Panda Monalisa, Garnayak Mamata, Ray Mitrabinda, Rath Smita, Mohanta Anuradha, Priyadarshini Sushree Bibhuprada B
Department of Computer Science and Engineering, Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar, India.
Department of Computer Science, Kalinga Institute of Social Sciences, Deemed to be University, Bhubaneswar, Odisha, India.
ScientificWorldJournal. 2025 Jan 21;2025:4375194. doi: 10.1155/tswj/4375194. eCollection 2025.
In today's data-intensive atmosphere, performance evaluation in the banking industry depends on timely and accurate insights, leading to better decision making and operational efficiency. Traditional methods for assessing bank performance often need to be improved to handle the volume, velocity, and variety of data generated in real time. This study proposes an event-driven approach for performance evaluation in banking alongside a Hadoop-based architecture. Infused with real-time event analytics, this scalable framework can process and analyze fast-moving transactional data. Hence, the framework allows banks to monitor key performance indicators and detect real-time operational anomalies. This is supported by the Hadoop ecosystem, which provides distribution of the processing and storage, making it fit for handling large datasets with high fault tolerance and parallel computation levels. This study analyzes transaction and user engagement data using Hive queries, focusing on credit card transactions via MasterCard. Two cases are examined: a detailed snapshot of individual transactions and a five-day trend analysis. Metrics like active users, card registrations, and retention are visualized through dashboards. Findings reveal user activity patterns and areas for improvement, emphasizing scalable, data-driven approaches for transaction analytics. This framework conceives a functional approach for banks to exploit extensive data-analytic capabilities to strive for competitive advantage and survivability of a business by adding any required metrics. The findings signify that the Hadoop-integrated event-driven analytics method could act as a game changer for performance evaluation in the banking sector.
在当今数据密集型环境中，银行业的绩效评估依赖于及时且准确的洞察，从而实现更好的决策制定和运营效率。传统的银行绩效评估方法往往需要改进，以处理实时生成的数据量、速度和多样性。本研究提出了一种用于银行业绩效评估的事件驱动方法以及一种基于Hadoop的架构。该可扩展框架融入了实时事件分析功能，能够处理和分析快速移动的交易数据。因此，该框架使银行能够监控关键绩效指标并检测实时运营异常。这得到了Hadoop生态系统的支持，该生态系统提供处理和存储的分布式功能，使其适合处理具有高容错性和并行计算水平的大型数据集。本研究使用Hive查询分析交易和用户参与数据，重点关注万事达信用卡交易。研究考察了两个案例：单个交易的详细快照和为期五天的趋势分析。诸如活跃用户、卡注册和留存率等指标通过仪表板进行可视化展示。研究结果揭示了用户活动模式和改进领域，强调了用于交易分析的可扩展、数据驱动方法。该框架为银行构思了一种功能性方法，通过添加任何所需指标来利用广泛的数据分析能力，以争取竞争优势和业务的生存能力。研究结果表明，集成Hadoop的事件驱动分析方法可能成为银行业绩效评估的变革者。
