The Data Mining: A Solution for Credit Card Fraud Detection in Banking

2018 ◽  
pp. 332-341 ◽  
Author(s):  
Hafsa El-kaime ◽  
Mostafa Hanoune ◽  
Ahmed Eddaoui
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Fraud Detection ◽  
Credit Card Fraud

Data Quality Analysis based Machine Learning models for Credit Card Fraud Detection

2021 ◽  
Vol 23 (06) ◽  
pp. 318-344
Author(s):  
Amit Pundir ◽  
◽  
Rajesh Pandey ◽  
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Quality Management ◽  
Data Quality ◽  
Decision Trees ◽  
Credit Card ◽  
Fraud Detection ◽  
Data Quality Management ◽  
Credit Card Fraud ◽  
Logistics Regression

Misrepresentation of money is a developing issue in monetary business with far-reaching consequences and keeping in mind that many processes have been found. Data quality management with data mining has been effectively applied to data sets to mechanize the investigation of massive amounts of complex information. Data mining has likewise played a notable role in identifying credit card fraud in online exchanges. Fraud detection in credit cards is a data quality management issue that considered under data mining, tested for two important reasons — first, the profiles of ordinary and false practices habitually change, and also because of the explanation that charge card fraud information is exceptionally slow. This research paper examines the performance of Decision Trees, Logistics Regression, and Random Forest rely strategically on profoundly skewed credit card fraud data. The dataset of credit card transactions is sourced from Kaggle (a publically accessible dataset repository) with 284,807 transactions. These methods are applied to raw data values and data preprocessing techniques. Assessment of the performance of techniques depends on accuracy, sensitivity, specificity, precision, and recall. Results indicate the optimal accuracy for the decision trees, logistics regression, and random forest classifiers with 90.8%, 98.5%, and 99.1% respectively.

A data mining based system for credit-card fraud detection in e-tail

2017 ◽  
Vol 95 ◽  
pp. 91-101 ◽  
Author(s):  
Nuno Carneiro ◽  
Gonçalo Figueira ◽  
Miguel Costa
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Fraud Detection ◽  
Credit Card Fraud

Distributed data mining in credit card fraud detection

1999 ◽  
Vol 14 (6) ◽  
pp. 67-74 ◽  
Author(s):  
P.K. Chan ◽  
W. Fan ◽  
A.L. Prodromidis ◽  
S.J. Stolfo
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Fraud Detection ◽  
Distributed Data Mining ◽  
Distributed Data ◽  
Credit Card Fraud

scholarly journals An Xgboost based system for financial fraud detection

2020 ◽  
Vol 214 ◽  
pp. 02042
Author(s):  
Shimin LEI ◽  
Ke XU ◽  
YiZhe HUANG ◽  
Xinye SHA
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Credit Card ◽  
Detection System ◽  
Fraud Detection ◽  
Financial Fraud ◽  
Credit Card Fraud

Credit card fraud leads to billions of losses in online transaction. Many corporations like Alibaba, Amazon and Paypal invest billions of dollars to build a safe transaction system. There are some studies in this area having tried to use machine learning or data mining to solve these problems. This paper proposed our fraud detection system for e- commerce merchant. Unlike many other works, this system combines manual and automatic classifications. This paper can inspire researchers and engineers to design and deploy online transaction systems.

scholarly journals Credit Card Fraud Detection in Data Mining using XGBoost Classifier

2020 ◽  
Vol 9 (1) ◽  
pp. 603-608
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Missing Values ◽  
Real Life ◽  
Fraud Detection ◽  
Data Sets ◽  
Global Business ◽  
Data Set ◽  
Credit Card Fraud ◽  
Fraudulent Activity

In today's economy, credit card (CC) plays a major role. It is an inevitable part of a household, business & global business. While using CCs can offer huge advantages if used cautiously and safely, significant credit & financial damage can be incurred by fraudulent activity. Several methods to deal with the rising credit card fraud (CCF) have been suggested. Both such strategies, though, are meant to prevent CCFs; each of them has its own drawbacks, benefits, and functions. CCF has become a significant global concern because of the huge growth of e-commerce and the proliferation of payment online. Machine learning (ML) algo as a data mining technology (DM) was recently very involved in the detection of CCF. There are however several challenges, including the absence of publicly available data sets, high unbalanced size, and different confusing behavior. In this paper, we discuss the state of the art in credit card fraud detection (CCFD), dataset and assessment standards after analyzing issues with the CCFD. Dataset is publicly available in the CCFD data set used in experiments. Here, we compare two ML algos of performance: Logistic Regression (LR) and XGBoost in detecting CCF Transactions Real Life Data. XGBoosthas an inherent ability to handle missing values. When XGBoost encounters node at lost value, it tries to split left & right hands & learn all ways to the highest loss. This is when the test runs on the data. The experimental results show an effective use of the XGBoost classifier. Technique of performance is widely accepted metric based on exclusion: accuracy & recall. Also, the comparison between both approaches displayed based on the ROC curve

scholarly journals Credit Card Fraud Detection in Card-Not-Present Transactions: Where to Invest?

2021 ◽  
Vol 11 (15) ◽  
pp. 6766
Author(s):  
Igor Mekterović ◽  
Mladen Karan ◽  
Damir Pintar ◽  
Ljiljana Brkić
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Production Systems ◽  
Detection System ◽  
Academic Research ◽  
Fraud Detection ◽  
Detection Problem ◽  
Real World Data ◽  
Credit Card Fraud ◽  
Dominant Type

Online shopping, already on a steady rise, was propelled even further with the advent of the COVID-19 pandemic. Of course, credit cards are a dominant way of doing business online. The credit card fraud detection problem has become relevant more than ever as the losses due to fraud accumulate. Most research on this topic takes an isolated, focused view of the problem, typically concentrating on tuning the data mining models. We noticed a significant gap between the academic research findings and the rightfully conservative businesses, which are careful when adopting new, especially black-box, models. In this paper, we took a broader perspective and considered this problem from both the academic and the business angle: we detected challenges in the fraud detection problem such as feature engineering and unbalanced datasets and distinguished between more and less lucrative areas to invest in when upgrading fraud detection systems. Our findings are based on the real-world data of CNP (card not present) fraud transactions, which are a dominant type of fraud transactions. Data were provided by our industrial partner, an international card-processing company. We tested different data mining models and approaches to the outlined challenges and compared them to their existing production systems to trace a cost-effective fraud detection system upgrade path.

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