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 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.

scholarly journals A Comparative Study on Credit Card Fraud Detection

2021 ◽  
Author(s):  
KOUSHIK DEB
Keyword(s):  
Credit Card ◽  
Financial Institution ◽  
Detection System ◽  
Dimensional Space ◽  
Fraud Detection ◽  
Modern Technology ◽  
Two Dimensional ◽  
Rating Score ◽  
Credit Card Fraud ◽  
Dependent Variables

Credit Card Fraud is increasing rapidly with the development of modern technology. This fraud detection system has been proven essential for banks and financial institution, to minimize their losses. This paper pr- oposes Credit Card Fraud Detection using clustering based on several uns- upervised Machine learning and deep learning algorithms. The method we follow to solve our problem is that we are going to plot the points into two dimensional space and some points turns out to be an outliers and some p- oints forms a valid clusters. These outliers are possible number of cheaters which is nothing but the fraudulent transactions and the bank may reject t- heir credit card application. And valid clusters are not cheaters therefore we are going to allocate them the credit card. So as a result we get the explicit list of customers i.e. the potential cheaters who have cheated. Thus, the clu- stering approach which will give better rating score can be chosen to be one of the best methods to detect fraud. In this paper, we worked with Statlog Australian Credit Card Approval Dataset in which the dependent variables have been removed to maintain the privacy of the customers.

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 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.

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 Behavioral Based Credit Card Fraud Detection

2021 ◽  
Vol 9 (VII) ◽  
pp. 3556-3561
Author(s):  
Shalini S
Keyword(s):  
Credit Card ◽  
User Behavior ◽  
Detection System ◽  
Fraud Detection ◽  
Recognition System ◽  
Behavior Patterns ◽  
User Characteristics ◽  
Credit Card Fraud ◽  
Usage Patterns ◽  
Credit Card Data

Credit card fraud is a significant threat in the BFSI sector. This credit card fraud detection system analyzes user behavioral patterns and their location to identify any unusual patterns. This consists of user characteristics, which includes user spending styles as well as standard user geographic places to verify his identity. One of the user behavior patterns includes spending habits, usage patterns, etc. This system deals with user credit card data for various characteristics, which includes user country, usual spending procedures. Based upon previous transactions information of that person, the system recognizes unusual patterns in the payment method. The fraud detection system contains the past transaction data of each user. Based on this data, it identifies the standard user behavior patterns for individual users, and any deviation from those normal user patterns becomes a trigger for the detection system. If it detects any unusual patterns, then user will be required to undergo the security verification, which identifies the original user using QR code recognition system. In case of any unusual activity, the system not only raises alerts but it will block the user after three invalid attempts.

scholarly journals Machine Learning Techniques for Credit Card Fraud Detection

2020 ◽  
Vol 4 (2) ◽  
pp. 98-112
Author(s):  
Hossam Eldin M. Abd Elhamid ◽  
◽  
Wael Khalif ◽  
Mohamed Roushdy ◽  
Abdel-Badeeh M. Salem ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Credit Card ◽  
Fraud Detection ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Credit Card Fraud ◽  
Data Mining Techniques ◽  
Learning Techniques ◽  
Over Time

The term “fraud”, it always concerned about credit card fraud in our minds. And after the significant increase in the transactions of credit card, the fraud of credit card increased extremely in last years. So the fraud detection should include surveillance of the spending attitude for the person/customer to the determination, avoidance, and detection of unwanted behavior. Because the credit card is the most payment predominant way for the online and regular purchasing, the credit card fraud raises highly. The Fraud detection is not only concerned with capturing of the fraudulent practices, but also, discover it as fast as they can, because the fraud costs millions of dollar business loss and it is rising over time, and that affects greatly the worldwide economy. . In this paper we introduce 14 different techniques of how data mining techniques can be successfully combined to obtain a high fraud coverage with a high or low false rate, the Advantage and The Disadvantages of every technique, and The Data Sets used in the researches by researcher

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