Hadoop in Banking: Event-Driven Performance Evaluation.

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.