The Issue of Credit Card Freud Detection

Credit card fraud is increasing at a very high rate due to the development of modern technology and the global superhighways of communication. Credit card fraud costs consumers and the financial company billions of dollars annually, and fraudsters continuously try to find new rules and tactics to commit illegal actions. 

Fraud is one of the major ethical issues in the financial industry and for the country. Credit card fraud refers to the physical loss of credit card or loss of sensitive credit card information. As these frauds are happening on a large scale we need efficient methods like Artificial Intelligence and Machine Learning techniques. Fraud detection and prevention are costly, time-consuming, and labor-intensive tasks. The most commonly methods used in fraud detection are Naïve Bayes (NB), Support Vector Machines (SVM), K-Nearest Neighbor algorithms (KNN). Various weaknesses of include unpublished literature and an imbalance classification. Imbalance classification is caused due to a small number of observations of the minority class compared with the majority in the data set. 

Fraud detection is a process undertaken to prevent money or property from being obtained through false pretences. Fraud detection is used in many industries such as banking or insurance. In banking, fraud may include forging checks or using stolen credit cards. Other forms of fraud may involve exaggerating losses or causing an accident with the sole intent for the payout. There are different types of fraud: insurance fraud, credit card fraud, statement fraud, securities fraud etc. Of all of them, credit card fraud is the most common type. It is defined as an unauthorized use of a credit card account. It occurs when the cardholder and the card issuer are not aware that the card is being used by a third party.

With rising number of ways someone can commit fraud, detection of frauds can be difficult to accomplish. Activities such as reorganization, downsizing, moving to new information systems or encountering a cybersecurity breach could weaken an organization's ability to detect fraud. It means techniques such as real-time monitoring for frauds is recommended. Fraud is typically an act which involves many repeated methods; making searching for patterns a general focus for fraud detection. For example, data analysts can prevent insurance fraud by making algorithms to detect patterns and anomalies. Fraud detection can be separated by the use of statistical data analysis techniques or artificial intelligence.

To summarize, fraud is a critical concern for business organizations as well as individuals. Different types of fraudulent activities cost billions of dollars every year. Of all types, credit card fraud is the most common and costly one which has raised huge concern globally. Detecting of fraud in credit card is very challenging. A lot of problems make it difficult to find solutions/proper classification for fraud detection, class imbalance problem being a major one.

References

• IEEE Transaction paper ,“An Experimental Study with Imbalanced Classification Approaches for Credit Card Fraud Detection”,by Sara Makki, Zainab Assaghir,[2019].
• Credit card Fraud Detection Using Bayesian and Neural Networks by Sam Maes, Karl Tuyls, Bram Vanschoenwinkel, Vrije Universiteit Brussel [2015].
• “Credit Card Fraud Detection- Machine Learning methods”, by Dejan Varmedja, Mirjana Karanovic, Srdjan Sladojevic, Marko Arsenovic, Andras Anderla, University of Novi Sad Novi Sad, Serbia,IEEE [2019].
• An article by Linda Delamaire (UK), Hussein Abdou (UK), John Pointon (UK) “Credit card fraud and detection techniques: a review” [2009].
• Research paper by Utkarsh Porwal, Smruthi Mukund from eBay Inc “Credit Card Fraud Detection in e-Commerce: An Outlier Detection Approach” [May,2019].
• Ishu Trivedi , Monika , Mrigya Mridushi ,“ Credit Card Fraud Detection”, published in International Journal of Advanced Research in Computer and Communication Engineering Vol. 5, Issue [January, 2016].
• “Face recognition using Deep Learning”, Master Thesis by Xavier SERRA [January,2017]
• Research paper on “Machine Learning For Credit Card Fraud Detection System” by Lakshmi S.V.S.S., Selvani Deepthi Kavila[2018].
• “Credit Card Fraud Detection in Internet Using KNN Algorithm”, by C.Sudha, T. Nirmal Raj, SCSVMV University Enathur Kanchipuram [2017].
•  “Credit card fraud detection using Naïve Bayes model based and KNN classifier”, by Sai Kiran and Jyoti Guru, International Journal Of Advance Research, Ideas and Innovations In Technology.