Credit Card Fraud Detection System

2021

Using new spam technologies to carry out internet banking fraud refers to shifting and withdrawing money from the user’s balance account without it’s authorization. Credit card fraud pops into the mind so far in the current scenario when the concept of fraud bursts into some conversation. Credit card fraud has escalated tremendously in recent times due to the incredible growth in credit card purchases. In order to assess, identify or prevent undesirable conduct, fraud detection requires tracking the purchase behavior of users/customers. The purpose of this project is to predict the genuine and fraud transactions with respect to the amount of the transaction utilizing various machine learning approaches like Logistic Regression, Decision Trees, Support Vector Machine, Naive Bayes, Random Forest and K-Nearest Neighbor. The model built who has greater accuracy and precision is considered to be best fit for this system.

International journal of computer applications, 2022

As the world is becoming more digitalized with every sector using the internet to flourish their businesses, online transactions have become an inevitable part of life. There has been a steady rise in the number of online transactions and this will continue to increase in the future as well. One of the major modes of online transactions is credit cards and along with its extensive use comes its major drawback, that is, credit card fraud. Machine learning plays a vital role in detecting credit card frauds as it is not possible for banks to monitor every transaction. This paper explores different machine learning algorithms used to detect credit card frauds.

The use of credit cards has increased, due to a rapid growth in the E-commerce technology. Credit card is the most popular mode of payment, but number of fraud cases associated with it is also rising. Nowadays credit card fraud is a major concern. We have heard of huge amounts of cases where there were credit card frauds and many of the companies had lost fortune and lots of capital from recovering from Credit Card Fraud. Due to huge amount of data, analysing fraud transaction manually is impractical. However, it is possible using machine learning, through informative features. In this project we have worked on modelling of dataset using machine learning for credit card fraud detection. It includes modelling past credit card transactions with the data of the ones that are fraud. In this, the new transaction is fraud or not is checked by this model. In this model, it is expected that using Machine Learning algorithms to perform analysis and find out the number of fraudulent transactions that have taken place by using the given transaction data. The Machine learning algorithm used are Logistic Regression, Decision Tree Model, Artificial Neural Network, and Gradient Boosting Algorithm.

Innovations in Bio-Inspired Computing and Applications: Proceedings of the 11th International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA 2020) held during December 16-18, 2020, 2021

Credit card is a critical element in digital banking. As modern technology is evolving day by day, credit card fraud is increasing significantly. Many financial companies lose billions of dollars annually because of credit card fraud. Criminals constantly try to discover various rules to require illegal actions. As a result, fraud detection methods are crucial for non-banking and banking financial institutions for minimizing the losses. This paper mainly discusses the performance of random forest (RF), AdaBoost, CatBoost classifiers for classification of credit card fraudulent activities. In this paper, the most important features are also selected by applying these classifiers. Results show that among the classifiers considered, RF and CatBoost have the best performance obtaining 99.92% accuracy in detecting credit card frauds.

2020

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...

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2019

Cashless transactions such as online transactions, credit card transactions, and mobile wallet are becoming more popular in financial transactions nowadays. With increased number of such cashless transaction, number of fraudulent transactions is also increasing. Fraud can be distinguished by analyzing spending behavior of customers (users) from previous transaction data. Credit card fraud has highly imbalanced publicly available datasets. In this paper, we apply many supervised machine learning algorithms to detect credit card fraudulent transactions using a real-world dataset. Furthermore, we employ these algorithms to implement a super classifier using ensemble learning methods. We identify the most important variables that may lead to higher accuracy in credit card fraudulent transaction detection. Additionally, we compare and discuss the performance of various supervised machine learning algorithms that exist in literature against the super classifier that we implemented in this paper.

2021

Online Transaction is a popular mode of payment and most of the payments done by using credit card but credit card fraud happens continuously and it leads to huge lose. So all the banks and other financial industries supports to the progress of Fraud identification of credit card. The fraudulent transactions can takes place in different ways, identification of credit card fraud is very important to the credit card companies so that customers will not be charged for the items those are not purchased. This paper explained the concepts related to credit card and used different machine learning algorithms like logistic regression and random forest method .2,84,808 credit card transactions of dataset is collected from kaggle and these transactions are from European bank. Identification of credit card is an example of classification and in this process includes analyzing and preprocessing the data set along with the deployment of Machine learning algorithms. Index Terms Credit card fraud,...

2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 2019

Credit card fraud events take place frequently and then result in huge financial losses. The number of online transactions has grown in large quantities and online credit card transactions hold a huge share of these transactions. Online transactions have become an important and necessary part of our lives. As frequency of transactions is increasing, number of fraudulent transactions is also increasing rapidly. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. Implementation of efficient fraud detection systems has thus become imperative for all credit card issuing banks to minimize their losses. Many modern techniques based on Artificial Intelligence, Data mining, Fuzzy logic, Machine learning, Sequence Alignment, Genetic Programming etc., has evolved in detecting various credit card fraudulent transactions. A clear understanding on all these approaches will certainly lead to an efficient credit card fraud detection system. The most commonly used fraud detection methods are Neural Network (NN), rule-induction techniques, fuzzy system, decision trees, Support Vector Machines (SVM), Artificial Immune System (AIS), genetic algorithms, K-Nearest Neighbor algorithms. These techniques can be used alone or in collaboration using ensemble or meta-learning techniques to build classifiers. This thesis presents a survey of various techniques used in credit card fraud detection and evaluates each methodology based on certain design criteria.

international journal for research in applied science and engineering technology ijraset, 2020

A major problem which is affecting growth in financial services is "CREDIT CARD FRAUD". Many organizations lost their amount due to these frauds. Even though many research studies are made on fraud detection, they lack on analyzing data extracted from actual transaction, due to privacy issues. Here, certain machine learning algorithms are used to detect fraudulent transactions. First, the Standard methods are applied. Then, in combination hybrid methods such as AdaBoost and majority voting are applied. A publicly available datasets are used so that model efficacy can be evaluated. Later, a real-world credit card data set is analyzed which is taken from certain financial institution. Further, to estimate the robustness of the algorithm, noise is added to the samples. By comparing various machine learning algorithms, the main aim is to find the best in those to detect the fraudulent transactions to avoid credit card fraud. The experimental results indicate that the hybrid methods such as majority voting efficiently provides nearly best accuracy for detecting fraudulent transactions of Credit cards.

IRJET, 2020

The speedy growth in E-Commerce industry has led to an aggressive increase in the use of credit cards for online purchases. In recent years, credit card fraud has become a major complication for banks as it has become very difficult for detecting fraud in the credit card system. To overcome this obstacle Machine learning plays an important role in detecting the credit card fraud in the transactions. As in Machine learning the machine is trained and it predicts the output so, to predict the various bank transactions various machine learning algorithms are used. This paper examines the performance of K-nearest neighbors, Decision Tree, Logistic regression and Random forest for credit card fraud detection. The dataset considers fraud transactions as "Class 1" and valid ones as the "Class 0". The data set is highly imbalanced, it has about 0.173% of fraud transactions and the rest are valid transactions. Four different machine learning classification algorithms used here and the task is implemented in Python language. The performance of the algorithm is evaluated by accuracy score, f1-score, precision and recall score.