Pattern recognition using LSTM in financial sectors

Manivannan, Neha (2024) Pattern recognition using LSTM in financial sectors. Masters thesis, Dublin, National College of Ireland.

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Abstract

This project looks into how Artificial Intelligence (AI) can be used in government work. It’s about finding the right mix between using new technology and being careful about things like keeping data private and safe. The main part of the study was making and testing a special computer model that can handle and understand detailed bank transaction data. The model uses a smart method called Long Short-Term Memory (LSTM) networks, showing that AI can improve the way we analyze data, a job usually done by people. The study shows two sides of using AI in government. Firstly, AI is great for doing complex jobs by itself, making things faster, and finding important details that might be missed by people. But, it also points out worries about keeping data private and the dangers of using AI wrongly, especially with the kind of private information that governments handle. In this project, we see that our encrypted data can be decrypted by hackers or terrorists these days. It is really for the government and securities to keep our financial details more safe and secure. This study adds a lot to both learning about and using AI in government work. It shows how important it is to protect data well and use AI in a good and fair way. For researchers, it opens up new areas to explore about AI’s role in government. For businesses, it creates chances for software firms and AI consulting services to help government agencies work better, but with a strong focus on keeping data safe and private. Overall, the project points out that AI can be used anywhere it can be misused by hackers or terrorists or can be safely used by the government, thinking about these its good points and possible dangers.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Syed, Muslim Jameel UNSPECIFIED
Subjects:	J Political Science > JS Local government Municipal government Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence
Divisions:	School of Computing > Master of Science in Artificial Intelligence
Depositing User:	Tamara Malone
Date Deposited:	04 Apr 2025 15:17
Last Modified:	04 Apr 2025 15:17
URI:	https://norma.ncirl.ie/id/eprint/7369

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