oalib
Search Results: 1 - 10 of 100 matches for " "
All listed articles are free for downloading (OA Articles)
Page 1 /100
Display every page Item
Credit Card Fraud Detection using Decision Tree for Tracing Email and IP  [PDF]
R.Dhanapal,Gayathiri.P
International Journal of Computer Science Issues , 2012,
Abstract: Credit card fraud is a wide-ranging term for theft and fraud committed using a credit card or any similar payment mechanism as a fraudulent source of funds in a transaction. The purpose may be to obtain goods without paying, or to obtain unauthorized funds from an account. Transactions completed with credit cards seem to become more and more popular with the introduction of online shopping and banking. Correspondingly, the number of credit card frauds has also increased .Currently; data mining is a popular way to combat frauds because of its effectiveness. Data mining is a well-defined procedure that takes data as input and produces output in the forms of models or patterns. In other words, the task of data mining is to analyze a massive amount of data and to extract some usable information that we can interpret for future uses. Frauds has also increased .Currently, data mining is a popular way to combat frauds because of its effectiveness. Data mining is a well-defined procedure that takes data as input and produces output in the forms of models or patterns. In other words, the task of data mining is to analyze a massive amount of data and to extract some usable information that we can interpret for future uses.
Prevention and Detection of Financial Statement Fraud – An Implementation of Data Mining Framework  [PDF]
Rajan Gupta,Nasib Singh Gill
International Journal of Advanced Computer Sciences and Applications , 2012,
Abstract: Every day, news of financial statement fraud is adversely affecting the economy worldwide. Considering the influence of the loss incurred due to fraud, effective measures and methods should be employed for prevention and detection of financial statement fraud. Data mining methods could possibly assist auditors in prevention and detection of fraud because data mining can use past cases of fraud to build models to identify and detect the risk of fraud and can design new techniques for preventing fraudulent financial reporting. In this study we implement a data mining methodology for preventing fraudulent financial reporting at the first place and for detection if fraud has been perpetrated. The association rules generated in this study are going to be of great importance for both researchers and practitioners in preventing fraudulent financial reporting. Decision rules produced in this research complements the prevention mechanism by detecting financial statement fraud.
A Review of Financial Accounting Fraud Detection based on Data Mining Techniques  [PDF]
Anuj Sharma,Prabin Kumar Panigrahi
Computer Science , 2013, DOI: 10.5120/4787-7016
Abstract: With an upsurge in financial accounting fraud in the current economic scenario experienced, financial accounting fraud detection (FAFD) has become an emerging topic of great importance for academic, research and industries. The failure of internal auditing system of the organization in identifying the accounting frauds has lead to use of specialized procedures to detect financial accounting fraud, collective known as forensic accounting. Data mining techniques are providing great aid in financial accounting fraud detection, since dealing with the large data volumes and complexities of financial data are big challenges for forensic accounting. This paper presents a comprehensive review of the literature on the application of data mining techniques for the detection of financial accounting fraud and proposes a framework for data mining techniques based accounting fraud detection. The systematic and comprehensive literature review of the data mining techniques applicable to financial accounting fraud detection may provide a foundation to future research in this field. The findings of this review show that data mining techniques like logistic models, neural networks, Bayesian belief network, and decision trees have been applied most extensively to provide primary solutions to the problems inherent in the detection and classification of fraudulent data.
A Survey on Hidden Markov Model for Credit Card Fraud Detection
Anshul Singh,Devesh Narayan
International Journal of Engineering and Advanced Technology , 2012,
Abstract: Credit card frauds are increasing day by day regardless of the various techniques developed for its detection. Fraudsters are so expert that they engender new ways for committing fraudulent transactions each day which demands constant innovation for its detection techniques as well. Many techniques based on Artificial Intelligence, Data mining, Fuzzy logic, Machine learning, Sequence Alignment, decision tree, neural network, logistic regression, na ve Bayesian, Bayesian network, metalearning, Genetic Programming etc., has evolved in detecting various credit card fraudulent transactions. A steady indulgent on all these approaches will positively lead to an efficient credit card fraud detection system. This paper presents a survey of various techniques used in credit card fraud detection mechanisms and Hidden Markov Model (HMM) in detail. HMM categorizes card holder’s profile as low, medium and high spending based on their spending behavior in terms of amount. A set of probabilities for amount of transaction is being assigned to each cardholder. Amount of each incoming transaction is then matched with card owner’s category, if it justifies a predefined threshold value then the transaction is decided to be legitimate else declared as fraudulent.
Comparative Analysis of Serial Decision Tree Classification Algorithms  [PDF]
Matthew Nwokejizie Anyanwu,Sajjan Shiva
International Journal of Computer Science and Security , 2009,
Abstract: Classification of data objects based on a predefined knowledge of the objects is a data mining and knowledge management technique used in grouping similar data objects together. It can be defined as supervised learning algorithms as it assigns class labels to data objects based on the relationship between the data items with a pre-defined class label. Classification algorithms have a wide range of applications like churn prediction, fraud detection, artificial intelligence, and credit card rating etc. Also there are many classification algorithms available in literature but decision trees is the most commonly used because of its ease of implementation and easier to understand compared to other classification algorithms. Decision Tree classification algorithm can be implemented in a serial or parallel fashion based on the volume of data, memory space available on the computer resource and scalability of the algorithm. In this paper we will review the serial implementations of the decision tree algorithms, identify those that are commonly used. We will also use experimental analysis based on sample data records (Statlog data sets) to evaluate the performance of the commonly used serial decision tree algorithms
Detecting Auto Insurance Fraud by Data Mining Techniques  [cached]
Rekha Bhowmik
Journal of Emerging Trends in Computing and Information Sciences , 2011,
Abstract: The paper presents fraud detection method to predict and analyze fraud patterns from data. To generate classifiers, we apply the Na ve Bayesian Classification, and Decision Tree-Based algorithms. A brief description of the algorithm is provided along with its application in detecting fraud. The same data is used for both the techniques. We analyze and interpret the classifier predictions. The model prediction is supported by Bayesian Na ve Visualization, Decision Tree visualization, and Rule-Based Classification. We evaluate techniques to solve fraud detection in automobile insurance.
Survey of Insurance Fraud Detection Using Data Mining Techniques  [PDF]
H.Lookman sithic,,T.Balasubramanian
International Journal of Innovative Technology and Exploring Engineering , 2013,
Abstract: With an increase in financial accounting fraud in the current economic scenario experienced, financial accounting fraud detection has become an emerging topics of great importance for academics, research and industries. Financial fraud is a deliberate act that is contrary to law, rule or policy with intent to obtain unauthorized financial benefit and intentional misstatements or omission of amounts by deceiving users of financial statements, especially investors and creditors. Data mining techniques are providing great aid in financial accounting fraud detection, since dealing with the large data volumes and complexities of financial data are big challenges for forensic accounting. Financial fraud can be classified into four: bank fraud, insurance fraud, securities and commodities fraud. Fraud is nothing but wrongful or criminal trick planned to result in financial or personal gains. This paper describes the more details on insurance sector related frauds and related solutions. In finance, insurance sector is doing important role and also it is unavoidable sector of every human being.
Survey of Insurance Fraud Detection Using Data Mining Techniques  [PDF]
H. Lookman Sithic,T. Balasubramanian
Computer Science , 2013,
Abstract: With an increase in financial accounting fraud in the current economic scenario experienced, financial accounting fraud detection has become an emerging topics of great importance for academics, research and industries. Financial fraud is a deliberate act that is contrary to law, rule or policy with intent to obtain unauthorized financial benefit and intentional misstatements or omission of amounts by deceiving users of financial statements, especially investors and creditors. Data mining techniques are providing great aid in financial accounting fraud detection, since dealing with the large data volumes and complexities of financial data are big challenges for forensic accounting. Financial fraud can be classified into four: bank fraud, insurance fraud, securities and commodities fraud. Fraud is nothing but wrongful or criminal trick planned to result in financial or personal gains. This paper describes the more details on insurance sector related frauds and related solutions. In finance, insurance sector is doing important role and also it is unavoidable sector of every human being.
Survey of Data-mining Techniques used in Fraud Detection and Prevention  [PDF]
Sheela Thiruvadi,Sandip C. Patel
Information Technology Journal , 2011,
Abstract: Data mining is a powerful tool widely used by organizations to enhance their businesses and gain a competitive advantage over their competitors. The data mining process helps in extracting and analyzing various data patterns, information or trends from large databases. Various data mining techniques are available to conduct the data mining process. Data mining techniques are used in a variety of applications, one of which is the detection and prevention of different types of frauds. Although there is existing research on data mining and various data mining techniques that can be used to detect and identify different types of frauds, there is little research that synthesizes various facets of fraud that uses the data mining techniques. In this survey study, we classify frauds into four categories with regards to the use of data mining as a tool in fraud detection and prevention. The four categories of fraud are management fraud, customer fraud, network fraud and computer-based fraud. We present the latest developments on the use of data mining as a tool for each of these categories.
FRAUD DETECTION IN CREDIT CARD SYSTEM USING WEB MINING  [PDF]
HETVI MODI,SHIVANGI LAKHANI,NIMESH PATEL,VAISHALI PATEL
International Journal of Innovative Research in Computer and Communication Engineering , 2013,
Abstract: Now a day the usage of credit cards has dramatically increased. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. Various techniques like classification, clustering and apriori of web mining will be integrated to represent the sequence of operations in credit card transaction processing and show how it can be used for the detection of frauds. Initially, web mining techniques trained with the normal behaviour of a cardholder. If an incoming credit card transaction is not accepted by the web mining model with sufficiently high probability, it is considered to be fraudulent. At the same time, the system will try to ensure that genuine transactions will not be rejected. Using data from a credit card issuer, a web mining model based fraud detection system will be trained on a large sample of labelled credit card account transactions and tested on a holdout data set that consisted of all account activity. Web mining techniques can be trained on examples of fraud due to lost cards, stolen cards, application fraud, counterfeit fraud, and mail-order fraud. The proposed system will be able to detect frauds by considering a cardholder’s spending habit without its significance. Usually, the details of items purchased in individual transactions are not known to any Fraud Detection System. The proposed system will be an ideal choice for addressing this problem of current fraud detection system. Another important advantage of proposed system will be a drastic reduction in the number of False Positives transactions. FDS module of proposed system will receive the card details and the value of purchase to verify, whether the transaction is genuine or not. If the Fraud Detection System module will confirm the transaction to be of fraud, it will raise an alarm, and the transaction will be declined.
Page 1 /100
Display every page Item


Home
Copyright © 2008-2017 Open Access Library. All rights reserved.