Credit card fraud detection using artificial neural network
ABSTARCT :
Frauds in credit card transactions are common today as most of us are using the credit card payment methods more frequently. This is due to the advancement of Technology and increase in online transaction resulting in frauds causing huge financial loss.
Therefore, there is need for effective methods to reduce the loss. In addition, fraudsters find ways to steal the credit card information of the user by sending fake SMS and calls, also through masquerading attack, phishing attack and so on.
This paper aims in using the multiple algorithms of Machine learning such as support vector machine (SVM), k-nearest neighbor (Knn) and artificial neural network (ANN) in predicting the occurrence of the fraud.
Further, we conduct a differentiation of the accomplished supervised machine learning and deep learning techniques to differentiate between fraud and non-fraud transactions.
EXISTING SYSTEM :
Every year, billions of dollars are lost due to credit card fraud, causing huge losses for users and the financial industry. This kind of illicit activity is perhaps the most common and the one that causes most concerns in the finance world.
In recent years great attention has been paid to the search for techniques to avoid this significant loss of money. In this degree project, we address credit card fraud by using an imbalanced dataset that contains transactions made by credit card users.
DISADVANTAGE :
Now -a-days, most of them are using credit cards for buying the goods which are so much in need but can't afford at the moment. In order to meet the needs credit cards are used and the fraud associated with it is also increasing so there is a need of developing a model that's fit well and predicts at higher accuracy.
Application fraud: When a fraudster acquires the control over the application, steals the credentials of customer, and makes a fake account and then the transactions takes place.
Electronic or manual card imprints: In this form of fraud, the fraudster skims the information from the magnetic strip which is present on the card then uses the credentials and fraud transactions are carried out
Card not present: This is a type of credit card in which physical card is not present during transaction
PROPOSED SYSTEM :
The Proposed system uses the Artificial Neural Network to find the fraud in the credit card transactions. Performance is measured and accuracy is calculated based on prediction. And also classification algorithms such as Support vector machine and k-Nearest Neighbor are used to build a credit card fraud detection model.
We compare all the three algorithms used in the experiment and made a decision that artificial neural networks predicts well than system developed using support vector machine and k-nearest neighbor algorithms.
The dataset used in the experiment consist of 31 attributes out of which 30 attributes consist of information related to name, age, account information and so on and last attribute give the outcome of the transaction in either 0 or 1.
ANN is biologically inspired by human brain. The neurons are interconnected in the human brain like the same nodes are interconnected in artificial neural network.
depicts the structure of ANN with input, output and hidden layers. Inputs are x1, x2…Xn and output is y. w1…wn are the weights associated with inputs x1…xn respectively. There are 15 hidden layers used in this neural network. The activation function used in our credit card fraud detection model is RELU.
ADVANTAGE :
The classification task can be understanding as sequential decision-making process, that uses a multiple agents to interact with the environment to obtain the optimal classification policy.
propose an ensemble pruning approach which is based on Reinforcement Learning framework.
They use Markov Decision Process and considered the ensemble pruning problem as a one player game, and select the best classifiers.
establish a deep reinforcement learning based model divided into instance selector and relational classifier, with the aim to learn the relationship classification in noisy text data.
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