Sequential credit card fraud detection: A joint deep neural network and probabilistic graphical model approach

2021 ◽  
Author(s):  
Javad Forough ◽  
Saeedeh Momtazi
Keyword(s):  
Neural Network ◽  
Credit Card ◽  
Deep Neural Network ◽  
Graphical Model ◽  
Fraud Detection ◽  
Probabilistic Graphical Model ◽  
Credit Card Fraud ◽  
Model Approach

Enhancing Credit Card Fraud Detection Using Deep Neural Network

2020 ◽  
pp. 301-313
Author(s):  
Souad Larabi Marie-Sainte ◽  
Mashael Bin Alamir ◽  
Deem Alsaleh ◽  
Ghazal Albakri ◽  
Jalila Zouhair
Keyword(s):  
Neural Network ◽  
Credit Card ◽  
Deep Neural Network ◽  
Fraud Detection ◽  
Credit Card Fraud

Using neural network for credit card fraud detection

2019 ◽  
Author(s):  
Sevdalina Georgieva ◽  
Maya Markova ◽  
Velizar Pavlov
Keyword(s):  
Neural Network ◽  
Credit Card ◽  
Fraud Detection ◽  
Credit Card Fraud

scholarly journals Spatio-Temporal Attention-Based Neural Network for Credit Card Fraud Detection

2020 ◽  
Vol 34 (01) ◽  
pp. 362-369 ◽  
Author(s):  
Dawei Cheng ◽  
Sheng Xiang ◽  
Chencheng Shang ◽  
Yiyi Zhang ◽  
Fangzhou Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Credit Card ◽  
Domain Knowledge ◽  
Empirical Studies ◽  
Fraud Detection ◽  
Temporal Attention ◽  
Credit Card Fraud ◽  
Domain Experts ◽  
Spatio Temporal ◽  
Better Than

Credit card fraud is an important issue and incurs a considerable cost for both cardholders and issuing institutions. Contemporary methods apply machine learning-based approaches to detect fraudulent behavior from transaction records. But manually generating features needs domain knowledge and may lay behind the modus operandi of fraud, which means we need to automatically focus on the most relevant patterns in fraudulent behavior. Therefore, in this work, we propose a spatial-temporal attention-based neural network (STAN) for fraud detection. In particular, transaction records are modeled by attention and 3D convolution mechanisms by integrating the corresponding information, including spatial and temporal behaviors. Attentional weights are jointly learned in an end-to-end manner with 3D convolution and detection networks. Afterward, we conduct extensive experiments on real-word fraud transaction dataset, the result shows that STAN performs better than other state-of-the-art baselines in both AUC and precision-recall curves. Moreover, we conduct empirical studies with domain experts on the proposed method for fraud post-analysis; the result demonstrates the effectiveness of our proposed method in both detecting suspicious transactions and mining fraud patterns.

scholarly journals Credit card fraud detection using neural network and geolocation

2017 ◽  
Vol 263 ◽  
pp. 042039 ◽  
Author(s):  
Aman Gulati ◽  
Prakash Dubey ◽  
C MdFuzail ◽  
Jasmine Norman ◽  
R Mangayarkarasi
Keyword(s):  
Neural Network ◽  
Credit Card ◽  
Fraud Detection ◽  
Credit Card Fraud
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