scholarly journals Fraud Detection of Credit Card using Data Mining Techniques

2019 ◽  
Vol 8 (12) ◽  
pp. 4410-4413
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Genetic Algorithm ◽  
Credit Card ◽  
Hidden Markov ◽  
Fraud Detection ◽  
Network Clustering ◽  
Transaction Data ◽  
Using Data ◽  
The Look

The handling of credit card for online and systematic purchase is booming and scam associated with it. An industry of fraud detection where cumulative rise can have huge perk for banks and client. Numerous stylish techniques like data mining, genetic programming, neural network etc. are used in identify fraudulent transaction. In online transaction, Data mining acquire indispensable aspect in discovery of credit card counterfeit. This paper uses gradient boosted trees, neural network, clustering technique and genetic algorithm and hidden markov model for achieving upshot of the fraudulent transaction. These all model are emerging in identifying various credit card fraudulent detection. The indispensable aims to expose the fraudulent transaction and to corroborate test data for further use. This paper presents the look over techniques and pinpoint the top fraud cases.

scholarly journals Detect Frauds in Credit Card using Data Mining Techniques

2019 ◽  
Vol 9 (2) ◽  
pp. 4891-4895
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Nearest Neighbor ◽  
Hidden Markov ◽  
K Nearest Neighbor ◽  
Detection Systems ◽  
Important Concern ◽  
On Line ◽  
Using Data ◽  
The Individual

In today era credit card are extensively used for day to day business as well as other transactions. Ascent within the variety of transactions through master card has junction rectifier to rise in the dishonest activities. In trendy day's fraud is one in every of the most important concern within the monetary loses not solely to the merchants however additionally to the individual purchasers. Data processing had competed a commanding role within the detection of credit card in on-line group action. Our aim is to first of all establish the categories of the fraud secondly, the techniques like K-nearest neighbor, Hidden Markov model, SVM, logistic regression, decision tree and neural network. So fraud detection systems became essential for the banks to attenuate their loses. In this paper we have research about the various detecting techniques to identify and detect the fraud through varied techniques of data mining

scholarly journals An Unique Methodology for Credit Card Fraud Detection based on Convolutional Neural Network

2021 ◽  
Vol 36 (1) ◽  
pp. 277-280
Author(s):  
S. Ravi ◽  
J. Thanga Kumar ◽  
Dr. Linda Joseph ◽  
Sumanth Raju Kunjeti ◽  
Nandu Vardhan Saniboina ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Learning Strategies ◽  
Credit Card ◽  
Fraud Detection ◽  
Web Based ◽  
Transaction Data ◽  
Itemset Mining ◽  
Identification Strategy

Internet based business, e-Services and numerous other web-based application have expanded the online payment modes, expanding the danger for online frauds. Expansion in fraud rates, analysts began utilizing distinctive machine learning strategies to identify and dissect frauds in online exchanges. The principle point of the paper is to plan and build up a novel fraud identification strategy for Streaming Transaction Data, with a target, to dissect the previous exchange subtleties of the clients and concentrate the personal conduct standards. This paper proposes a canny model for detecting fraud in credit card exchange datasets that are unusually imbalanced and enigmatic. The class irregularity issue is dealt with by finding lawful just as fraud exchange designs for every client by utilizing continuous itemset mining.

scholarly journals Customer Churn Prediction in Telecommunication Industry Using Data Mining Methods

2020 ◽  
Vol 8 (1) ◽  
pp. 1-8
Author(s):  
Homa Meghyasi ◽  
Abas Rad
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Genetic Algorithm ◽  
Churn Prediction ◽  
Modular Neural Network ◽  
Customer Churn ◽  
Important Challenge ◽  
Accuracy Result ◽  
Customer Churn Prediction ◽  
Using Data

At present, in competitive space between companies and organizations, customers churn is their most important challenge. When a customer becomes churn, organizations lose one of their most important assets, which can lead to financial losses and even bankruptcy.  Customer churn prediction using data mining techniques can alleviate these problems to some extent.  The aim of the present study is to provide a hybrid method based on Genetic Algorithm and Modular Neural Network to customer churn prediction in telecommunication industries and use Irancell data as a sample. The accuracy result of this study which is 95.5% get the highest accuracy rank in comparisons with the result of other methods, which shows using modular neural network with two modules of feedforward neural network and also using genetic algorithm to obtain optimal structure for modules of the neural network are the most important indicators of this method to each the highest accuracy result among the rest of methods.

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