Cost sensitive modeling of credit card fraud using neural network strategy

2016 ◽  
Author(s):  
Fahimeh Ghobadi ◽  
Mohsen Rohani
Keyword(s):  
Neural Network ◽  
Credit Card ◽  
Credit Card Fraud

scholarly journals Credit Card Fraud Detection using Deep Learning based on Neural Network and Auto-encoder

2020 ◽  
Vol 9 (5) ◽  
pp. 1140-1143
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Credit Card ◽  
Online Media ◽  
Credit Card Fraud ◽  
Online Transactions ◽  
The Past ◽  
Past Behavior ◽  
The Neural Network ◽  
The Comparative Study

Credit card fraud is an event problem and fraud detecting techniques getting more sophisticated each day. Mainly internet is becoming more common in almost every domain. Online transactions, shopping, and e-commerce are expanding step by step. Due to which in the online payment system, fraudulent activities have also increased. It has cost banks and their customers a loss of billions of rupees. The techniques used now a day detects the anomaly only after the fraud transaction takes place. The intruders have found ways to crack the system loopholes and defeat the security. These frauds are not consistent in their actions, they constantly alter. Thus, Artificial Intelligent (AI) algorithms are used to detect the behavior of such activity by learning the past behavior of the transaction of the users. An unsupervised algorithm is used to detect online transactions, as fraudsters commit fraud once by online media and then move on to other techniques. This paper discusses the performance analysis and the comparative study of the two Deep Learning algorithms which include auto-encoder and the neural network. In this paper accuracy, precision, recall, and AUC curve are considered as a model evaluation factor.

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.

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