scholarly journals Artificial Intelligence based Credit Card Fraud Identification using Fusion Method

2019 ◽  
Vol 8 (4) ◽  
pp. 4876-4878
Keyword(s):  
Artificial Intelligence ◽  
Credit Card ◽  
Credit Card Fraud ◽  
Online Transactions ◽  
Detection Algorithms ◽  
Improve Accuracy ◽  
Multiple Detection ◽  
Low Probability ◽  
Verification Process ◽  
Design Ideas

Increase of online transactions has given a greater scope for increasing of credit card frauds. In this work we develop a general framework with Artificial Intelligence based Hadoop. Also that fuses multiple detection algorithms to improve accuracy, reliability. Further to support large amount of transactions storage. The workflow satisfies the design ideas of current credit card fraud identification systems. The verification process for all the transactions is implemented. If incoming transaction that passed through trained model with low probability then it is rejected.

scholarly journals Prospects and challenges in the investigation of credit card fraud in Vall Region of the Gauteng Province, South Africa

2022 ◽  
Vol 27 ◽  
pp. 959-967
Author(s):  
Morero Motseki
Keyword(s):  
Credit Card ◽  
Justice System ◽  
Phenomenological Approach ◽  
Research Approach ◽  
Sampling Techniques ◽  
Snowball Sampling ◽  
Gauteng Province ◽  
Credit Card Fraud ◽  
Online Transactions ◽  
And Training

Today use of Credit Card even in developing countries has become a common scenario. People use it to shop, pay bills and for online transactions. But with increase in number of Credit Card users, the cases of fraud in Credit Card have also been on rise. Credit Card related frauds cause globally a loss of billions of Rands. Credit Card fraud can be done in numerous ways. The article begins with an examination of the extent of the challenge and response by the relevant stakeholders, especially the Criminal Justice System (CJS). This study was carried out utilising a qualitative research approach with a convenience, purposive and snowball sampling techniques. Thirtynine (39) interviews were conducted to solicit the views of the participants and police investigators from Vanderbijlpark, Sebokeng, Sharpeville and Vereeniging police stations, members of the community, and victims of credit card fraud were interviewed. These interviews were analysed according to the phenomenological approach, aided with the inductive Thematic Content Analysis (TCA) to identify the participants’ responses and themes. The findings indicated that the extent of credit card fraud in Vaal region is reaching alarming rates. Based on the findings, the authors provided recommendations such as: police investigators being taken for regular workshops and training on how to investigate sophisticated methods used by perpetrators such as technology, awareness in the society about credit card fraud should be prioritised and enhanced. This study recommends that the CCTV cameras should be installed in the ATM, where cases of credit card are taking place. In addition, the police be visible in the areas which are most prevalent to credit card fraud.

scholarly journals Credit Card Fraud Detection using Deep Learning based on Neural Network and Auto-encoder

2020 ◽  
Vol 9 (5) ◽  
pp. 1140-1143
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Credit Card ◽  
Online Media ◽  
Credit Card Fraud ◽  
Online Transactions ◽  
The Past ◽  
Past Behavior ◽  
The Neural Network ◽  
The Comparative Study

Credit card fraud is an event problem and fraud detecting techniques getting more sophisticated each day. Mainly internet is becoming more common in almost every domain. Online transactions, shopping, and e-commerce are expanding step by step. Due to which in the online payment system, fraudulent activities have also increased. It has cost banks and their customers a loss of billions of rupees. The techniques used now a day detects the anomaly only after the fraud transaction takes place. The intruders have found ways to crack the system loopholes and defeat the security. These frauds are not consistent in their actions, they constantly alter. Thus, Artificial Intelligent (AI) algorithms are used to detect the behavior of such activity by learning the past behavior of the transaction of the users. An unsupervised algorithm is used to detect online transactions, as fraudsters commit fraud once by online media and then move on to other techniques. This paper discusses the performance analysis and the comparative study of the two Deep Learning algorithms which include auto-encoder and the neural network. In this paper accuracy, precision, recall, and AUC curve are considered as a model evaluation factor.

scholarly journals Anomaly Detection Algorithms in Financial Data

2021 ◽  
Vol 10 (5) ◽  
pp. 76-78
Author(s):  
Abhisu Jain ◽  
Mayank Arora ◽  
Anoushka Mehra ◽  
Aviva Munshi
Keyword(s):  
Random Forest ◽  
Anomaly Detection ◽  
Credit Card ◽  
Optimal Solution ◽  
Financial Data ◽  
Credit Card Fraud ◽  
Detection Algorithms ◽  
Isolation Forest

The main aim of this project is to understand and apply the separate approach to classify fraudulent transactions in a database using the Isolation forest algorithm and LOF algorithm instead of the generic Random Forest approach. The model will be able to identify transactions with greater accuracy and we will work towards a more optimal solution by comparing both approaches. The problem of detecting credit card fraud involves modelling past credit card purchases with the perception of those that turned out to be fraud. Then, this model is used to determine whether or not a new transaction is fraudulent. The objective of the project here is to identify 100% of the fraudulent transactions while mitigating the incorrect classifications offraud.

scholarly journals Multifactor Authentication over Credit Card Fraud Detection and Prevention

2020 ◽  
Vol 9 (9) ◽  
pp. 680-685
Keyword(s):  
Face Recognition ◽  
Credit Card ◽  
Fraud Detection ◽  
Online Banking ◽  
Scanning System ◽  
User Friendliness ◽  
Credit Card Fraud ◽  
Online Transactions ◽  
Multifactor Authentication ◽  
Online Methods

Online banking becomes most used method for banking transaction now days. Now the trend is turning towards digitization and so is the population going towards the same thing. People often go to the credit/debit card, Net Banking, etc. online methods. Confidentiality may be hacked during online transactions. To reduced, fraud online activities so, as to secure the data by a two-step authentication method. The primary step of authentication is to verifying OTP. Once the OTP is verified, face recognition will be done. The data is analyzed and the results for both the valid and invalid transactions are sent to the Bank. A new card scanning system has important factor such as most safety, user-friendliness, etc. The application's importance is to mitigate credit card fraud through Face device awareness. The customers get both most usable and highly secure online banking application.

scholarly journals Analysis of Credit Card Fraud detection using Machine Learning models on balanced and imbalanced datasets

2021 ◽  
Vol 9 (7) ◽  
pp. 846-852
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Predictive Models ◽  
Credit Card ◽  
Fraud Detection ◽  
Classification Algorithms ◽  
Learning Models ◽  
Credit Card Fraud ◽  
Online Transactions ◽  
Machine Learning Models

With the advent of modern transaction technology, many are using online transactions to transfer money from one person to another. Credit Card Fraud, a rising problem in the financial department goes unnoticed most of the time. A lot of research is going on in this area.The Credit Card Fraud Detection project is developed to spot whether a new transaction is fraudulent or not with the knowledge of previousdata. We use various predictive models to ascertain how accurate they are in predicting whether a transaction is abnormalor regular. Techniques like Decision Tree, Logistic Regression, SVMand Naïve Bayes are the classification algorithms to detect non-fraud and fraud transactions.

scholarly journals Credit Card Fraud Detection using Machine Learning and Data Science

2021 ◽  
Vol 9 (VII) ◽  
pp. 3788-3792
Author(s):  
Aman .
Keyword(s):  
Machine Learning ◽  
Credit Card ◽  
Data Science ◽  
Fraud Detection ◽  
Data Sets ◽  
Data Set ◽  
Credit Card Fraud ◽  
Detection Algorithms ◽  
Processing Data ◽  
Isolation Forest

It is important that companies are able to identify fraudulent credit card transactions so that customers are not charged for items that they did not purchase. These problems can be handled with Data Science and its importance, along with Machine Learning. This project aim is to illustrate the modelling of a data set using machine learning with Credit Card. Our objective is to detect 100% of the fraudulent transactions while minimizing the incorrect fraud classifications. Credit Card Fraud Detection is a sample of classification. In this process, we have focused on analysing and pre-processing data sets as well as the deployment of multiple anomaly detection algorithms such as Local Outlier Factor and Isolation Forest algorithm on the PCA transformed Credit Card Transaction data.

Literature Study –Data Mining Techniques on Detecting Fradulent Activities in Credit Card

2017 ◽  
Vol 6 (10) ◽  
pp. 60
Author(s):  
S. K. Saravanan ◽  
G. N. K. Suresh Babu
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Data Transfer ◽  
Study Data ◽  
Time Requirement ◽  
Literature Study ◽  
Data Mining Techniques ◽  
Online Transactions ◽  
Secured Data ◽  
Credit Card Usage

In contemporary days the more secured data transfer occurs almost through internet. At same duration the risk also augments in secure data transfer. Having the rise and also light progressiveness in e – commerce, the usage of credit card (CC) online transactions has been also dramatically augmenting. The CC (credit card) usage for a safety balance transfer has been a time requirement. Credit-card fraud finding is the most significant thing like fraudsters that are augmenting every day. The intention of this survey has been assaying regarding the issues associated with credit card deception behavior utilizing data-mining methodologies. Data mining has been a clear procedure which takes data like input and also proffers throughput in the models forms or patterns forms. This investigation is very beneficial for any credit card supplier for choosing a suitable solution for their issue and for the researchers for having a comprehensive assessment of the literature in this field.

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