Fraud is a critical issue in our society today. Losses due to payment fraud are on the increase as ecommerce keeps evolving. Organizations, governments, and individuals have experienced huge losses due
to payment. Merchant Savvy projects that global losses due to payment fraud will increase to about $40.62
billion in 2027 . Among all payment fraud, credit card fraud results in a higher loss. Therefore, we intend
to leverage the potential of machine learning to deal with the problem of fraud in credit cards which can
be generalized to other fraud types. This paper compares the performance of logistic regression, decision
trees, random forest classifier, isolation forest, local outlier factor, and one-class support vector machines
(SVM) based on their AUC and F1-score. We applied a smote technique to handle the imbalanced nature
of the data and compared the performance of the supervised models on the oversampled data to the raw
data. From the results, the Random Forest classifier outperformed the other models with a higher AUC
score and better f1-score on both the actual and oversampled data. Oversampling the data didn't change
the result of the decision trees. One-class SVM performs better than isolation forest in terms of AUC score
but has a very low f1-score compared to isolation forest. The local outlier factor had the poorest
performance.
Credit Card Fraudulent Transaction Detection Research PaperGarvit Burad
Credit Card Fraudulent Transaction Detection Research Paper using Machine Learning technologies like Logistic Regression, Random Forrest, Feature Engineering and various techniques to deal with highly skewed dataset
Improving Credit Card Fraud Detection: Using Machine Learning to Profile and ...Melissa Moody
Researchers Navin Kasa, Andrew Dahbura, and Charishma Ravoori undertook a capstone project—part of the UVA Data Science Institute Master of Science in Data Science program—that addresses credit card fraud detection through a semi-supervised approach, in which clusters of account profiles are created and used for modeling classifiers.
Credit Card Fraudulent Transaction Detection Research PaperGarvit Burad
Credit Card Fraudulent Transaction Detection Research Paper using Machine Learning technologies like Logistic Regression, Random Forrest, Feature Engineering and various techniques to deal with highly skewed dataset
Improving Credit Card Fraud Detection: Using Machine Learning to Profile and ...Melissa Moody
Researchers Navin Kasa, Andrew Dahbura, and Charishma Ravoori undertook a capstone project—part of the UVA Data Science Institute Master of Science in Data Science program—that addresses credit card fraud detection through a semi-supervised approach, in which clusters of account profiles are created and used for modeling classifiers.
A Study on Credit Card Fraud Detection using Machine Learningijtsrd
Due to the high level of growth in each number of transactions done using credit card has led to high rise in fraudulent activities. Fraud is one of the major issues related to credit card business, since each individual do more of offline or online purchase of product via internet there is need to developed a secured approach of detecting if the credit card been used is a fraudulent transaction or not. Pattern involves in the fraud detection has to be re analyze to change from reactive approach to a proactive approach. In this paper, our objectives are to detect at least 95 of fraudulent activities using machine learning to deployed anomaly detection system such as logistic regression, k nearest neighbor and support vector machine algorithm. Ajayi Kemi Patience | Dr. Lakshmi J. V. N "A Study on Credit Card Fraud Detection using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30688.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/30688/a-study-on-credit-card-fraud-detection-using-machine-learning/ajayi-kemi-patience
This slides shares some tips on how to identify credit card fraud - brought to you by FraudLabs Pro.com
Read the full article at https://www.fraudlabspro.com/resources/tutorials/how-to-identify-credit-card-fraud/#slideshare
According to the Nilson report, the global Credit card and debit card fraud resulted in losses amounting to $24.71 billion in 2016 and 72% were bored by the Card issuers. Therefore, the card issue companies are eager to predict the fraud in real time and in advance to reduce their loss and protect their revenue. The goal of the project is to provide fraud analytics for credit card issue companies to predict fraud in real-time and in advance. By building a supervised fraud prediction model, we are aiming to capture the maximum number of real frauds while limiting the occurrence of mis-flagged frauds, in order to achieve a win-win situation both maximize our ROI and achieve customer satisfaction.
A Survey of Online Credit Card Fraud Detection using Data Mining TechniquesIJSRD
Nowadays the use of credit card has increased, because the amount of online transaction is growing. With the day to day use of credit card for payment online as well as regular purchase, case of fraud associated with it is also rising. To reduce the huge financial loss caused by frauds, a number of modern techniques have been developed for fraud detection which is based on data mining, neural network, genetic algorithm etc. Here a survey of techniques for online credit card fraud detection using Hidden Markov Model, Genetic Algorithm and Hybrid Model, and comparison between them has been shown.
Principal Component Analysis, or PCA, is a factual method that permits you to sum up the data contained in enormous information tables by methods for a littler arrangement of "synopsis files" that can be all the more handily envisioned and broke down.
India recently kicked off the Data Empowerment architecture, a framework for consented data sharing across the financial sector. This allows Nandini (Persona) to share data on her business’ regular invoices or GST payments seamlessly and securely.
Any bank or NBFC can now offer a regular stream of small-ticket working capital loans based on her demonstrated ability to repay. This is in sharp contrast to the status quo, where banks typically offer only larger loans backed by collateral. Using cash flows rather than collateral as the basis for credit is known as Flow-Based lending. Because producing collateral is a roadblock for the poorest Indians, Flow-Based lending may be their only opportunity to access the credit they sorely need for growth.
Build an Ensemble classifier that can detect credit card fraudulent
transactions.Implemented a classifier by use of machine learning algorithms, such as
Decision Trees, Logistic Regression, Artificial Neural Networks and Gradient Boosting
Classifier.
AlgoCharge offers a web-based fraud management system that assists in credit card fraud detection & prevention with Geo-based filters. The system provides various levels of fraud protection to enhance acceptance rate & reduce the risk of charge-backs.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
call for paper 2012, hard copy of journal, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal,
A Study on Credit Card Fraud Detection using Machine Learningijtsrd
Due to the high level of growth in each number of transactions done using credit card has led to high rise in fraudulent activities. Fraud is one of the major issues related to credit card business, since each individual do more of offline or online purchase of product via internet there is need to developed a secured approach of detecting if the credit card been used is a fraudulent transaction or not. Pattern involves in the fraud detection has to be re analyze to change from reactive approach to a proactive approach. In this paper, our objectives are to detect at least 95 of fraudulent activities using machine learning to deployed anomaly detection system such as logistic regression, k nearest neighbor and support vector machine algorithm. Ajayi Kemi Patience | Dr. Lakshmi J. V. N "A Study on Credit Card Fraud Detection using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30688.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/30688/a-study-on-credit-card-fraud-detection-using-machine-learning/ajayi-kemi-patience
This slides shares some tips on how to identify credit card fraud - brought to you by FraudLabs Pro.com
Read the full article at https://www.fraudlabspro.com/resources/tutorials/how-to-identify-credit-card-fraud/#slideshare
According to the Nilson report, the global Credit card and debit card fraud resulted in losses amounting to $24.71 billion in 2016 and 72% were bored by the Card issuers. Therefore, the card issue companies are eager to predict the fraud in real time and in advance to reduce their loss and protect their revenue. The goal of the project is to provide fraud analytics for credit card issue companies to predict fraud in real-time and in advance. By building a supervised fraud prediction model, we are aiming to capture the maximum number of real frauds while limiting the occurrence of mis-flagged frauds, in order to achieve a win-win situation both maximize our ROI and achieve customer satisfaction.
A Survey of Online Credit Card Fraud Detection using Data Mining TechniquesIJSRD
Nowadays the use of credit card has increased, because the amount of online transaction is growing. With the day to day use of credit card for payment online as well as regular purchase, case of fraud associated with it is also rising. To reduce the huge financial loss caused by frauds, a number of modern techniques have been developed for fraud detection which is based on data mining, neural network, genetic algorithm etc. Here a survey of techniques for online credit card fraud detection using Hidden Markov Model, Genetic Algorithm and Hybrid Model, and comparison between them has been shown.
Principal Component Analysis, or PCA, is a factual method that permits you to sum up the data contained in enormous information tables by methods for a littler arrangement of "synopsis files" that can be all the more handily envisioned and broke down.
India recently kicked off the Data Empowerment architecture, a framework for consented data sharing across the financial sector. This allows Nandini (Persona) to share data on her business’ regular invoices or GST payments seamlessly and securely.
Any bank or NBFC can now offer a regular stream of small-ticket working capital loans based on her demonstrated ability to repay. This is in sharp contrast to the status quo, where banks typically offer only larger loans backed by collateral. Using cash flows rather than collateral as the basis for credit is known as Flow-Based lending. Because producing collateral is a roadblock for the poorest Indians, Flow-Based lending may be their only opportunity to access the credit they sorely need for growth.
Build an Ensemble classifier that can detect credit card fraudulent
transactions.Implemented a classifier by use of machine learning algorithms, such as
Decision Trees, Logistic Regression, Artificial Neural Networks and Gradient Boosting
Classifier.
AlgoCharge offers a web-based fraud management system that assists in credit card fraud detection & prevention with Geo-based filters. The system provides various levels of fraud protection to enhance acceptance rate & reduce the risk of charge-backs.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
call for paper 2012, hard copy of journal, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal,
Credit card plays a very vital role in todays economy and the usage of credit cards has dramatically increased. Credit card has become one of the most common method of payment for both online and offline as well as for regular purchases of a common man. It is very necessary to distinguish fraudulent credit card transactions by the credit card organizations so their clients are not charged for the purchases that they didn’t make. Despite the fact that using credit card gives huge benefits when used responsibly carefully and however significant credit and financial damages could be caused by fraudulent activities as well. Numerous methods have been proposed to stop these fraudulent activities. The project illustrates the model of a dataset to predict fraud transactions using machine learning. The model then detects if it is a fraudulent or a genuine transaction. The model also analyses and pre processes the dataset along with deployment of multiple anomaly detection using algorithms such as Local forest outlier and Isolation forest. Nikitha Pradeep | Dr. A Rengarajan "Credit Card Fraud Detection" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd41289.pdf Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/41289/credit-card-fraud-detection/nikitha-pradeep
Billions of dollars of loss are caused every year by fraudulent credit card transactions. The design of efficient fraud detection algorithms is key for reducing these losses, and more and more algorithms rely on advanced machine learning techniques to assist fraud investigators. The design of fraud detection algorithms is however particularly challenging due to the non-stationary distribution of the data, the highly unbalanced classes distributions and the availability of few transactions labeled by fraud investigators. At the same time public data are scarcely available for confidentiality issues, leaving unanswered many questions about what is the best strategy. In this thesis we aim to provide some answers by focusing on crucial issues such as: i) why and how under sampling is useful in the presence of class imbalance (i.e. frauds are a small percentage of the transactions), ii) how to deal with unbalanced and evolving data streams (non-stationarity due to fraud evolution and change of spending behavior), iii) how to assess performances in a way which is relevant for detection and iv) how to use feedbacks provided by investigators on the fraud alerts generated. Finally, we design and assess a prototype of a Fraud Detection System able to meet real-world working conditions and that is able to integrate investigators’ feedback to generate accurate alerts.
A rule-based machine learning model for financial fraud detectionIJECEIAES
Financial fraud is a growing problem that poses a significant threat to the banking industry, the government sector, and the public. In response, financial institutions must continuously improve their fraud detection systems. Although preventative and security precautions are implemented to reduce financial fraud, criminals are constantly adapting and devising new ways to evade fraud prevention systems. The classification of transactions as legitimate or fraudulent poses a significant challenge for existing classification models due to highly imbalanced datasets. This research aims to develop rules to detect fraud transactions that do not involve any resampling technique. The effectiveness of the rule-based model (RBM) is assessed using a variety of metrics such as accuracy, specificity, precision, recall, confusion matrix, Matthew’s correlation coefficient (MCC), and receiver operating characteristic (ROC) values. The proposed rule-based model is compared to several existing machine learning models such as random forest (RF), decision tree (DT), multi-layer perceptron (MLP), k-nearest neighbor (KNN), naive Bayes (NB), and logistic regression (LR) using two benchmark datasets. The results of the experiment show that the proposed rule-based model beat the other methods, reaching accuracy and precision of 0.99 and 0.99, respectively.
The credit card has become the most popular mode of payment for both online as well as
regular purchase, in cases of fraud associated with it are also rising. Credit card frauds are increasing
day by day regardless of the various techniques developed for its detection. Fraudsters are so expert that
they generate new ways for committing fraudulent transactions each day which demands constant
innovation for its detection techniques. Most of the techniques based on Artificial Intelligence, Fuzzy
logic, neural network, logistic regression, naïve Bayesian, Machine learning, Sequence Alignment,
decision tree, Bayesian network, meta learning, Genetic Programming etc., these are evolved in
detecting various credit card fraudulent transactions. This paper presents a survey of various techniques
used in credit card fraud detection mechanisms.
Online Transaction Fraud Detection using Hidden Markov Model & Behavior AnalysisCSCJournals
Card payment are mostly preferred by many for transactions instead of cash. Due to its convenience, it is the most accepted payment method for offline as well as online purchases, irrespective of region or country the purchase is made. Currently, cards are used for everyday activities, such as online shopping, bill pays, subscriptions, etc. Consequently, there are more chances of fraudulent transactions. Online transactions are the prime target as it does not require real card, only card details are enough and can be stored digitally. The current system detects the fraud transaction after the transaction is completed. Proposed system in this paper, uses Hidden Markov Model (HMM), which is one of the statistical stochastic models used to model randomly changing systems. Using Hidden Markov Model, a fraud transaction can be detected during the time of transaction itself and after 3 attempts of verification card can blocked at the same time. Behavior Analysis (BA) helps to understand the spending habits of cardholder. Hidden Markov Model helps to acquire high-level fraud analysis with a low false alarm ratio.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
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Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Governing Equations for Fundamental Aerodynamics_Anderson2010.pdf
MACHINE LEARNING ALGORITHMS FOR CREDIT CARD FRAUD DETECTION
1. Machine Learning and Applications: An International Journal (MLAIJ) Vol.9, No.4, December 2022
DOI:10.5121/mlaij.2022.9402 17
MACHINE LEARNING ALGORITHMS FOR
CREDIT CARD FRAUD DETECTION
Amarachi Blessing Mbakwe and Sikiru Ademola Adewale
Department of Computer Science, Virginia Tech, Blacksburg, VA, USA
ABSTRACT
Fraud is a critical issue in our society today. Losses due to payment fraud are on the increase as e-
commerce keeps evolving. Organizations, governments, and individuals have experienced huge losses due
to payment. Merchant Savvy projects that global losses due to payment fraud will increase to about $40.62
billion in 2027 . Among all payment fraud, credit card fraud results in a higher loss. Therefore, we intend
to leverage the potential of machine learning to deal with the problem of fraud in credit cards which can
be generalized to other fraud types. This paper compares the performance of logistic regression, decision
trees, random forest classifier, isolation forest, local outlier factor, and one-class support vector machines
(SVM) based on their AUC and F1-score. We applied a smote technique to handle the imbalanced nature
of the data and compared the performance of the supervised models on the oversampled data to the raw
data. From the results, the Random Forest classifier outperformed the other models with a higher AUC
score and better f1-score on both the actual and oversampled data. Oversampling the data didn't change
the result of the decision trees. One-class SVM performs better than isolation forest in terms of AUC score
but has a very low f1-score compared to isolation forest. The local outlier factor had the poorest
performance.
KEYWORDS
Credit card, fraud, detection, Isolation Forest, One-class SVM, Supervised algorithms.
1. INTRODUCTION
Advancement in technology has brought about rapid innovation in payment methods for goods
and services for faster and more convenient ways of payment. We are no longer in the days when
the world solely relies on physical cash alone. Credit card is one of the means of payment. Credit
cards are small cards issued by credit companies to their approved customers as a means of
payment. It gives the cardholders the privilege of paying for goods and services and repaying the
company at a prescribed billing cycle.
Despite the ease and convenience associated with using credit cards, fraudsters increase the risk
of credit card usage. Credit card fraud involves the unauthorized use of credit cards or accounts
for transactions without the owner’s permission. Different measures have taken place to increase
the security of credit cards, but fraud in credit cards increases daily. The report from Merchant
Savvy shows that global losses due to payment fraud have increased from $9.84 billion in 2011 to
$32.39 in 2020. They projected that payment fraud will increase to about $40.62 billion in 2027
[1]. Among all the payment frauds, credit card fraud resulted in a higher loss.
Fraud can be avoided through prevention, preventing its occurrence, or through fraud detection
when the action occurs. Banks and credit companies are applying different techniques to control
payment fraud, such as monitoring the risk scores in real-time, physical biometrics (voice, facial),
rules, and machine learning behavioral biometrics studying patterns in human behavior [1].
2. Machine Learning and Applications: An International Journal (MLAIJ) Vol.9, No.4, December 2022
18
Though we have many fraud detection systems, this area still demands further contribution from
researchers as fraudsters keep devising new ways of operations, resulting in an increase in loss
due to payment fraud, especially credit card fraud. Therefore, this paper focuses on some
machine learning algorithms comparison such as decision trees, random forest classifier, logistic
regression, ensemble of Logistic regression, decision trees, random forest classifier, local outlier
factor, isolation forest, and anomaly detection algorithms.
The paper is organized as follows: Section 2 presents an overview of related works.
Subsequently, in section 3, we discuss the methodology. In section 4, we show the results.
Finally, in section 5, we present the conclusion and future direction.
2. RELATED WORK
Fraud has become a major concern to different organizations and the government as it constitutes
among the major causes of loss to organizations and government, including individuals. US
payment fraud statistics show that 77% of companies in the US said they had suffered fraud
during digital payment. 53% of customers said that they had been victims of fraud during digital
payment, and 23% of online sales ended up being fraudulent compared to 18% in 2018 [1].
Credit card fraud can be defined as a situation where a person uses a credit card belonging to
someone else for personal reasons without authorization from the owner.
Research has been ongoing in this area; different researchers have applied machine learning
algorithms for fraud detection. This paper [2] compared the performance of Random Forest,
Support vector machine, and Logistic regression in detecting credit card fraud. They used the
SMOTE sampling method to handle imbalanced class sizes. Incremental learning was used by the
authors to tackle the problem of ever-changing fraud patterns. Static and incremental learning
was performed and evaluated using AUC and Average precision. The result showed that SVM
had the poorest performance, with a slight difference in Random Forest and Logistic regression
models.
Paper [3] compared local outlier factors and isolation forests for credit card fraud detection. In
paper [4], the authors proposed an ensemble learning for credit card fraud detection. They
aggregated three feed-forward neural networks and two random forest algorithms. The authors
combined the two algorithms based on the notion that the Neural network can be more accurate
in detecting fraudulent instances while Random Forest is more accurate in detecting normal
instances. They used Binomial Logistic Regression as prior art and compared their approach with
it. They reported using oversampling (by replication) of minority class and SMOTE technique,
but they didn’t yield encouraging results on the dataset. The 3-feedforward neural networks
include L1: which consists of 3 hidden layers with 45, 68, and 102 neurons respectively, and L2
and L3 consist of 2 hidden layers of 15 and 8 neurons. The models used sigmoid activation
functions. L1 and L2 were applied to 60% of normal transactions and 60% of fraudulent
transactions. L3 was applied to 60% of fraudulent transactions and half of the normal
transactions. The two Random Forests consist of L4 built using 300 decision trees and L5 built
using 400 decision trees. The ensemble of the neural network and Random Forest yielded the best
result.
[5] compares SVM, Decision Tree, Logistic Regression, and Random Forest using Kaggle dataset
obtained from European cardholders that contain 284,786 transactions. The techniques were
evaluated using accuracy, sensitivity, specificity, and precision. Their results show that Random
3. Machine Learning and Applications: An International Journal (MLAIJ) Vol.9, No.4, December 2022
19
Forest performed best, with an accuracy score of 98.6%, compared to 97.7% for Logistic
Regression, 97.5% for SVM, and 95.5% for Decision Tree.
In the paper [6], the authors designed an analytical framework interface with Hadoop that can
read large volumes of data. It is made up of a Hadoop network for storing data from multiple
sources in HDFS. SAS is used in reading the data from Hadoop and converting it into a raw data
file before passing it to an analytical model for prediction. The authors compared three analytical
models. Random Forest performed best in terms of accuracy, precision, and recall than Logistic
Regression and Decision trees.
Paper [3] compares Isolation Forest and Local Outlier Factors using 284807 payments made by
European consumers obtained from Kaggle. Isolation Forest achieved 99.72% accuracy with a
precision of 0.28, recall of 0.29, and F1-Score of 0.28 while Local Outlier factor achieved an
accuracy score of 99.62% with a precision of 0.02, recall of 0.02, and F1-Score of 0.02. Isolation
Forest was proposed to perform better than the Local Outlier factor. The authors recorded that
Isolation Forest had an accuracy of 97% in online transactions.
The approach that this paper [3] proposes, uses the Isolation Forest and Local Outlier Factor
algorithms to detect anomalous activities, called outliers. The algorithm reached over 99.6%
accuracy; Isolation Forest precision remains only at 28% when a tenth of the data set is taken into
consideration. However, when the entire dataset is fed into the Isolation Forest algorithm, the
precision rises to 33%. Local Outlier factor had a low precision and recall. The paper mentioned
that the high percentage of accuracy was due to the huge imbalance between the number of valid
and the number of genuine transactions. The precision of the algorithms increases when the size
of the data set is increased.
In some of the papers, the authors achieved high accuracy in some of the models and based their
judgment on high accuracy. This is interesting, but due to the imbalanced nature of the data, they
might be considering high accuracy at the cost of misclassification of fraudulent transactions as
normal transactions. Some of the works have either performed oversampling by replication or
under-sampling or applied SMOTE technique to handle the imbalanced nature of the data.
In this paper, we will compare different machine learning models, Logistic Regression, Decision
trees, Random Forests, Isolation Forests, Local Outlier Factors, and One-class support vector
machines based on AUC and F1 scores.
3. METHODOLOGY
3.1. Dataset Description
The dataset is downloaded from Kaggle, a website for data science competition. The datasets
parameters include the amount, time, class (label), and 28 principal component analysis (PCA)
transformed features to protect user identities and sensitive features (v1-v28). In the dataset, there
is no missing data, no NA, no empty row. The datasets show 284807 transactions of credit card
holders. In this dataset, 492 transactions are found to be fraud and 284,315 transactions are found
to be normal transactions. There are 31 columns and 284807 rows in this dataset. The summary
statistics, exploratory analysis result, and graphical representation of the dataset are provided for
clarity. Due to the structure of the data, we realized that the dataset is unbalanced. The fraud is
0.172% of all the transactions. Twenty-eight columns are transformed using principal component
analysis (PCA), and three columns are not transformed by PCA. The three columns that are not
transformed are time, amount, and class. After careful examination of the dataset, the class is the
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response while the other 30 columns are the independent variable/features/explanatory variables.
The response variable takes the value of 1 if the transaction is fraud and 0 otherwise. Apart from
time and amount, the other variable, v1 – v28 obtained using PCA dimensionality reduction to
secure the cardholder's information.
Figure 1. Distribution of the Class Labels
Figure 2. Amount of all transactions against Time
Figure 3. Fraudulent Amount transactions against Time
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3.2. Models
We explored six machine learning models which include: three supervised models and three
unsupervised models. The machine models were selected based on their explainability attribute
and performance in several domains.
Logistic Regression is a supervised learning model that is used for classification problems. The
goal is to estimate the posterior probability using continuous function and predict using discrete
categorical value. Logistic regression (LR) is an arithmetic technique like linear regression since
LR finds an equation that predicts the result of a twofold variable, Y, from one or more response
variables, X. Nevertheless, unlike linear regression the response variables can be categorical or
continuous, as the model does not firmly require continuous data. To predict group association,
LR uses the log odds ratio rather than probabilities and an iterative maximum likelihood
technique rather than least squares to fit the final model. This means the researcher has more
freedom when using LR and the method may be more appropriate for non-normally distributed
data or when the samples have unequal covariance matrices which is one of the assumptions for
running regression analysis [7]. Logistic regression assumes independence among variables,
which is not always met in some datasets. However, as is often the case, the applicability of the
method (and how well it works, e.g., the classification error) often trumps statistical assumptions.
One demerit of LR is that the technique cannot produce typicality probabilities (useful for
forensic casework), but these values may be substituted with nonparametric methods such as
ranked probabilities and ranked between similarity measures [8].
Decision tree is another supervised learning method that is used for classification. Decision trees
(DTs) are a way to vividly establish a chronological decision process. A decision tree contains
decision nodes, each with branches for each of the alternative decisions [9]. Unintended nodes
(random variables) also appear in the tree, with the efficacy of each branch computed at the leaf
of each branch. The expected efficacy of any decision can then be calculated based on the
weighted addition of all branches from the decision to all leaves from that branch.
Types of decision trees are based on the type of response variable we have [10]. There are two
types: Decision Tree which has a categorical response variable then it is referred to as a
categorical variable decision tree and decision tree that has a continuous response variable is
referred to as a continuous variable decision tree.
Random Forest is an ensemble method to discover the decision tree that best fits the training data
by creating many decision trees and then determining the "average" one. The "random" part of
the term refers to building each of the decision trees from a random selection of features; the
"forest" refers to the set of decision trees. Random forest is another supervised learning
algorithm. The "forest" built is an ensemble of decision trees, normally trained with the
“bagging” method [9].
The general idea of the bagging method is that a combination of learning models increases the
overall result. Most relevant merit of random forest is that it can be used for both classification
and regression problems, which form most current machine learning systems. Random forest is
also a very accessible algorithm because the default hyperparameters it uses often yield a good
prediction result. Understanding the hyperparameters is forthright, and there are also not that
many of them.
The main demerit of random forest is that a great number of trees can make the algorithm too
slow and unproductive for real-time predictions. In general, these algorithms are fast to train, but
slow to create predictions once they are trained. A more accurate prediction requires more trees,
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which results in a slower model. In most practical applications, the random forest algorithm is
fast enough but there can certainly be situations where run-time performance is important and
other approaches would be preferred.
Local Outlier Factor (LOF) is an unsupervised learning technique for spotting outliers which
calculates the local density deviation of a given data value apropos its neighbors. It considers as
outliers the subsets that have a significantly lesser density than their neighbors. The LOF of a
point expresses the density of this point related to the density of its neighbors. If the density of a
data value is considerably lesser than the densities of its neighbors (LOF ≫1), the data value is
far from thick areas and, hence, an outlier. The result of our LOF is discussed in the next section.
Isolation Forest is another outlier detection algorithm in machine learning. Isolation forest was
first introduced by [11]. They took advantage of two quantitative properties of anomalous data
points in a sample: Minor - they are the minority consisting of fewer instances; and Dissimilar -
they have attribute values that are very dissimilar from those of normal instances. Since
anomalies are "minor and dissimilar", they are easier to “isolate” compared to normal points.
Isolation Forest builds an ensemble of “Isolation Trees” (iTrees) for the data set, and anomalies
are the values that have lesser average path lengths on the iTrees [11]. This is an unsupervised
algorithm that learns a decision function for novelty detection: classifying new data as similar or
different to the training set. The algorithm is based on the assumption that fewer instances of
anomalies result in shorter path length [12]. This suggests that data instances that have shorter
path length are most likely to be anomalies. The necessary input parameters for building the
Isolation Forest algorithm: are the subsampling size, the number of trees and the height of the tree
[12].The subsampling size was suggested to be smaller for the machine learning algorithm to
function faster and yield a better detection result [13].
One Class Support Vector Machine is an unsupervised algorithm that learns a decision function
for abnormal observation detection: classifying new data as similar or different to the training set.
It has different kernels which include radial basis, polynomial, linear and sigmoid.
4. RESULT AND EVALUATION
In this section, we will compare supervised models; Logistic regression, decision trees, and
random forest classifier models, including unsupervised models; Isolation Forest, Local outlier
factor, and One-class Support vector machine models. We conducted several experiments on the
credit card data set.
4.1. Dataset Processing
The dataset was splitted into training and test sets in the ratio of 70:30 respectively. The choice
for the ratio division is based on empirical studies the best results are obtained if we use 20-30%
of the data for testing, and the remaining 70-80% of the data for training. The models were built,
and the performance was evaluated using 10-Fold cross validation to ensure the models were not
overfitting on the training data set. Then, the model performance on unseen data was tested using
the test data.
The supervised models were first implemented on the imbalanced data set. Afterward, to handle
the imbalanced nature of the data, the data was oversampled using Smote (Synthetic Minority
Over-sampling) technique. This works by generating synthetic samples.
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4.2. Evaluation Metrics
Most machine learning algorithms are evaluated using predictive accuracy, but this is not
appropriate in fraud detection because they are mostly imbalanced. In terms of imbalanced data,
we mean that the proportion of data points in each class are not approximately equal. In fraud
detection, we are very much interested in correctly predicting the minority class (fraudulent
class).
The evaluation metrics adopted in this paper for evaluation and comparison of our models are
standard area under the ROC curve (AUC), ROC and F1-score. The Receiver Operating
Characteristic (ROC) curve was proposed as a standard technique for evaluating the performance
of classifiers over a range of trade-offs between true positive and false positive error rates
[14,15]. The ROC curve accepted performance metric is Area Under the Curve [15,16].
4.3. Results
Testing the predictive performance on the test data which constitutes 30 percent of the entire
data, the result of each model is given in table 1.
Table 1. Results of AUC and F1-Score Comparison Among Models.
Models Dataset AUC FI-Score for
Fraudulent Class
F1-Score for
Normal Class
Logistic Regression Original data 0.978 0.74 1.00
Decision trees Original data 0.897 0.74 1.00
Random Forest Classifier Original data 0.962 0.86 1.00
Logistic Regression Oversampled data 0.981 0.10 0.99
Decision trees Oversampled data 0.897 0.74 1.00
Random Forest Classifier Oversampled data 0.987 0.87 1.00
Isolation Forest Original data 0.626 0.25 1.00
Local Outlier Factor Original data 0.499 0.00 1.00
One-Class SVM Original data 0.721 0.07 0.99
Analysing the obtained results, we can see that the supervised algorithms achieved a high AUC
score and good f1-score. Random forest classifier has the highest AUC score and f1-score. The
result obtained by the random forest classifier on the oversampled data shows that oversampling
can help correct imbalance data. Oversampling can help improve model performance, avoid
overfitting, and reduce computational time. Local outlier factor has the lowest performance.
4.4. Model Comparison
The models are compared using ROC curve, AUC, and F1-score. The ROC curve helps to
visualize the performance of the model based on AUC. Random forest classifier achieves the
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highest AUC and f1 score, followed by Logistic regression while local outlier factor has the
poorest performance.
Figure 4. Model Comparison using ROC curve
5. CONCLUSION AND FUTURE WORK
Credit card fraud is an important problem that calls for efficient solutions. Having an efficient
solution will drastically reduce the loss incurred by the government, companies, and individuals.
In this paper, we compared both supervised and unsupervised models. Random Forest classifier
outperformed all the other models based on the AUC, followed by logistic regression, decision
trees, one-class support vector machine, isolation forest and local Outlier factor.
Oversampling the data improved the performance of the random forest classifier on unseen data.
Also, we can see here that the supervised algorithms performed better than the unsupervised
algorithms, which shows that supervised algorithms can be used in fraud detection with the
advantage of less computational time and ease of interpretation.
For a better performance of predicting the fraudulent transactions, a better well-informed data
might improve the models generally. The data used in this paper is a bit challenging because
some information has been transformed due to confidentiality of customer’s information. For
future research, we will try to obtain well-informed data or use synthetic data that imitates real-
life transactions. Also, for future work, we will try a context-aware learning approach following
the idea in [17].
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AUTHORS
Amarachi Blessing Mbakwe:
I am a third-year Ph.D. student in the department of Computer Science at Virginia Tech
and a member of the PLAN lab. My research interests lie at the intersection of artificial
intelligence (AI), natural language processing (NLP), machine learning, and
multimodal learning. My previous work introduced an anatomy-aware model for
tracking longitudinal relationships between images.
Sikiru Ademola Adewale:
I am a third year Ph.D. student of computer science and applications at Virginia Tech.
Most of my research work is in the area of machine learning & applications, artificial
intelligence, and computer vision. As a member of the Perception + LANguage
(PLAN) Lab, I am currently working on scene graphs.