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Smearing Machine Learning and Deep Learning in E-Commerce Transactions for Monetary Justice: Crushing Financial Frauds and Fostering Strong Financial Institutions
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Author(s): Bhupinder Singh (Sharda University, India), Anjali Raghav (Sharda University, India), Saquib Ahmed (Sharda University, India), Manmeet Kaur Arora (Sharda University, India)and Sahil Lal (Galgotias University, Greater Noida, India)
Copyright: 2025
Pages: 18
Source title:
Artificial Intelligence in Peace, Justice, and Strong Institutions
Source Author(s)/Editor(s): Christian Kaunert (Dublin City University, Ireland), Anjali Raghav (Sharda University, India), Kamalesh Ravesangar (Tunku Abdul Rahman University of Management and Technology, Malaysia), Bhupinder Singh (Sharda University, India)and Budi Agus Riswandi (Universitas Islam Indonesia, Indonesia)
DOI: 10.4018/979-8-3693-9395-6.ch014
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Abstract
Machine learning and deep learning techniques have emerged as powerful tools against several types of financial fraud. The banking and financial industry, a fundamental component of contemporary economies, is experiencing a significant upheaval due to the rise of digital transactions. This transition has resulted in an increase in financial fraud, necessitating a fundamental change in security standards. The use of advanced analytics, such as anomaly detection and pattern recognition, is examined to establish a strong defense against the continually changing strategies used by fraudulent entities in the ad click sector. Credit card management, a constant target for nefarious actions, necessitates an advanced strategy for fraud detection. This chapter examines AI-based document verification systems, highlighting their crucial role in safeguarding transactions reliant on document authentication. It addresses issues related to falsified documentation through novel methods, including the integration of blockchain technology with AI.
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