AI-Powered Threat Detection in Digital Payments: Addressing Cyber Fraud
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V3I4P103Keywords:
AI-Powered Threat Detection, Digital Payments Security, Cyber Fraud Prevention, Machine Learning in Payments, Fraud Detection Algorithms, Real-Time Fraud Monitoring, Behavioral Biometrics, Natural Language Processing (NLP) in Fraud Detection, Predictive Analytics in PaymentsAbstract
The rapid growth of digital payments has led to a corresponding increase in cyber fraud, posing significant challenges to financial institutions, consumers, and businesses. Cyber criminals are exploiting vulnerabilities in digital payment systems to conduct various fraudulent activities such as transaction fraud, account takeovers, and phishing attacks. Traditional fraud detection methods, including rule-based and signature-based systems, are becoming increasingly ineffective against sophisticated and dynamic fraud schemes. In response, AI-powered threat detection systems have emerged as a promising solution to combat fraud in real-time. AI technologies, particularly machine learning, deep learning, and natural language processing (NLP), offer enhanced capabilities in detecting complex fraud patterns and preventing fraudulent transactions before they occur. Machine learning algorithms can analyze vast amounts of transaction data to identify anomalies and flag potentially fraudulent activities, while deep learning models leverage neural networks to recognize intricate patterns that would be difficult for human analysts or traditional systems to detect. NLP techniques are also being applied to identify phishing attempts and fraudulent communications, thereby enhancing the security of digital payment platforms. Despite the promising advancements in AI-driven fraud detection, challenges remain, including concerns over data privacy, false positives, and biases within AI models. Furthermore, ethical and regulatory considerations surrounding the use of AI in digital payments must be addressed to ensure that these systems are fair, transparent, and compliant with data protection regulations. This paper discusses the current landscape of cyber fraud in digital payments, explores the role of AI in fraud detection, and presents real-world applications and case studies of AI-powered solutions in the payment industry. Additionally, it highlights the ethical and regulatory implications of using AI for fraud detection, offering insights into the future of AI in securing digital payments
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