scholarly journals Credit Card Fraud Detection through Parenclitic Network Analysis

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Massimiliano Zanin ◽  
Miguel Romance ◽  
Santiago Moral ◽  
Regino Criado
Keyword(s):  
Data Mining ◽  
Complex Network ◽  
Credit Card ◽  
Reference Group ◽  
Detection System ◽  
Fraud Detection ◽  
Data Representation ◽  
Classification Algorithm ◽  
Data Set ◽  
Economic Implications

The detection of frauds in credit card transactions is a major topic in financial research, of profound economic implications. While this has hitherto been tackled through data analysis techniques, the resemblances between this and other problems, like the design of recommendation systems and of diagnostic/prognostic medical tools, suggest that a complex network approach may yield important benefits. In this paper we present a first hybrid data mining/complex network classification algorithm, able to detect illegal instances in a real card transaction data set. It is based on a recently proposed network reconstruction algorithm that allows creating representations of the deviation of one instance from a reference group. We show how the inclusion of features extracted from the network data representation improves the score obtained by a standard, neural network-based classification algorithm and additionally how this combined approach can outperform a commercial fraud detection system in specific operation niches. Beyond these specific results, this contribution represents a new example on how complex networks and data mining can be integrated as complementary tools, with the former providing a view to data beyond the capabilities of the latter.

Data Mining for Fraud Detection System

2011 ◽  
pp. 411-416
Author(s):  
Roberto Marmo
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
User Behavior ◽  
Detection System ◽  
Fraud Detection ◽  
Modern Technology ◽  
Data Sets ◽  
Security Measures ◽  
Economic Activities ◽  
Huge Data

As a conseguence of expansion of modern technology, the number and scenario of fraud are increasing dramatically. Therefore, the reputation blemish and losses caused are primary motivations for technologies and methodologies for fraud detection that have been applied successfully in some economic activities. The detection involves monitoring the behavior of users based on huge data sets such as the logged data and user behavior. The aim of this contribution is to show some data mining techniques for fraud detection and prevention with applications in credit card and telecommunications, within a business of mining the data to achieve higher cost savings, and also in the interests of determining potential legal evidence. The problem is very difficult because fraudsters takes many different forms and are adaptive, so they will usually look for ways to avoid every security measures.

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

Data Mining for Secure Online Payment Transaction

2018 ◽  
pp. 286-312
Author(s):  
Masoumeh Zareapoor ◽  
Pourya Shamsolmoali ◽  
M. Afshar Alam
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Detection Rate ◽  
Detection System ◽  
Fraud Detection ◽  
Holistic Approach ◽  
False Alarms ◽  
Detection Systems ◽  
Online Payment ◽  
Proposed Model

The fraud detection method requires a holistic approach where the objective is to correctly classify the transactions as legitimate or fraudulent. The existing methods give importance to detect all fraudulent transactions since it results in money loss. For this most of the time, they have to compromise on some genuine transactions. Thus, the major issue that the credit card fraud detection systems face today is that a significant percentage of transactions labelled as fraudulent are in fact legitimate. These “false alarms” delay the transactions and creates inconvenience and dissatisfaction to the customer. Thus, the objective of this research is to develop an intelligent data mining based fraud detection system for secure online payment transaction system. The performance evaluation of the proposed model is done on real credit card dataset and it is found that the proposed model has high fraud detection rate and less false alarm rate than other state-of-the-art classifiers.

scholarly journals Multilevel Fraud Detection System using Voting Techniques

2019 ◽  
Vol 8 (12) ◽  
pp. 1805-1811
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Detection System ◽  
Fraud Detection ◽  
Classification Algorithms ◽  
Traffic Patterns ◽  
Security Systems ◽  
Data Set ◽  
Classification Techniques ◽  
Best First Search

Fraud detection is an enduring topic that pose a threat to the information security systems. Data mining helps detect and rapidly identify fraud and take instant action to reduce losses. It analysis the various forms of attacks through classifications algorithms of data mining. In this dissertation the data set of NSL-KDD is examined in identifying anomalies in network traffic patterns and the importance of various classification techniques is studied. Firstly the data is classify in two classes Normal and Anomaly and after then the anomaly data categories in various Attack forms i.e Probe, U2R, R2L, DOS to detect the fraud. The analysis performed through feature selection and classification techniques present in WEKA tool of data mining. The features are extracted through feature selection (CfsSubsetEval, InfoGain, FilterSubsetEval and FilterAttributeEval) methods from 41 attributes to 11 attributes using CfsSubsetEval with best first Search and after extraction the features the classification algorithms(Naïve Bayes, RC, RT, RF and DT) applied for finding Accuracy. The best Result in terms of Accuracy is finding through the voting method using Random Committee and Random Forest is 99.94%.

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.

scholarly journals Credit Card Fraud Detection System Using Machine Learning

2021 ◽  
Vol 13 (4) ◽  
pp. 24-37
Author(s):  
Angela Makolo ◽  
◽  
Tayo Adeboye
Keyword(s):  
Machine Learning ◽  
Credit Card ◽  
Performance Metrics ◽  
Detection System ◽  
Fraud Detection ◽  
Support Vector ◽  
Multivariate Normal ◽  
Data Set ◽  
Credit Card Fraud ◽  
Key Factor

The security of any system is a key factor toward its acceptability by the general public. We propose an intuitive approach to fraud detection in financial institutions using machine learning by designing a Hybrid Credit Card Fraud Detection (HCCFD) system which uses the technique of anomaly detection by applying genetic algorithm and multivariate normal distribution to identify fraudulent transactions on credit cards. An imbalance dataset of credit card transactions was used to the HCCFD and a target variable which indicates whether a transaction is deceitful or otherwise. Using F-score as performance metrics, the model was tested and it gave a prediction accuracy of 93.5%, as against artificial neural network, decision tree and support vector machine, which scored 84.2%, 80.0% and 68.5% respectively, when trained on the same data set. The results obtained showed a significant improvement as compared with the other widely used algorithms.

Data Mining for Secure Online Payment Transaction

2017 ◽  
pp. 62-89
Author(s):  
Masoumeh Zareapoor ◽  
Pourya Shamsolmoali ◽  
M. Afshar Alam
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Detection Rate ◽  
Detection System ◽  
Fraud Detection ◽  
Holistic Approach ◽  
False Alarms ◽  
Detection Systems ◽  
Online Payment ◽  
Proposed Model

The fraud detection method requires a holistic approach where the objective is to correctly classify the transactions as legitimate or fraudulent. The existing methods give importance to detect all fraudulent transactions since it results in money loss. For this most of the time, they have to compromise on some genuine transactions. Thus, the major issue that the credit card fraud detection systems face today is that a significant percentage of transactions labelled as fraudulent are in fact legitimate. These “false alarms” delay the transactions and creates inconvenience and dissatisfaction to the customer. Thus, the objective of this research is to develop an intelligent data mining based fraud detection system for secure online payment transaction system. The performance evaluation of the proposed model is done on real credit card dataset and it is found that the proposed model has high fraud detection rate and less false alarm rate than other state-of-the-art classifiers.

scholarly journals Fraudulent credit card transaction detection using soft computing techniques

2021 ◽  
Vol 23 (3) ◽  
pp. 1634
Author(s):  
Aishwarya Priyadarshini ◽  
Sanhita Mishra ◽  
Debani Prasad Mishra ◽  
Surender Reddy Salkuti ◽  
Ramakanta Mohanty
Keyword(s):  
Soft Computing ◽  
Credit Card ◽  
Nearest Neighbor ◽  
Detection System ◽  
Fraud Detection ◽  
K Nearest Neighbor ◽  
Computationally Efficient ◽  
Detection Techniques ◽  
Government Organizations ◽  
Soft Computing Techniques

<p>Nowadays, fraudulent or deceitful activities associated with financial transactions, predominantly using credit cards have been increasing at an alarming rate and are one of the most prevalent activities in finance industries, corporate companies, and other government organizations. It is therefore essential to incorporate a fraud detection system that mainly consists of intelligent fraud detection techniques to keep in view the consumer and clients’ welfare alike. Numerous fraud detection procedures, techniques, and systems in literature have been implemented by employing a myriad of intelligent techniques including algorithms and frameworks to detect fraudulent and deceitful transactions. This paper initially analyses the data through exploratory data analysis and then proposes various classification models that are implemented using intelligent soft computing techniques to predictively classify fraudulent credit card transactions. Classification algorithms such as K-Nearest neighbor (K-NN), decision tree, random forest (RF), and logistic regression (LR) have been implemented to critically evaluate their performances. The proposed model is computationally efficient, light-weight and can be used for credit card fraudulent transaction detection with better accuracy.</p>

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