scholarly journals Provisional Research on Ensemble Learning Techniques for Card Fraud Detection

2019 ◽  
Vol 8 (6S) ◽  
pp. 13-17
Keyword(s):  
Machine Learning ◽  
Credit Card ◽  
Academic Research ◽  
Fraud Detection ◽  
Learning Approaches ◽  
Financial Industry ◽  
Detection Systems ◽  
Learning Techniques ◽  
High Prediction ◽  
Global Statistics

Machine learning have revolutionized fraud detection in various domains like telecommunication and ecommerce. Global statistics shows how billions of dollars are lost because of card frauds every year and millions of people falling the victims. Fraud detection systems used for credit card fraud detection 2 decades ago are still being used because of the trust and stability they have provided for so long. With a number of academic research being done in fraud detection their effect on the financial industry has been minimum. Even with high prediction accuracy using machine learning approaches like deep learning and stack ensemble most of these research gets directly rejected by the industry. Our research objective is to highlight the reason of rejectection which are mostly ignored by the researchers and there adverse effect on the results

scholarly journals Machine Learning Approaches for Credit Card Fraud Detection

2018 ◽  
Vol 7 (2) ◽  
pp. 917
Author(s):  
S Venkata Suryanarayana ◽  
G N. Balaji ◽  
G Venkateswara Rao
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Language Processing ◽  
Credit Card ◽  
Fraud Detection ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Credit Card Fraud ◽  
Public Data ◽  
The Impact

With the extensive use of credit cards, fraud appears as a major issue in the credit card business. It is hard to have some figures on the impact of fraud, since companies and banks do not like to disclose the amount of losses due to frauds. At the same time, public data are scarcely available for confidentiality issues, leaving unanswered many questions about what is the best strategy. Another problem in credit-card fraud loss estimation is that we can measure the loss of only those frauds that have been detected, and it is not possible to assess the size of unreported/undetected frauds. Fraud patterns are changing rapidly where fraud detection needs to be re-evaluated from a reactive to a proactive approach. In recent years, machine learning has gained lot of popularity in image analysis, natural language processing and speech recognition. In this regard, implementation of efficient fraud detection algorithms using machine-learning techniques is key for reducing these losses, and to assist fraud investigators. In this paper logistic regression, based machine learning approach is utilized to detect credit card fraud. The results show logistic regression based approaches outperforms with the highest accuracy and it can be effectively used for fraud investigators.  

scholarly journals Machine Learning confronted with the operational constraints of detection systems

2019 ◽  
Vol 1 (1) ◽  
pp. 1-7
Author(s):  
Sridarala Ramu ◽  
Daniel Osaku
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Academic Research ◽  
Intrusion Detection Systems ◽  
Machine Learning Techniques ◽  
Detection Model ◽  
Detection Systems ◽  
Learning Techniques ◽  
High Level ◽  
Operational Constraints

Intrusion detection systems, traditionally based on signatures, have not escaped the recent appeal of machine learning techniques. While the results presented in academic research articles are often excellent, security experts still have many reservations about the use of Machine Learning in intrusion detection systems. They generally fear an inadequacy of these techniques to operational constraints, in particular because of a high level of expertise required, or a large number of false positives. In this article, we show that Machine Learning can be compatible with the operational constraints of detection systems. We explain how to build a detection model and present good practices to validate it before it goes into production. The methodology is illustrated by a case study on the detection of malicious PDF files and we offer a free tool, SecuML, to implement it.

scholarly journals An Empirical Study of AML Approach for Credit Card Fraud Detection–Financial Transactions

2019 ◽  
Vol 14 (6) ◽  
pp. 670-690 ◽  
Author(s):  
Ajeet Singh ◽  
Anurag Jain
Keyword(s):  
Machine Learning ◽  
Credit Card ◽  
Evaluation Criteria ◽  
Fraud Detection ◽  
Machine Learning Techniques ◽  
Credit Card Fraud ◽  
Detection Techniques ◽  
Digital World ◽  
Learning Techniques ◽  
Sensitivity Specificity

Credit card fraud is one of the flip sides of the digital world, where transactions are made without the knowledge of the genuine user. Based on the study of various papers published between 1994 and 2018 on credit card fraud, the following objectives are achieved: the various types of credit card frauds has identified and to detect automatically these frauds, an adaptive machine learning techniques (AMLTs) has studied and also their pros and cons has summarized. The various dataset are used in the literature has studied and categorized into the real and synthesized datasets.The performance matrices and evaluation criteria have summarized which has used to evaluate the fraud detection system.This study has also covered the deep analysis and comparison of the performance (i.e sensitivity, specificity, and accuracy) of existing machine learning techniques in the credit card fraud detection area.The findings of this study clearly show that supervised learning, card-not-present fraud, skimming fraud, and website cloning method has been used more frequently.This Study helps to new researchers by discussing the limitation of existing fraud detection techniques and providing helpful directions of research in the credit card fraud detection field.

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