A semantic rule based digital fraud detection

Ahmed, Mansoor; Ansar, Kainat; Muckley, Cal B.; Khan, Abid; Anjum, Adeel; Talha, Muhammad

doi:10.7717/peerj-cs.649

Cited by 11 publications

(8 citation statements)

References 42 publications

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“…The results show a prime for unsupervised learning, while emphasizing the effects on performance when using supervised learning methods. A fraud financial detection method was presented in [1] named Intimation Rule Based (IRB) alert generation algorithm using ontology-based system which benefited from ontology alert. The author constructed their method by including forty categories and sub-categories which effectively can capture www.ijacsa.thesai.org fraud by sending different notifications according to their extremity.…”

Section: Related Workmentioning

confidence: 99%

“…The evolution of telecommunications technologies and the adoption of electronic payments from vast financial institutions led to unanticipated spike in fraud transactions. Personal and organizational assets nowadays are vulnerable due to cybersecurity breaches [1]. In 2020 alone, banks have suffered over $28 billion in credit card losses globally.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of SVM kernels in Credit Card Fraud Detection using GANs

Alshawi

2024

IJACSA

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The technological evolution in smartphones and telecommunication systems have led people to be more dependent on online shopping and electronic payments, which created burdensome task of transaction validation for many financial institutions. This paper examined and evaluated the efficacy of Support vector machine (SVM) kernels on Generative Adversarial Network (GAN)-generated synthetic data to detect credit card fraud transactions. Four SVM kernels have been investigated and compared; linear, polynomial, sigmoid, and redial basis function. The accuracy results indicated that linear and polynomial kernels reached over 91%, while sigmoid and redial basis function reached 79% and 83% respectively. Linear and polynomial models received over 90% ROC and F1 score, in contrast the ROC scores were lower for sigmoid (81%) and redial basis function (83%). Both sigmoid and redial basis function achieved over 80% in terms of F1 score. The precision score demonstrated a high score for both linear and polynomial kernel reaching 99%. Additionally, sigmoid and redial basis function achieved over 80%. These results overcame the imbalance dataset issue through the generation of synthetic data by applying the SVM kernels using GANs algorithm.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of SVM kernels in Credit Card Fraud Detection using GANs

Alshawi

2024

IJACSA

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“…As a result, we will obviously try to find the one with the fewest attributes. Genetic algorithms are also applied to the simultaneous computation of many reductases [1,8,10].…”

Section: Reductsmentioning

confidence: 99%

“…Fraud prevention is a challenging task. Researchers from all over the world are suggesting new strategies to increase deterrent effects [1].…”

Section: Introductionmentioning

confidence: 99%

RDQN: ensemble of deep neural network with reinforcement learning in classification based on rough set theory for digital transactional fraud detection

Tekkali

Natarajan

2023

Complex Intell. Syst.

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All financial sectors are facing the most common frauds, which are digital transactional frauds. Fraudsters have always engaged in illegal activities such as stealing personal information and logging in with unauthorised credentials. Many machine learning algorithms predict whether the transaction is factual or nonfactual but fail to decrease the processing time. Hybrid models are used in this case to identify the fraud in a quick and efficient manner. This article demarcates to construct a novel model, RDQN, i.e., deep reinforcement learning, that combines with the rough set theory. This article has three steps, including data pre-processing to determine the quality of the data, which affects the learning ability of the model, determining the structural relationship and gaining useful features from the data set using rough set theory, and doing a hybridization of DNN (deep neural network) and Q learning, which is called DQN. It uses the MISH activation function and the ReLU activation function in different layers for training dynamics in the neural network. The proposed model classifies and predicts that the transaction belongs to the category implemented by the agents by activating the reward function. The reinforcement-learning agent’s performance improves based on reward assessment. This reward function gives a more precise value for each transaction, and no fraudster can escape from the agent’s sight. This novel approach improves accuracy and reduces processing time by considering the best feature selection during the process.

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“…In the end, banks, as third-party actors, yield a safe habitat in which to store such monies [2]. So, banks in their quest to reach many users across urban, semiurban, and rural dwellings [3] -have sought better and improved means to accomplish this feat, with credit cards proffering one of many such solutions. With the many challenges of infrastructure and coverage areas [4]- [6] -banks now introduce technologies and platforms such as credit cards, agent banking, point of sales, etc -as means to remain connected with their numerous customer-base, and also ensure financial inclusivity [7].…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Random Forest Model via Synthetic Minority Oversampling Technique for Credit-Card Fraud Detection

Aghware,

Ojugo,

Adigwe

et al. 2024

J. Comput. Theor. Appl.

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Fraudsters increasingly exploit unauthorized credit card information for financial gain, targeting un-suspecting users, especially as financial institutions expand their services to semi-urban and rural areas. This, in turn, has continued to ripple across society, causing huge financial losses and lowering user trust implications for all cardholders. Thus, banks cum financial institutions are today poised to implement fraud detection schemes. Five algorithms were trained with and without the application of the Synthetic Minority Over-sampling Technique (SMOTE) to assess their performance. These algorithms included Random Forest (RF), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Support Vector Machines (SVM), and Logistic Regression (LR). The methodology was implemented and tested through an API using Flask and Streamlit in Python. Before applying SMOTE, the RF classifier outperformed the others with an accuracy of 0.9802, while the accuracies for LR, KNN, NB, and SVM were 0.9219, 0.9435, 0.9508, and 0.9008, respectively. Conversely, after the application of SMOTE, RF achieved a prediction accuracy of 0.9919, whereas LR, KNN, NB, and SVM attained accuracies of 0.9805, 0.9210, 0.9125, and 0.8145, respectively. These results highlight the effectiveness of combining RF with SMOTE to enhance prediction accuracy in credit card fraud detection.

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A semantic rule based digital fraud detection

Cited by 11 publications

References 42 publications

Comparison of SVM kernels in Credit Card Fraud Detection using GANs

Comparison of SVM kernels in Credit Card Fraud Detection using GANs

RDQN: ensemble of deep neural network with reinforcement learning in classification based on rough set theory for digital transactional fraud detection

Enhancing the Random Forest Model via Synthetic Minority Oversampling Technique for Credit-Card Fraud Detection

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