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Automated credit assessment framework using ETL process and machine learning.

Neepa Biswas1, Anindita Sarkar Mondal1, Ari Kusumastuti2

  • 1Department of Information Technology, Jadavpur University, Salt Lake Campus, Kolkata, West Bengal 700106 India.

Innovations in Systems and Software Engineering
|January 9, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an automated credit risk assessment method using machine learning and ETL processes, aligning with Basel II standards for improved financial decision-making.

Keywords:
Automated credit risk assessmentData integrationData warehouseETLMachine learning

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Area of Science:

  • Financial Risk Management
  • Machine Learning Applications
  • Business Intelligence

Background:

  • Real-time enterprise data analysis via Business Intelligence (BI) is vital for operational and strategic decisions.
  • Automated ETL (extraction, transformation, load) processes enable near real-time data ingestion for BI.
  • Automated credit decision-making systems enhance risk management, operational efficiency, and regulatory compliance for lenders.

Purpose of the Study:

  • To develop and evaluate an automated credit risk assessment methodology for the financial domain.
  • To leverage machine learning classification techniques for self-regulating data categorization in credit scoring.
  • To integrate automated ETL processes for data preparation in machine learning model development.

Main Methods:

  • Empirical approach using logistic regression and neural network classification.
  • Adherence to Basel II standards for calculating expected loss.
  • Implementation of an automated ETL process for data integration and machine learning model building.

Main Results:

  • Demonstrated the feasibility of machine learning for automated credit risk assessment.
  • Validated the integration of automated ETL processes for efficient data handling.
  • Showcased compliance with Basel II standards in the proposed methodology.

Conclusions:

  • The proposed machine learning-based methodology offers an effective approach to automated credit risk assessment.
  • Automated ETL processes are crucial for enabling real-time data-driven credit decisions.
  • The study provides a foundation for enhanced risk management and operational efficiency in financial institutions.