Automated Banking Fraud Detection for Identification and Restriction of Unauthorised Access in Financial Sector

Biswas, Ankur; Deol, Ramandeep S.; Jha, Barun Kumar; Jakka, Geethamanikanta; Suguna, M. Raja; Thomson, Benjamin Isaac

doi:10.1109/icosec54921.2022.9951931

Cited by 10 publications

(9 citation statements)

References 7 publications

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“…Fraud Detection and Risk Management: AI algorithms analyze transaction patterns to detect anomalies and potential fraudulent activities (Sharma & Pote, 2020), enhancing the security and integrity of banking operations (Biswas, 2022).…”

Section: The Role Of Ai and Technology In Indian Bankingmentioning

confidence: 99%

The Landscape of AI in Indian Banking Sector: A Theoretical Perspective

2024

Journal of Informatics Education and Research

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This paper presents an in-depth study of the evolving role of Artificial Intelligence (AI) in the Indian banking sector, emphasizing its transformative impact on service delivery, operational efficiency, and customer engagement.Highlighting key advancements and integration of AI, the paper emphasizes on AI's crucial role in modernizing traditional banking practices and addressing contemporary challenges. It offers a balanced view by discussing both the opportunities AI presents for enhancing banking services and the challenges, including technical complexities and regulatory considerations. The future outlook section anticipates the continued growth of AI in banking, focusing on its potential to further personalize banking experiences and improve risk management. The study aims to provide a holistic view of AI's integration in Indian banking, serving as a resource for understanding the dynamic interplay between technological innovation and financial services.

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Section: The Role Of Ai and Technology In Indian Bankingmentioning

confidence: 99%

The Landscape of AI in Indian Banking Sector: A Theoretical Perspective

2024

Journal of Informatics Education and Research

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“…This paper assesses the effectiveness of AI in identifying and mitigating fraudulent activities in banking operations, highlighting the significant strides made in this domain. Biswas et al (2022) emphasize the critical role of AI-based technologies and machine learning (ML) algorithms in detecting and restricting unauthorized access in the financial sector. Their research identifies data mining as an effective tool for targeting potential fraudulent activities in the credit process.…”

Section: Ai In Fraud Detection and Prevention: Assessing The Effectiv...mentioning

confidence: 99%

“…They discuss various data mining tools such as Support Vector Machine (SVM), Logistic Regression, Decision Tree, and Neural Networks, which are employed in the banking sector's fraud detection processes. The study proposes an auto encoder model for fraud detection without requiring data balancing, suggesting its effectiveness in reducing risks associated with cybercrime in the banking sector (Biswas et al, 2022).…”

Section: Ai In Fraud Detection and Prevention: Assessing The Effectiv...mentioning

confidence: 99%

AI's evolving impact in US banking: An insightful review

Akoh Atadoga,

Ogugua Chimezie Obi,

Shedrack Onwusinkwue

et al. 2024

Int. J. Sci. Res. Arch.

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This paper presents an in-depth review of the evolving impact of Artificial Intelligence (AI) in the US banking sector, focusing on how AI technologies are revolutionizing various aspects of banking operations. The review synthesizes information from a wide range of sources, including industry reports, academic literature, case studies, and expert interviews, to provide a comprehensive understanding of AI's role in the banking industry. The key findings of the review indicate that AI has significantly improved operational efficiencies in banks by automating routine tasks and enhancing decision-making processes. AI applications in customer service, such as chatbots and virtual assistants, have transformed customer interactions by offering round-the-clock support and personalized financial advice. In the realm of risk management, AI algorithms have proven effective in detecting fraudulent activities and managing credit risks, outperforming traditional methods through advanced data analysis capabilities. Additionally, AI is playing a crucial role in helping banks comply with regulatory requirements by automating compliance processes and conducting real-time transaction monitoring. The review concludes that AI offers substantial opportunities for innovation and efficiency in the US banking sector. However, it also highlights the challenges associated with AI adoption, including ethical issues, data privacy concerns, and the imperative for workforce up skilling. The paper emphasizes the need for a strategic approach to AI integration in banking, where technological advancements are balanced with human oversight, ethical considerations, and regulatory compliance to maximize the benefits of AI while mitigating its risks

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“…The implementation of an automated fraud detection system is much broader than the choice of system, and should take into account the range of tasks from the implementation of ideology among employees, formalization of procedures for collecting and storing information to changes in organizational structure and distribution of team roles. This is to some extent reflected in the work [17]. But in the work, there is no description of the automated system.…”

Section: IImentioning

confidence: 99%

Fraud Detection in Credit Card Automated System using ML with AWS SageMaker

Pujari,

Diwanji,

Malegaonkar

et al. 2023

IJRASET

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The article discusses the need for the widespread adoption of payment systems, which has been driven by advances in technology. However, the issue of fraud remains a major concern for financial institutions, as there is no one-size-fits-all approach to detecting and preventing fraudulent transactions. Machine learning has been identified as a potential solution to this problem, but it requires the development of a reliable automated system capable of handling large volumes of data in realtime. In the article, the author details the structure and setup of an automated fraud detection system for payment systems that relies on a web service hosted on the cloud. The deployment of this system is justified by utilizing Amazon Web Services as the platform, which includes Amazon Fraud Detector and Amazon A2I task type to authenticate and validate forecasts that are deemed high-risk. One instance of developing a system for detecting anomalies on Amazon DynamoDB streams is presented by utilizing AWS SageMaker, AWS Glue, and AWS Lambda. The software product aims to prevent and detect fraud in payment systems, with a rapid detection time and integration with various business institutions. The article also highlights the importance of developing a specific methodology for implementing the software product for fraud detection in payment systems.

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Automated Banking Fraud Detection for Identification and Restriction of Unauthorised Access in Financial Sector

Cited by 10 publications

References 7 publications

The Landscape of AI in Indian Banking Sector: A Theoretical Perspective

The Landscape of AI in Indian Banking Sector: A Theoretical Perspective

AI's evolving impact in US banking: An insightful review

Fraud Detection in Credit Card Automated System using ML with AWS SageMaker

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