大数据
分析
风险管理
计算机科学
信用风险
功能(生物学)
业务
金融服务
人口
数据管理
数据科学
风险分析(工程)
计算机安全
财务
数据挖掘
人口学
进化生物学
社会学
生物
作者
M VenkateswaraRao,Sai Srinivas Vellela,Venkateswara Reddy B,Nagagopiraju Vullam,Khader Basha Sk,D Roja
标识
DOI:10.1109/icaccs57279.2023.10113084
摘要
The banking industry has experienced significant transformations in terms of how effectively they function and provide services over the past few decades. The banking services infrastructure will be challenged by an expanding worldwide population. While serving a sizable segment of clients, it improves the number of consumers, online transactions, and produces enormous amounts of data. Today, banks in the US and other nations use Big Data Analytics (BDA) to handle this scenario daily. It looks for different trends in their databases in order to help their organizations make more money. Banks are changing from a straightforward approach to managing credit risk to a comprehensive risk management methodology. Banking dangers originate from numerous systems and channels. Big data technology offers an insightful and effective method for managing data, making it appropriate for use in risk management applications that call for complicated data analysis and increased data. The big data architecture of a banking credit investigation and integrated risk management system is described in this analysis. Comparisons and analyses unambiguously show that the described system performs better. Hence, this model shows that efficiency and security has improved.
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