Automated credit assessment framework using ETL process and machine learning

Biswas, Neepa, Mondal, Anindita Sarkar, Kusumastuti, Ari, Saha, Swati and Mondal, Kartick Chandra (2022) Automated credit assessment framework using ETL process and machine learning. Innovations in System and Software Engineering, 18 (4). ISSN 16145046; 16145054

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In the current business scenario, real-time analysis of enterprise data through Business Intelligence (BI) is crucial for supporting operational activities and taking any strategic decision. The automated ETL (extraction, transformation, and load) process ensures data ingestion into the data warehouse in near real-time, and insights are generated through the BI process based on real-time data. In this paper, we have concentrated on automated credit risk assessment in the financial domain based on the machine learning approach. The machine learning-based classification techniques can furnish a self-regulating process to categorize data. Establishing an automated credit decision-making system helps the lending institution to manage the risks, increase operational efficiency and comply with regulators. In this paper, an empirical approach is taken for credit risk assessment using logistic regression and neural network classification method in compliance with Basel II standards. Here, Basel II standards are adopted to calculate the expected loss. The required data integration for building machine learning models is done through an automated ETL process. We have concluded this research work by evaluating this new methodology for credit risk assessment.

Item Type: Journal Article
Keywords: Data integration; ETL; Data warehouse; Machine learning; Automated credit risk assessment
Subjects: 01 MATHEMATICAL SCIENCES > 0103 Numerical and Computational mathematics > 010399 Numerical and Computational Mathematics not elsewhere classified
Depositing User: Ari Kusumastuti
Date Deposited: 14 Apr 2023 14:51


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