Predictive Modelling For Credit Card Fraud Detection Using Data Analytics

Patil, Suraj; Nemade, Varsha; Soni, Piyush Kumar

doi:10.1016/j.procs.2018.05.199

Cited by 93 publications

(29 citation statements)

References 6 publications

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“…A comparative analysis of algorithms used mostly for credit card fraud detection was conducted in the work by Ref. [16] that involves logistic regression, decision tree, and random forest. Publicly available dataset of German credit data served for evaluation among the algorithms.…”

Section: Related Workmentioning

confidence: 99%

Credit Card Fraud Detection Using Fuzzy Rough Nearest Neighbor and Sequential Minimal Optimization with Logistic Regression

Hussein

Khairy

Najeeb

et al. 2021

Int. J. Interact. Mob. Technol.

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<p>The global online communication channel made possible with the internet has increased credit card fraud leading to huge loss of monetary fund in their billions annually for consumers and financial institutions. The fraudsters constantly devise new strategy to perpetrate illegal transactions. As such, innovative detection systems in combating fraud are imperative to curb these losses. This paper presents the combination of multiple classifiers through stacking ensemble technique for credit card fraud detection. The fuzzy-rough nearest neighbor (FRNN) and sequential minimal optimization (SMO) are employed as base classifiers. Their combined prediction becomes data input for the meta-classifier, which is logistic regression (LR) resulting in a final predictive outcome for improved detection. Simulation results compared with seven other algorithms affirms that ensemble model can adequately detect credit card fraud with detection rates of 84.90% and 76.30%.</p>

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Section: Related Workmentioning

confidence: 99%

Credit Card Fraud Detection Using Fuzzy Rough Nearest Neighbor and Sequential Minimal Optimization with Logistic Regression

Hussein

Khairy

Najeeb

et al. 2021

Int. J. Interact. Mob. Technol.

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“…Many other studies [9][10][11] reviewed and compared the existing financial fraud detection models to identify the method with the best performance. Patil et al in [10] used the confusion matrix and found that, the Random Forest model performed better as compared to Logistic Regression and Decision Tree in terms of accuracy, precision and recall parameters, whereas, Albashrawi in [11] found that the Logistic Regression model appeared to be the leading machine learning technique in detecting financial fraud. Other researchers [12][13] have proposed using a hybrid approach.…”

Section: Relared Workmentioning

confidence: 99%

A SMOTe based Oversampling Data-Point Approach to Solving the Credit Card Data Imbalance Problem in Financial Fraud Detection

Mqadi¹,

Naicker²,

Adeliyi³

2021

IJCDS

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Credit card fraud has negatively affected the market economic order, broken the confidence and interest of stakeholders, financial institutions, and consumers. Losses from card fraud is increasing every year with billions of dollars being lost. Machine Learning methods use large volumes of data as examples for learning to improve the performance of classification models. Financial institutions use Machine Learning to identify fraudulent patterns from the large amounts of historical financial records. However, the detection of credit card fraud remains as a significant challenge for business intelligence technologies as most datasets containing credit card transactions are highly imbalanced. To overcome this challenge, this paper proposed the use of the data-point approach in machine learning. An experimental study was conducted applying Oversampling with SMOTe, a data-point approach technique, on an imbalanced credit card dataset. State-of-the-art classical machine learning algorithms namely, Support Vector Machines, Logistic Regression, Decision Tree and Random Forest classifiers were used to perform the classifications and the accuracy was evaluated using precision, recall, F1-score, and the average precision metrics. The results show that if the data is highly imbalanced, the model struggles to detect fraudulent transactions. After using the SMOTe based Oversampling technique, there was a significant improvement to the ability to predict positive classes.

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“…The raw data is transferred to the analytical model in order to build the data model. That cycle helps the system learn the model by itself in a scalable and realtime manner [29].…”

Section: Real Time Working Problemsmentioning

confidence: 99%

Approaches to Fraud Detection on Credit Card Transactions using Artificial Intelligence Methods

Yazıcı¹

2020

Computer Science &Amp; Information Technology

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Credit card fraud is an ongoing problem for almost all industries in the world, and it raises millions of dollars to the global economy each year. Therefore, there is a number of research either completed or proceeding in order to detect these kinds of frauds in the industry. These researches generally use rule-based or novel artificial intelligence approaches to find eligible solutions. The ultimate goal of this paper is to summarize state-of-the-art approaches to fraud detection using artificial intelligence and machine learning techniques. While summarizing, we will categorize the common problems such as imbalanced dataset, real time working scenarios, and feature engineering challenges that almost all research works encounter, and identify general approaches to solve them. The imbalanced dataset problem occurs because the number of legitimate transactions is much higher than the fraudulent ones whereas applying the right feature engineering is substantial as the features obtained from the industries are limited, and applying feature engineering methods and reforming the dataset is crucial. Also, adapting the detection system to real time scenarios is a challenge since the number of credit card transactions in a limited time period is very high. In addition, we will discuss how evaluation metrics and machine learning methods differentiate among each research.

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Predictive Modelling For Credit Card Fraud Detection Using Data Analytics

Cited by 93 publications

References 6 publications

Credit Card Fraud Detection Using Fuzzy Rough Nearest Neighbor and Sequential Minimal Optimization with Logistic Regression

Credit Card Fraud Detection Using Fuzzy Rough Nearest Neighbor and Sequential Minimal Optimization with Logistic Regression

A SMOTe based Oversampling Data-Point Approach to Solving the Credit Card Data Imbalance Problem in Financial Fraud Detection

Approaches to Fraud Detection on Credit Card Transactions using Artificial Intelligence Methods

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