CNN Data Mining Algorithm for Detecting Credit Card Fraud

Vardhani, P.; Priyadarshini, Yuvika; Narasimhulu, Y.

doi:10.1007/978-981-13-0059-2_10

Cited by 19 publications

(11 citation statements)

References 6 publications

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“…The discovery of hidden information is achieved by running data mining algorithms that combine statistics with computer science to mine valuable information from a seemingly meaningless data jumble. Data mining can be applied in various fields such as engineering (Adekitan et al., 2019; Saini and Aggarwal, 2018), business management (Zuo et al., 2016), marketing and product design (Jin et al., 2019), computer science (Mahendra et al., 2019), education (Ibrahim et al., 2019; Porouhan, 2018), genetics (Noreña et al., 2018), biological studies (Gu et al., 2018), facility maintenance management (Miguel-Cruz et al., 2019), health and drug development studies (Keserci et al., 2017), chemistry and toxicity analysis (Saini and Srivastava, 2019), meteorology (Kovalchuk et al., 2019), transportation safety (Divya et al., 2019) and traffic management (Amiruzzaman, 2019), fraud detection (Vardhani et al., 2019), and so forth. In the educational sector, volumes of data are daily generated from various teaching and learning activities within an institution.…”

Section: Introductionmentioning

confidence: 99%

The impact of engineering students' performance in the first three years on their graduation result using educational data mining

Adekitan

Salau

2019

Heliyon

122

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Research studies on educational data mining are on the increase due to the benefits obtained from the knowledge acquired from machine learning processes which help to improve decision making processes in higher institutions of learning. In this study, predictive analysis was carried out to determine the extent to which the fifth year and final Cumulative Grade Point Average (CGPA) of engineering students in a Nigerian University can be determined using the program of study, the year of entry and the Grade Point Average (GPA) for the first three years of study as inputs into a Konstanz Information Miner (KNIME) based data mining model. Six data mining algorithms were considered, and a maximum accuracy of 89.15% was achieved. The result was verified using both linear and pure quadratic regression models, and R 2 values of 0.955 and 0.957 were recorded for both cases. This creates an opportunity for identifying students that may graduate with poor results or may not graduate at all, so that early intervention may be deployed.

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Section: Introductionmentioning

confidence: 99%

The impact of engineering students' performance in the first three years on their graduation result using educational data mining

Adekitan

Salau

2019

Heliyon

122

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“…The steps included for detecting credit card fraud is represented as flowchart below in Figure 2. [14]. Attribute class is considered as target class and value 1 is a total count of 473 which is for fraud detection and value 0 is 283253 count which is for non-fraud detection [4].…”

Section: Proposed Methodologymentioning

confidence: 99%

“…Due to the lack of high-security systems, credit cards became the most common fraud issue globally [14]. Hence, the detection of credit cards is quite challenging.…”

Section: Introductionmentioning

confidence: 99%

Credit Card Fraud Detection using Imbalance Resampling Method with Feature Selection

2021

IJATCSE

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Many fraud transactions exist in the online world that affects various financial institutions but Credit Card Fraud transaction is the most occurring problem in the world. Credit Card fraud is the situation in which fraudsters misuse credit cards for illegal purposes. Hence, detection of fraudulent transactions is essen-tial. Several researchers have worked on detecting fraud transactions and also provide solutions whose surveys are given in this paper. This study makes a major contribution to research on the detection of Credit Card fraud transactions through Machine Learning Algorithms suchas Decision Tree and Naive Bayes. The data have been selected from Kag-gle and categorize into training (80%) and testing (20%) data. The whole experiment was performed on the Jupyter Notebook tool for which the Anaconda Navigator has been installed. The Heatmap is used for visualization and colorfully represents the data. The main aim of this work is to balance the dataset with Near-Miss Under-sampling Method. The information gain method is applied for feature selection. The best algorithm founded in this paper is Decision Tree with 97% accuracy as compared to Naïve Bayes with 90%. The results are achieved based on Accuracy, Recall, Precision, and F1-score. We have also shown the ROC Curve and Precision-Recall Curve of the algorithm in this paper.

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“…Recent research [35] has proposed a non-parametric novel approach with subset of relevant transactions created through data reduction. Another novel technique [36] based on Gradient Boosting Decision Tree (GBDT) and DeepWalk, achieved promising results, with F1 score ranging from 61.43% and 71.84%.…”

Section: ) Supervised Techniquesmentioning

confidence: 99%

Evaluation of Deep Neural Networks for Reduction of Credit Card Fraud Alerts

Carrasco

Sicilia-Urbán

2020

IEEE Access

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Fraud detection systems support advanced detection techniques based on complex rules, statistical modelling and machine learning. However, alerts triggered by these systems still require expert judgement to either confirm a fraud case or discard a false positive. Reducing the number of false positives that fraud analysts investigate, by automating their detection with computer-assisted techniques, can lead to significant cost efficiencies. Alert reduction has been achieved with different techniques in related fields like intrusion detection. Furthermore, deep learning has been used to accomplish this task in other fields. In our paper, a set of deep neural networks have been tested to measure their ability to detect false positives, by processing alerts triggered by a fraud detection system. The performance achieved by each neural network setting is presented and discussed. The optimal setting allowed to capture 91.79% of total fraud cases with 35.16% less alerts. Obtained alert reduction rate would entail a significant reduction in cost of human labor, because alerts classified as false positives by the neural network wouldn't require human inspection.

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CNN Data Mining Algorithm for Detecting Credit Card Fraud

Cited by 19 publications

References 6 publications

The impact of engineering students' performance in the first three years on their graduation result using educational data mining

The impact of engineering students' performance in the first three years on their graduation result using educational data mining

Credit Card Fraud Detection using Imbalance Resampling Method with Feature Selection

Evaluation of Deep Neural Networks for Reduction of Credit Card Fraud Alerts

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