Automated credit assessment framework using ETL process and machine learning.

IF 1.1 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Neepa Biswas, Anindita Sarkar Mondal, Ari Kusumastuti, Swati Saha, Kartick Chandra Mondal
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引用次数: 0

Abstract

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.

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使用 ETL 流程和机器学习的自动信用评估框架。
在当前的商业环境下,通过商业智能(BI)对企业数据进行实时分析,对于支持运营活动和做出任何战略决策都至关重要。自动化 ETL(抽取、转换和加载)流程可确保近乎实时地将数据摄入数据仓库,并通过基于实时数据的 BI 流程产生洞察力。在本文中,我们重点讨论了基于机器学习方法的金融领域信用风险自动评估。基于机器学习的分类技术可以为数据分类提供一个自我调节的过程。建立自动化信贷决策系统有助于贷款机构管理风险、提高运营效率并符合监管机构的要求。本文根据《巴塞尔 II 新资本协议》的标准,采用逻辑回归和神经网络分类方法进行信用风险评估。本文采用《巴塞尔 II 新资本协议》标准来计算预期损失。建立机器学习模型所需的数据整合是通过自动 ETL 流程完成的。我们通过评估这一新的信用风险评估方法,结束了这项研究工作。
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来源期刊
Innovations in Systems and Software Engineering
Innovations in Systems and Software Engineering COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
3.80
自引率
8.30%
发文量
75
期刊介绍: Innovations in Systems and Software Engineering: A NASA Journal addresses issues and innovations in Systems Engineering, Systems Integration, Software Engineering, Software Development and other related areas that are specifically of interest to NASA. The journal includes peer-reviewed world-class technical papers on topics of research, development and practice related to NASA''s missions and projects, topics of interest to NASA for future use, and topics describing problem areas for NASA together with potential solutions. Papers that do not address issues related to NASA are of course very welcome, provided that they address topics that NASA might like to consider for the future. Papers are solicited from NASA and government employees, contractors, NASA-supported academic and industrial partners, and non-NASA-supported academics and industrialists both in the USA and worldwide. The journal includes updates on NASA innovations, articles on NASA initiatives, papers looking at educational activities, and a State-of-the-Art section that gives an overview of specific topic areas in a comprehensive format written by an expert in the field.
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