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Banking — Financial Feature Engineering for Fraud Detection

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This DAG facilitates the extraction and transformation of financial features for risk analysis. It ensures high-quality data preparation for machine learning model training, enhancing fraud detection capabilities.

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Overview

The primary purpose of this DAG is to create features from raw financial data, which are essential for analyzing financial risks and detecting anomalies in banking transactions. The data sources include transaction logs, customer profiles, and historical fraud cases. The ingestion pipeline begins with the extraction of these data sources, followed by a series of transformation steps that include normalization, aggregation, and the calculation of key performance indicators (KPIs) such as transact

The primary purpose of this DAG is to create features from raw financial data, which are essential for analyzing financial risks and detecting anomalies in banking transactions. The data sources include transaction logs, customer profiles, and historical fraud cases. The ingestion pipeline begins with the extraction of these data sources, followed by a series of transformation steps that include normalization, aggregation, and the calculation of key performance indicators (KPIs) such as transaction frequency and average transaction value. Quality control measures are implemented to validate the transformed data, ensuring its relevance and accuracy for subsequent analysis. This includes automated checks for data completeness and consistency. The validated features are then stored in a centralized data warehouse, making them readily accessible for training machine learning models aimed at identifying fraudulent activities. In case of any processing failures, a robust recovery mechanism is in place to ensure data integrity and continuity of operations. Monitoring key performance indicators such as feature generation time and data quality scores allows for ongoing assessment of the DAG's effectiveness. The business value of this DAG lies in its ability to enhance the accuracy of fraud detection systems, ultimately reducing financial losses and improving customer trust in banking services.

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

Use cases

  • Increased accuracy in fraud detection algorithms
  • Reduced financial losses due to timely anomaly detection
  • Enhanced customer trust through improved risk management
  • Streamlined data processing workflows for efficiency
  • Scalable feature engineering to adapt to evolving threats

Technical Specifications

Inputs

  • • Transaction logs from banking systems
  • • Customer profile data from CRM systems
  • • Historical fraud case records
  • • Market trend data for financial analysis
  • • Regulatory compliance data

Outputs

  • • Engineered features for machine learning models
  • • Data quality reports for validation
  • • Stored datasets in the data warehouse
  • • Anomaly detection insights for stakeholders
  • • Performance metrics for monitoring

Processing Steps

  1. 1. Extract data from transaction logs and customer profiles
  2. 2. Transform raw data into meaningful features
  3. 3. Calculate key performance indicators for transactions
  4. 4. Implement quality control checks for data accuracy
  5. 5. Store validated features in the data warehouse
  6. 6. Monitor processing performance and data quality
  7. 7. Generate reports for stakeholders on insights and metrics

Additional Information

DAG ID

WK-0010

Last Updated

2025-07-30

Downloads

111

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