveraging historical data to enhance scoring models within the banking sector. The architecture consists of a robust data pipeline that ingests various data sources, including transaction records, customer profiles, and credit history logs. The ingestion pipeline begins with data extraction from these sources, followed by a series of processing steps that include normalization to standardize data formats, transformation to

The purpose of this DAG is to automate the feature engineering process, leveraging historical data to enhance scoring models within the banking sector. The architecture consists of a robust data pipeline that ingests various data sources, including transaction records, customer profiles, and credit history logs. The ingestion pipeline begins with data extraction from these sources, followed by a series of processing steps that include normalization to standardize data formats, transformation to derive new features, and selection to identify the most impactful features for scoring models. Quality controls are integrated throughout the process to ensure data integrity and accuracy, enabling reliable model training and evaluation. The outputs of this DAG include refined feature sets that directly contribute to improved scoring model accuracy and performance metrics. Key performance indicators (KPIs) monitored during the process include processing time and the quantifiable impact on model performance, allowing for continuous optimization. The business value of this DAG lies in its ability to enhance the predictive capabilities of scoring models, ultimately leading to better risk assessment, improved customer targeting, and increased operational efficiency in banking operations.

Part of the AI Assistants & Contact Center solution for the Banking industry.

Use cases

• Improved accuracy of scoring models for better risk assessment
• Enhanced customer targeting through refined feature sets
• Increased operational efficiency in model training processes
• Faster processing times leading to timely decision-making
• Ability to adapt to changing data landscapes in banking