scholarly journals Introduction to the Minitrack on Fraud Detection Using Machine Learning

2022 ◽  
Author(s):  
Tim Verdonck ◽  
Wouter Verbeke ◽  
Maria Oskarsdottir ◽  
Bart Baesens
2021 ◽  
Vol 163 ◽  
pp. 113740 ◽  
Author(s):  
Jerzy Błaszczyński ◽  
Adiel T. de Almeida Filho ◽  
Anna Matuszyk ◽  
Marcin Szeląg ◽  
Roman Słowiński

2015 ◽  
Vol 3 (2) ◽  
pp. 115-126 ◽  
Author(s):  
Naresh Babu Bynagari

Artificial Intelligence (AI) is one of the most promising and intriguing innovations of modernity. Its potential is virtually unlimited, from smart music selection in personal gadgets to intelligent analysis of big data and real-time fraud detection and aversion. At the core of the AI philosophy lies an assumption that once a computer system is provided with enough data, it can learn based on that input. The more data is provided, the more sophisticated its learning ability becomes. This feature has acquired the name "machine learning" (ML). The opportunities explored with ML are plentiful today, and one of them is an ability to set up an evolving security system learning from the past cyber-fraud experiences and developing more rigorous fraud detection mechanisms. Read on to learn more about ML, the types and magnitude of fraud evidenced in modern banking, e-commerce, and healthcare, and how ML has become an innovative, timely, and efficient fraud prevention technology.


Author(s):  
Shashank Singh and Meenu Garg

It is essential that Visa organizations can distinguish false Mastercard exchanges so clients are not charged for things that they didn't buy. Such issues can be handled with Data Science and its significance, alongside Machine Learning, couldn't be more important. This undertaking expects to outline the demonstrating of an informational collection utilizing AI with Credit Card Fraud Detection. The Credit Card Fraud Detection Problem incorporates demonstrating past Visa exchanges with the information of the ones that ended up being extortion. This model is then used to perceive if another exchange is fake. Our target here is to identify 100% of the fake exchanges while limiting the off base misrepresentation arrangements. Charge card Fraud Detection is an average example of arrangement. In this cycle, we have zeroed in on examining and pre- preparing informational indexes just as the sending of numerous irregularity discovery calculations, for example, Local Outlier Factor and Isolation Forest calculation on the PCA changed Credit Card Transaction


Author(s):  
Guilherme Ferreira Pelucio Salome ◽  
Jo�ão Luiz Chela ◽  
Jo�ão Carlos Pacheco Junior

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