Open AccessBig Data Analytics for Improved Risk Management and Customer Segregation in Banking Applications
Author(s) -
Subarna Shakya,
S. Smys
Publication year - 2021
Publication title -
journal of ismac the journal of iot in social, mobile, analytics, and cloud
Language(s) - English
Resource type - Journals
ISSN - 2582-1369
DOI - 10.36548/jismac.2021.3.005
Subject(s) - big data , analytics , data science , computer science , financial institution , customer intelligence , retail banking , asset (computer security) , business , customer retention , finance , computer security , data mining , service quality , marketing , service (business)
While the phrase Big Data analytics is not only applicable for a certain realm of technology, diverse business segments like banking also benefit from the use of advanced mathematical and statistical models like predictive analysis, artificial intelligence, and data mining. If it is a query that is data volume generated in a bank or any financial institution is huge, it is absolutely a yes. As per the recent survey, it is observed that banks worldwide aren't just concentrating on improving the asset quality and fulfilling regulatory compliance but on the lookout for a digital convergence strategy to reach customers effectively in delivering services and products. As most of the data generated in internet banking and ATM transactions are unstructured accounting around for 2.5 quintillion bytes useful for fraud detection, risk management, and customer satisfaction, the use of trending Big Data Analytics methodology can be used to tackle the challenges and competition among banks. There are surplus advantages of Big Data strategy in the banking field and in this paper, we have made an analysis over Big Data Analytics on banking applications and their related concepts.