Credit card fraud detection using fuzzy ID3

Askari, S Md S; Hussain, Md. Anwar

doi:10.1109/ccaa.2017.8229897

Cited by 24 publications

(11 citation statements)

References 16 publications

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“…e measurement matrices used to evaluate the accuracy of the performance are the precision, recall, F1-score, average precision (AP), and confusion matrix [14,46]. Precision is a metric that assesses a model's ability to forecast positive classifications [47][48][49]. Precision = TP/(TP + FP).…”

Section: Performance Metricsmentioning

confidence: 99%

Solving Misclassification of the Credit Card Imbalance Problem Using Near Miss

Mqadi

Naicker

Adeliyi

2021

Mathematical Problems in Engineering

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In ordinary credit card datasets, there are far fewer fraudulent transactions than ordinary transactions. In dealing with the credit card imbalance problem, the ideal solution must have low bias and low variance. The paper aims to provide an in-depth experimental investigation of the effect of using a hybrid data-point approach to resolve the class misclassification problem in imbalanced credit card datasets. The goal of the research was to use a novel technique to manage unbalanced datasets to improve the effectiveness of machine learning algorithms in detecting fraud or anomalous patterns in huge volumes of financial transaction records where the class distribution was imbalanced. The paper proposed using random forest and a hybrid data-point approach combining feature selection with Near Miss-based undersampling technique. We assessed the proposed method on two imbalanced credit card datasets, namely, the European Credit Card dataset and the UCI Credit Card dataset. The experimental results were reported using performance matrices. We compared the classification results of logistic regression, support vector machine, decision tree, and random forest before and after using our approach. The findings showed that the proposed approach improved the predictive accuracy of the logistic regression, support vector machine, decision tree, and random forest algorithms in credit card datasets. Furthermore, we found that, out of the four algorithms, the random forest produced the best results.

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Section: Performance Metricsmentioning

confidence: 99%

Solving Misclassification of the Credit Card Imbalance Problem Using Near Miss

Mqadi

Naicker

Adeliyi

2021

Mathematical Problems in Engineering

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“…The applied methodology overcomes the difficulties of minimum support and confidence, optimizes the execution times, reduces the excessive generation of rules, and makes the results more intuitive, thereby facilitating the work of fraud analysts. In addition, Askari [8] proposed fraud detection algorithm based on Fuzzy-ID3.…”

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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“…With the help of this proposed method, the complex multiattributes decision issues can be resolved easily. [17] Presented the use of fuzzy logic which is the combination of mathematical process by which an ID3 decision tree system is generated in this paper. They implemented the FuzzITree algorithm on the normalized training data.…”

Section: Literature Reviewmentioning

confidence: 99%

Fraud prediction for credit card using classification method

Monika

Kaur

2018

IJET

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With the improvement of innovation like credit cards, debit cards, mobile banking, Internet managing an account is the mainstream medium to exchange the cash starting with one record then onto the next. Credit card is picking up fame day by day which expands the online exchange with the expansion in online shopping, online charge payment, insurance premium and different charges so the extortion cases identified with this are likewise expanding and it puts an extraordinary anxiety on the economy, affecting the two clients and budgetary bodies. It costs cash as well as an awesome measure of time to reestablish the damage done. In this paper, we look whether data mining procedures are valuable to estimate and categorize the client's credit risk score (normal/fraud) to beat the future dangers. The reason for existing is to keep the clients from online exchange by utilizing particular Data mining classification methods. The fakes are ascertained by Naïve Bayes method way to deal with break down the exchange is actual or fake. The exploratory outcome demonstrates that our model has great classification accuracy, recall and precision.

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Credit card fraud detection using fuzzy ID3

Cited by 24 publications

References 16 publications

Solving Misclassification of the Credit Card Imbalance Problem Using Near Miss

Solving Misclassification of the Credit Card Imbalance Problem Using Near Miss

Adaptive Model for Credit Card Fraud Detection

Fraud prediction for credit card using classification method

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