Improved fraud detection in e-commerce transactions

Shaji, Jisha; Panchal, Dakshata

doi:10.1109/cscita.2017.8066537

Cited by 16 publications

(13 citation statements)

References 8 publications

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“…In recent years, there has been an increasing interest in fuzzy neural network approach where author [3] used neuro-fuzzy approach for credit card fraud detection; author [19] used hybrid fuzzy C-Means clustering algorithm and Multi-Layer feed forward neural network with back-propagation learning algorithm to analyzed browsing patterns of users to extract information in web server; author [12] used hybrid approach based on neural networks and fuzzy logic for intrusion detection system modeling using Self-Organizing Map; author [16] used Neuro-Fuzzy Interface System for neuro fuzzy based attack detection; author [18] also used Adaptive Neuro-Fuzzy Inference System which is a hybrid approach based on neural networks and fuzzy logic for fraud detection in e-Commerce transactions; author [9] used Adaptive Neuro-Fuzzy Inference System for domestic water usage prediction; and author [26] used Adaptive neuro-fuzzy inference system for continuous implicit authentication for mobile devices. Previous research has shown that the fuzzy neural network can be used for continuous authentication.…”

Section: Keystroke Analysis Methodsmentioning

confidence: 99%

An Initial Framework of Fuzzy Neural Network Approach for Online Learner Verification Process

Sadikan¹

2019

IJATCSE

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Online learning is become more popular among university due to its flexibility and adaptability. The student authentication as online learner is widely seen as a major concern for online assessment. In most cases, there is absent of face-to-face supervision during online assessment, this situation leads the student to use or find help from others in order to get high scores in their result. This paper address the issue related to online assessment. The main objective of this work was to propose the use of online learner verification framework. This proposed solution utilizes the keystroke analysis and activity-based authentication for the online learner authentication. A fuzzy neural network is used to train and validate the online learner's identity. The proposed framework can be implementing in any online learning environment for verifying online learner identity.

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Section: Keystroke Analysis Methodsmentioning

confidence: 99%

An Initial Framework of Fuzzy Neural Network Approach for Online Learner Verification Process

Sadikan¹

2019

IJATCSE

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“…These technologies have not only enhanced the accuracy of fraud detection systems but have also introduced adaptability and learning capabilities, allowing these systems to evolve with changing fraudster tactics. Notably, the Adaptive Neuro-Fuzzy Inference System has been applied to detect fraudulent transactions in e-commerce with notable success, showcasing the potential of hybrid systems that combine neural networks and fuzzy logic (Shaji and Panchal, 2017). Further, the implementation of feature engineering and advanced algorithms such as CatBoost has improved the accuracy of detecting fraudulent activities in financial transactions (Chen and Han, 2021).…”

Section: Impact Analysis Improvements In Fraud Detection Accuracymentioning

confidence: 99%

Transforming Fintech Fraud Detection With Advanced Artificial Intelligence Algorithms

Philip Olaseni Shoetan,

Babajide Tolulope Familoni

2024

Financ. account. res. j.

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The rapid evolution of financial technology (fintech) platforms has exponentially increased the volume and sophistication of financial transactions, concurrently elevating the risk and complexity of fraudulent activities. This necessitates a paradigm shift in fraud detection methodologies towards more agile, accurate, and predictive solutions. This paper presents a comprehensive study on the transformative potential of advanced Artificial Intelligence (AI) algorithms in enhancing fintech fraud detection mechanisms. By leveraging cutting-edge AI techniques including deep learning, machine learning, and natural language processing, this research aims to develop a robust fraud detection framework capable of identifying, analyzing, and preventing fraudulent transactions in real-time. Our methodology encompasses the deployment of several AI algorithms on extensive datasets comprising genuine and fraudulent financial transactions. Through a comparative analysis, we identify the most effective algorithms in terms of accuracy, efficiency, and scalability. Key findings reveal that deep learning models, particularly those employing neural networks, outperform traditional machine learning models in detecting complex and nuanced fraudulent activities. Furthermore, the integration of natural language processing enables the extraction and analysis of unstructured data, significantly enhancing the detection capabilities. Conclusively, this paper underscores the critical role of advanced AI algorithms in revolutionizing fintech fraud detection. It highlights the superior performance of AI-based models over conventional methods, offering fintech platforms a more dynamic and predictive approach to fraud prevention. This research not only contributes to the academic discourse on financial security but also provides practical insights for fintech companies striving to safeguard their operations against fraud. Keywords: Artificial Intelligence, Fintech, Fraud Detection, Ethical Ai, Regulatory Compliance, Data Privacy, Algorithmic Bias, Predictive Analytics, Blockchain Technology, Quantum Computing, Interdisciplinary Collaboration, Innovation, Transparency, Accountability, Continuous Learning, Ethical Principles, Real-Time Processing, Financial Sector.

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“…It overcomes the difficulties of minimum support and confidence, optimizes the execution times, reduces the excessive generation of rules, and helps make the results more intuitive, there by facilitating the work of fraud analysts, for a scoring risk. According to the profile of the user and the rules, we decide whether the transaction is non-fraudulent and if the framework can continue to the next [7] [8] [21].…”

Section: Fuzzy Association Rules (Far)mentioning

confidence: 99%

Adaptive Model for Credit Card Fraud Detection

Sadgali¹,

Sael²,

Benabbou³

2020

Int. J. Interact. Mob. Technol.

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<p>While the flow of banking transactions is increasing, the risk of credit card fraud is becoming greater particularly with the technological revolution that we know, fraudulent are improve and always find new methods to deal with the preventive measures that financial systems set up. Several studies have proposed predictive models for credit card fraud detection based on different machine learning techniques. In this paper, we present an adaptive approach to credit card fraud detection that exploits the performance of the techniques that have given high level of accuracy and consider the type of transaction and the client's profile. Our proposition is a multi-level framework, which encompasses the banking security aspect, the customer profile and the profile of the transaction itself.</p>

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Improved fraud detection in e-commerce transactions

Cited by 16 publications

References 8 publications

An Initial Framework of Fuzzy Neural Network Approach for Online Learner Verification Process

An Initial Framework of Fuzzy Neural Network Approach for Online Learner Verification Process

Transforming Fintech Fraud Detection With Advanced Artificial Intelligence Algorithms

Adaptive Model for Credit Card Fraud Detection

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