scholarly journals Credit Card Fraud Detection using Machine Learning and Deployment of Model in Public Cloud as a Web Service

2020 ◽  
Vol 9 (2) ◽  
pp. 548-552
Keyword(s):  
Machine Learning ◽  
Web Service ◽  
Credit Card ◽  
Detection System ◽  
Credit Cards ◽  
Fraud Detection ◽  
Machine Learning Algorithms ◽  
Credit Card Fraud ◽  
Detection Model ◽  
The World

In recent times, usage of credit cards has increased exponentially which has given way to an increase in the number of cybercrimes related to transactions using credit cards. In this paper, the aim is to reduce the fraudulent credit card transactions happening around the world. Latest technologies like machine learning algorithms, cloud computing and web service implementation has been used in this paper. The model uses Local outlier factor algorithm and Isolation forest algorithm to develop the credit card fraud detection model using unsupervised learning techniques. The model has been implemented as a Web service to make the solution integratable with other applications and clients across the world. A third party prototype application is developed and integrated to the Fraud Detection Model using Web Services. The complete Fraud Detection System is deployed on the cloud. The Fraud Detection Model shows exceptionally high accuracy when compared to other models already existing.

scholarly journals DETECTING CREDIT CARD FRAUD USING MACHINE LEARNING ALGORITHMS

InterConf ◽  
2021 ◽  
pp. 393-403
Author(s):  
Olexander Shmatko ◽  
Volodimir Fedorchenko ◽  
Dmytro Prochukhan
Keyword(s):  
Machine Learning ◽  
Financial Services ◽  
Credit Card ◽  
Banking Sector ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Credit Cards ◽  
Internet Banking ◽  
Machine Learning Algorithms ◽  
Credit Card Fraud

Today the banking sector offers its clients many different financial services such as ATM cards, Internet banking, Debit card, and Credit card, which allows attracting a large number of new customers. This article proposes an information system for detecting credit card fraud using a machine learning algorithm. Usually, credit cards are used by the customer around the clock, so the bank's server can track all transactions using machine learning algorithms. It must find or predict fraud detection. The dataset contains characteristics for each transaction and fraudulent transactions need to be classified and detected. For these purposes, the work proposes the use of the Random Forest algorithm.

scholarly journals Credit Card Fraud Detection System

2020 ◽  
Vol 6 (12) ◽  
pp. 24-27
Author(s):  
Shashank Singh and Meenu Garg
Keyword(s):  
Machine Learning ◽  
Credit Card ◽  
Data Science ◽  
Detection System ◽  
Fraud Detection ◽  
Detection Problem ◽  
Credit Card Fraud ◽  
Local Outlier Factor ◽  
Local Outlier ◽  
Isolation Forest

It is essential that Visa organizations can distinguish false Mastercard exchanges so clients are not charged for things that they didn't buy. Such issues can be handled with Data Science and its significance, alongside Machine Learning, couldn't be more important. This undertaking expects to outline the demonstrating of an informational collection utilizing AI with Credit Card Fraud Detection. The Credit Card Fraud Detection Problem incorporates demonstrating past Visa exchanges with the information of the ones that ended up being extortion. This model is then used to perceive if another exchange is fake. Our target here is to identify 100% of the fake exchanges while limiting the off base misrepresentation arrangements. Charge card Fraud Detection is an average example of arrangement. In this cycle, we have zeroed in on examining and pre- preparing informational indexes just as the sending of numerous irregularity discovery calculations, for example, Local Outlier Factor and Isolation Forest calculation on the PCA changed Credit Card Transaction

scholarly journals Emerging Approach for Detection of Financial Frauds Using Machine Learning

2021 ◽  
pp. 9-22
Author(s):  
Upasana Mukherjee ◽  
Vandana Thakkar ◽  
Shawni Dutta ◽  
Utsab Mukherjee ◽  
Samir Kumar Bandyopadhyay
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Credit Card ◽  
Fraud Detection ◽  
Loan Default ◽  
Credit Card Fraud ◽  
Detection Model ◽  
Default Prediction ◽  
Automated Process

The growth of regularly generated data from many financial activities has significant implications for every corner of financial modelling. This study has investigated the utilization of these continuous growing data by a means of an automated process. The automated process can be developed by using Machine learning based techniques that analyze the data and gain experience from the underlying data. Different important domains of financial fields such as Credit card fraud detection, bankruptcy detection, loan default prediction, investment prediction, marketing and many more can be modelled by implementing machine learning methods. Among several machine learning based techniques, the use of parametric and non-parametric based methods are approached by this research. Two parametric models namely Logistic Regression, Gaussian Naive Bayes models and two non-parametric methods such as Random Forest, Decision Tree are implemented in this paper. All the mentioned models are developed and implemented in the field of Credit card fraud detection, bankruptcy detection, loan default prediction. In each of the aforementioned cases, the comparative study among the classification techniques is drawn and the best model is identified. The performance of each classifier on each considered domain is evaluated by various performance metrics such as accuracy, F1-score and mean squared error. In the credit card fraud detection model the decision tree classifier performs the best with an accuracy of 99.1% and, in the loan default prediction and bankruptcy detection model, the random forest classifier gives the best accuracy of  97% and 96.84% respectively.

Sign in / Sign up

Export Citation Format

Share Document