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Hadoop in Banking: Event-Driven Performance Evaluation.

Monalisa Panda¹, Mamata Garnayak², Mitrabinda Ray¹

¹Department of Computer Science and Engineering, Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar, India.

Thescientificworldjournal

|January 29, 2025

Summary

This study introduces an event-driven, Hadoop-based framework for real-time banking performance evaluation. It enhances operational efficiency by analyzing transactional data and detecting anomalies for improved decision-making.

Keywords:

Hadoop architecture Hive query card-analytics distributed Hadoop-file-system event-analytics event-log financial report format log file

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Area of Science:

Computer Science
Data Science
Financial Technology

Background:

Traditional banking performance evaluation struggles with large, real-time data volumes.
Need for enhanced decision-making and operational efficiency in data-intensive banking environments.

Purpose of the Study:

To propose an event-driven, Hadoop-based architecture for real-time banking performance evaluation.
To enable monitoring of key performance indicators and detection of operational anomalies.

Main Methods:

Developed a scalable, event-driven framework integrated with the Hadoop ecosystem.
Utilized Hive queries for analyzing credit card transaction and user engagement data.
Visualized metrics such as active users, card registrations, and retention via dashboards.

Main Results:

Successfully processed and analyzed fast-moving transactional data in real-time.
Identified user activity patterns and areas for improvement in transaction analytics.
Demonstrated the framework's capability for detailed transaction snapshots and trend analysis.

Conclusions:

The Hadoop-integrated, event-driven analytics method offers a transformative approach to banking performance evaluation.
Banks can leverage this framework for competitive advantage through scalable, data-driven insights.
The proposed system provides a functional approach for exploiting extensive data-analytic capabilities.