Automatic Test Data Generation in Banking Applications Using Deep Learning

Bagh, Taniya (2023) Automatic Test Data Generation in Banking Applications Using Deep Learning. Masters thesis, Dublin, National College of Ireland.

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Abstract

It is well known that data associated with the Banking domain are huge in volume. With such large numbers come concerns regarding security and Privacy. These concerns are addressed with proper testing of the applications associated with the domain. In the present scenario, with the growth of technology, there is a rise in complexity in terms of handling and testing the software. Testing in the banking domain is one of the most crucial parts of the industry since it ensures not only the functionality of an organization but also responsible for reliability and security of the data indulged with the association. Therefore, robust testing is necessary to achieve accurate results which further assists in identifying issues and vulnerabilities that can potentially harm the financial data which leads to security breaches, and help them rectify the issues, safeguarding the sensitive data. In this paper, the Application of various deep learning techniques such as Generative Adversarial Networks, Variational Encoders, Recurrent Neural Networks, and other Hybrid models will be utilized to produce synthetic transaction data and the performance of the model will be evaluated by calculating statistical values such as MSE, MAE and RMSE.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Palaniswamy, Sasirekha UNSPECIFIED
Subjects:	H Social Sciences > HG Finance Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HG Finance > Banking Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	07 May 2025 10:24
Last Modified:	07 May 2025 10:24
URI:	https://norma.ncirl.ie/id/eprint/7494

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