Implementation of Bengali voice signature authentication techniques in financial systems using deep learning
| dc.contributor.advisor | Sadeque, Farig Yousuf | |
| dc.contributor.author | Rashid, Mahinur | |
| dc.contributor.author | Ahmad, Safwan | |
| dc.contributor.author | Faiza Binte, Arif | |
| dc.contributor.author | Anjum, Ramisa | |
| dc.contributor.author | Rimu, Sanjida Arefin | |
| dc.contributor.department | Department of Computer Science and Engineering | |
| dc.date.accessioned | 2026-02-17T04:24:00Z | |
| dc.date.available | 2026-02-17T04:24:00Z | |
| dc.date.copyright | 2025 | |
| dc.date.issued | 2025-10 | |
| dc.description | Cataloged from PDF version of thesis. | |
| dc.description | Includes bibliographical references (pages 128-131). | |
| dc.description | This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2025. | en_US |
| dc.description.abstract | In developing-nations like Bangladesh, the need for more secure user-friendly authentication methods to protect financial transactions are a substantial need nowadays due to the growing digitalization in finances. Nowadays the need for more secure, fast and user-friendly authentication methods in the financial sectors is increasing rapidly and here the voice-based authentication provides an aspiring substitution to the regular conventional methods. The aim of this paper is to discuss the area of coverage for the potential application of deep learning models in reference to implementing voice signature authentication procedures for cash-out processes in physical banking and digital mobile payment systems. The methodology includes the development and testing of a voice verification system and its integration with the already existing banking and mobile payment infrastructures. Initially, the Bengali voice data samples are collected from available sources, and then analyzed using statistical methods based on differentiable voice/tone metrics and thematic analysis for purposes of user identification and detection of fraud and AI generated voices. It greatly relies on the use of deep learning techniques in voice signature analysis and authentication along with ensuring the authentication procedures are safe and smooth. Key findings support this study in regarding voice signature authentication process, as it can be a promising and cheap addition to the security protocols by increasing accuracy, reliability and also reduce fraud along with improving smooth user experience in cash-out processes. Additionally, this study offers real-world proof of the successful use of deep learning in financial authentication systems, this study adds up the study to the vast domain of deep learning. This study intends to contribute to the improvement of financial security methods through preserving the sensitive financial data and by tackling the growing need for more secure and user-friendly intuitive authentication techniques. Subsequent future research could dive deeper into voice verification technology and its application in other sensitive sectors for further developments; opening a new door of authentication protocol in a country like Bangladesh. | en_US |
| dc.description.degree | Bachelor of Science in Computer Science and Engineering | |
| dc.description.statementofresponsibility | Mahinur Rashid | |
| dc.description.statementofresponsibility | Safwan Ahmad | |
| dc.description.statementofresponsibility | Faiza Binte Arif | |
| dc.description.statementofresponsibility | Ramisa Anjum | |
| dc.description.statementofresponsibility | Sanjida Arefin Rimu | |
| dc.format.extent | 131 pages | |
| dc.identifier.other | ID 22301714 | |
| dc.identifier.other | ID 21101311 | |
| dc.identifier.other | ID 21201498 | |
| dc.identifier.other | ID 21301399 | |
| dc.identifier.other | ID 21201655 | |
| dc.identifier.uri | http://hdl.handle.net/10361/27531 | |
| dc.language.iso | en | en_US |
| dc.publisher | BRAC University | en_US |
| dc.rights | BRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. | |
| dc.subject | Bengali voice authentication | en_US |
| dc.subject | Deep learning | en_US |
| dc.subject | Financial security | en_US |
| dc.subject | Mobile payment systems | en_US |
| dc.subject | Fraud detection | en_US |
| dc.subject | User-friendly authentication | en_US |
| dc.subject.lcsh | Speaker recognition. | |
| dc.subject.lcsh | Voiceprints. | |
| dc.subject.lcsh | Biometric identification. | |
| dc.subject.lcsh | Authentication (Computer security). | |
| dc.subject.lcsh | Deep learning (Machine learning). | |
| dc.subject.lcsh | Electronic funds transfers. | |
| dc.subject.lcsh | Bengali language--Speech recognition. | |
| dc.subject.lcsh | Automatic speech recognition. | |
| dc.title | Implementation of Bengali voice signature authentication techniques in financial systems using deep learning | en_US |
| dc.type | Thesis | en_US |
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