Description
Credit Card Fraud Detection using Artificial Intelligence
Abstract – Credit Card Fraud Detection using Artificial Intelligence – In our project, we mainly focused on credit card Number detection in the real world. Initially, I will collect the credit card datasets for the trained dataset. Then will provide the user credit card queries for the testing data set. After the classification process of deep learning using to the already analyzing data set and the user provides the current dataset. Finally optimizing the number detection Then will apply the processing of some of the attributes provided can find affected fraud detection in viewing the graphical model visualization. The performance of the techniques is evaluated based on computer vision Technique
Introduction
With the rapid development of Internet finance, the convenience of electronic transfer, and the rapid expansion of the credit card business, the use of credit cards in daily life is becoming more and more widespread. The risks associated with credit card frauds of all types are related to major issuing cards and all Cardholders have caused serious economic, credit, and other threats. With the alarming increase in the number of credit card fraud transactions in the world, the continuous refurbishment of credit card fraud tactics has mainly manifested itself in the fraudulent use of other people’s credit card transactions and malicious overdrafts, the forgery of credit card fraud, and the use of obsolete credit cards fraud, etc., resulting in increasing losses. Methods to identify credit cards effectively, quickly, and accurately have become a hot topic in recent research. Currently, the data mining algorithms used for detecting credit card fraud risk are mainly based on the Bayesian network algorithm, decision tree algorithm, and neural network algorithm. Credit card transactions are extremely commonplace now but they also come with their own set of problems. There are a lot of problems faced during fraud detection. The process of acceptance or rejection of a transaction happens within a very small time frame, which may range between micro and milliseconds. Therefore, the process adopted for the detection of a fraudulent transaction has to be extremely quick and effective. Another problem is that there are a vast number of similar types of transactions happening at the same time. This makes it difficult to monitor each and every transaction individually and hence determine fraud. Thus, an efficient Fraud Detection System must be put into work to be able to differentiate between a genuine and a fraud transaction. Such a system works on the principle of learning user-specific card usage behavior.
Conclusion
Thus in the proposed model using deep learning the developed model works well compared to the existing one has low cost and the accuracy of the model is also similar to that of the existing model. The computational complex algorithm used in the previously existing method is replaced with the deep learning neural network that offers more advantages when compared to the existing model.
REFERENCES
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- C. Wang, Y. Wang, Z. Ye, L. Yan, W. Cai and S. Pan, “Credit Card Fraud Detection Based on Whale Algorithm Optimized BP Neural Network,” 2018 13th International Conference on Computer Science & Education (ICCSE), Colombo, Sri Lanka, 2018, pp. 1-4, doi: 10.1109/ICCSE.2018.8468855.
- Y. Lucas et al., “Dataset Shift Quantification for Credit Card Fraud Detection,” 2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), Sardinia, Italy, 2019, pp. 97-100, doi: 10.1109/AIKE.2019.00024.
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- Agrawal, S. Kumar and A. K. Mishra, “Implementation of Novel Approach for Credit Card Fraud Detection,” 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India, 2015, pp. 1-4.
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- Agrawal, S. Kumar and A. K. Mishra, “A Novel Approach for Credit Card Fraud Detection,” 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India, 2015, pp. 8-11.
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