Machine Learning Techniques for SIM Box Fraud Detection

2019 ◽  
Author(s):  
Mhair Kashir ◽  
Sajid Bashir
Keyword(s):  
Machine Learning ◽  
Fraud Detection ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Fraud Detection: A Study of AdaBoost Classifier and K-Means Clustering

2021 ◽  
Author(s):  
Anwesha Mishra
Keyword(s):  
Machine Learning ◽  
Research Collaboration ◽  
Credit Cards ◽  
Fraud Detection ◽  
Machine Learning Techniques ◽  
Learning Group ◽  
The Past ◽  
Big Data Mining ◽  
Learning Techniques ◽  
Adaboost Classifier

Abstract Fraud is a problem which can affect the economy greatly. Billions of dollars are lost because of fraud cases. These problems can occur through credit cards, insurance and bank accounts. Currently there have been many studies for preventing fraud. Machine learning techniques have helped in analysing fraud detection. These include many supervised and unsupervised models. Neural networks can be used for fraud detection. The dataset for the present work was collected from a research collaboration between Worldline and the Machine Learning Group of Université Libre de Bruxelles on the topic of big data mining and fraud detection. It consists of the time and amount of various transactions of European card holders during the month of September in 2013. This paper gives an analysis of the past and the present models used for fraud detection and presents a study of using K-Means Clustering and AdaBoost Classifier by comparing their accuracies.

scholarly journals THE USE OF MACHINE LEARNING TECHNIQUES IN ACCOUNTING. A SHORT SURVEY

2021 ◽  
Vol 4 (3) ◽  
pp. 139-143
Author(s):  
Mariana Vlad ◽  
◽  
Sorin Vlad ◽  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Real Life ◽  
Fraud Detection ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Behavior Prediction ◽  
Accounting Fraud ◽  
Research Fields ◽  
Learning Techniques

Machine learning (ML) is a subset of artificial Intelligence (AI) aiming to develop systems that can learn and continuously improve the abilities through generalization in an autonomous manner. ML is presently all around us, almost every facet of our digital and real life is embedding some ML related content. Customer recommendation systems, customer behavior prediction, fraud detection, speech recognition, image recognition, black & white movies colorization, accounting fraud detection are just some examples of the vast range of applications in which ML is involved. The techniques that this paper investigates are mainly focused on the use of neural networks in accounting and finance research fields. An artificial neural network is modelling the brain ability of learning intricate patterns from the information presented at its inputs using elementary interconnected units, named neurons, grouped in layers and trained by means of a learning algorithm. The performance of the network depends on many factors like the number of layers, the number of each neurons in each layer, the learning algorithm, activation functions, to name just a few of them. Machine learning algorithms have already started to replace humans in jobs that require document’s processing and decision making.

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.

Sign in / Sign up

Export Citation Format

Share Document