Big Data Analytics in Banking: Unlocking Insights for Strategic Decisions
Keywords:
Big Data Analytics, Fraud Management, Feedback Analysis, B2B Analysis, E-Wiring Transaction.Abstract
Big data analytics in the banking sector is the methodical application of data and quantitative analytical methods to learn more about consumer behavior, spot trends, lower risks, and find expansion prospects. In order to optimize supply chain financing and boost the effectiveness of marketing campaigns and strategies, commercial banks use big data analytics to examine internal business-to-business data, as this article illustrates. Throughout the investigation, a range of analytical methods will be used, such as customer and predictive analytics, market and cross-selling, E-wiring analytics, risk and fraud management, and customer base segmentation. The purpose of this study is to offer insightful information about the application of these techniques. Based on this data, a number of tables and charts will be produced to show how analytics, such as transactional analytics, fraud and security measures, and feedback analysis, are applied in the banking industry. Many financial industries are presently using big data analytics to help banks improve the services they offer to their internal and external customers while also fortifying their active and passive security measures. It is clear from this study that big data analytics helps companies to glean useful information from enormous amounts of data. Additionally, it is demonstrated how banks may use consumer behavior analysis, pattern identification, and risk mitigation techniques to improve decision-making and the general customer experience.
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