Big Data Mining and Analysis in the Financial Industry

Surun Mu ()
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Surun Mu: Tianjin University of Finance and Economics

A chapter in Proceedings of the 2024 3rd International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2024), 2024, pp 275-283 from Springer

Abstract: Abstract The integration of big data mining into the financial sector has revolutionized the way institutions operate, offering unprecedented insights and capabilities. The paper delves into the multifaceted applications of big data, such as enhancing risk management by identifying patterns and anomalies that may indicate potential threats, thereby allowing for proactive measures to mitigate financial risks. In the realm of fraud detection, big data analytics has proven to be a powerful tool, employing sophisticated algorithms to detect suspicious activities and prevent fraudulent transactions, safeguarding both the institution and its customers. Customer behavior analysis has been transformed through the use of big data, enabling financial institutions to understand consumer preferences and trends, thereby personalizing financial products and services to meet individual needs more effectively. Credit scoring has also been significantly impacted by big data, with advanced models now capable of assessing creditworthiness in a more nuanced and accurate manner, taking into account a wider array of data points. This synergy allows the financial industry to not only harness the power of big data for improved services and operational efficiencies but also to uphold the principles of data integrity and consumer protection. By striking this balance, the financial sector can navigate the complexities of big data mining with confidence, leveraging its capabilities to drive forward a more secure, efficient, and customer-centric industry.

Keywords: Big Data Analytics; Financial Industry; Risk Management; Fraud Detection; Customer Behavior Analysis; Credit Scoring (search for similar items in EconPapers)
Date: 2024
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DOI: 10.2991/978-94-6463-548-5_30

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