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26-04-2017, 09:52 PM

Sir please can u send me the ppt for credit card fraud detection

jaseela123d · 28-04-2017, 11:13 AM

The problem of credit card fraud detection of a bank and tried to improve the performance of an existing solution. In doing so, we have not undertaken the typical goal of maximizing the number of correctly classified transactions, but we have defined a new objective function where the costs of misclassification are variable and therefore, the correct classification of some transactions is more important than classifying correctly the others. For this purpose we have made an application of genetic algorithms which is a novel in related literature, both in terms of the application domain and the cross-over operator used. The algorithm is applied to real-life data where the savings obtained are almost three times the current practice.

Due to the rise and rapid growth of e-commerce, the use of credit cards for online shopping has increased dramatically and caused an explosion in credit card fraud. Fraud is one of the major ethical issues in the credit card industry. As the credit card becomes the most popular payment mode for both online purchase and regular purchase, fraud cases related to it are also increasing. In real life, fraudulent transactions are scattered with genuine transactions and simple pattern matching techniques are often not enough to detect such frauds accurately. The implementation of efficient fraud detection systems has become an imperative for all credit card issuers to minimize their losses. Many modern techniques based on Artificial Intelligence, data mining, fuzzy logic, automatic learning, sequence alignment, genetic programming, etc., have evolved in the detection of several fraudulent credit card transactions. A genetic algorithm is an evolutionary search and optimization technique that mimics natural evolution to find the best solution to a problem.

It can be understood in the following video:

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