DIGITAL CHEQUE SIGNATURE FRAUD DETECTION USING MACHINE LEARNING

Registration ID: IJNRD_224554

Published ID: IJNRD2407031

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Bursu Nithya Thulasi, P Satish Kumar, Mannuru malleswari

DIGITAL CHEQUE SIGNATURE, FRAUD DETECTION, MACHINE LEARNING TECHNIQUES

Digital transactions have transformed the landscape of financial operations, offering unprecedented convenience and speed. However, alongside these advancements, the proliferation of digital cheque signature fraud has posed significant challenges to the security and integrity of financial transactions. Detecting fraudulent activities, particularly forged cheque signatures, remains a critical task for financial institutions aiming to protect their customers and maintain trust in digital banking systems. This study investigates the application of machine learning techniques to develop a robust fraud detection system capable of effectively identifying and mitigating digital cheque signature fraud. The research begins by acquiring and curating a comprehensive dataset comprising genuine and fraudulent digital cheque signatures. Various machine learning algorithms, including Support Vector Machines (SVM), Random Forests, and Convolutional Neural Networks (CNNs), are explored for their efficacy in learning and distinguishing fraudulent patterns from authentic signatures. The models are trained on labeled data to optimize performance metrics such as accuracy, precision, recall, and F1-score. Implementation of the developed system into banking infrastructure facilitates real-time analysis and validation of digital cheque signatures during transaction processing. Cloud-based deployment ensures scalability and accessibility across diverse banking platforms, enhancing operational efficiency and fraud prevention capabilities. Performance evaluation metrics validate the effectiveness of the proposed model in detecting fraudulent cheque signatures. Cross-validation techniques ensure the model's robustness and generalization capabilities across different subsets of data, providing insights into its reliability in real-world applications. Ethical considerations, including privacy preservation and responsible data usage, are prioritized throughout the development and deployment phases of the fraud detection system. Future research directions include exploring ensemble learning approaches, integrating explainable AI techniques for enhanced model interpretability, and collaborating with financial institutions to enhance the system's adaptability to evolving fraud tactics. In conclusion, the application of machine learning in digital cheque signature fraud detection represents a proactive strategy in combating financial fraud and enhancing security in digital banking environments. By leveraging advanced technologies and comprehensive datasets, this study contributes to advancing fraud detection capabilities, safeguarding financial transactions, and preserving trust in digital banking systems.

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Bursu Nithya Thulasi, P Satish Kumar, & Mannuru malleswari (July-2024). DIGITAL CHEQUE SIGNATURE FRAUD DETECTION USING MACHINE LEARNING. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(7), a297-a306. https://ijnrd.org/papers/IJNRD2407031.pdf

Volume 9 Issue 7, July-2024

Pages : a297-a306

Paper Reg. ID: IJNRD_224554

Published Paper Id: IJNRD2407031

Research Area: Computer Engineering 

Author Type: Indian Author

Country: Cuddapah, Andra Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2407031.pdf

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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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