SCAM Detection in Credit Card Application (original) (raw)
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Resilient Identity Crime Detection
IEEE Transactions on …, 2010
Identity crime is well known, prevalent, and costly; and credit application fraud is a specific case of identity crime. The existing non-data mining detection systems of business rules and scorecards, and known fraud matching have limitations. To address these limitations and combat identity crime in real-time, this paper proposes a new multi-layered detection system complemented with two additional layers: Communal Detection (CD) and Spike Detection (SD). CD finds real social relationships to reduce the suspicion score, and is tamper-resistant to synthetic social relationships. It is the whitelist-oriented approach on a fixed set of attributes. SD finds spikes in duplicates to increase the suspicion score, and is probe-resistant for attributes. It is the attribute-oriented approach on a variable-size set of attributes. Together, CD and SD can detect more types of attacks, better account for changing legal behaviour, and remove the redundant attributes. Experiments were carried out on CD and SD with several million real credit applications. Results on the data support the hypothesis that successful credit application fraud patterns are sudden and exhibit sharp spikes in duplicates. Although this research is specific to credit application fraud detection, the concept of resilience, together with adaptivity and quality data discussed in the paper, are general to the design, implementation, and evaluation of all detection systems.
Real-Time Credit Application Fraud Detection System Based On Data Mining
Due to market uncertainties, declining economic growth and significant growth of online e-commerce makes fraud widespread. Rapid advancement in the electronic commerce technology, the use of credit cards has increased. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of credit card fraud also rising. The applications for these credit cards are based on internet or manual applications by the customers who require the smart cards and various loans. The applications in above cases found fraud and is a specific case of identity crime. Identity crime has emerged as a serious problem for credit card customers and banks. Fraudsters steal customer's identity and obtain credit cards. This posses a major threat to the customers and banks. The existing non-data-mining detection system of business rules and scorecards, and known fraud matching has limitations. To address these limitations this paper provides an approach in identifying fraudsters at the time of application submission i.e. in real-time. The paper presents a new multi-layer fraud detection system based on data-mining algorithms. The detection system utilizes the two algorithms: communal detection (CD) and spike detection (SD) which complements each other, improving the fraud detection systems accuracy, time and cost. The credit card application undergoes validations at the time of submission before issuing the card.
DEFORMED IDENTITY CRIME DETECTION
TJPRC, 2014
Identity crime is well known, prevalent, and very prominent in our society and credit application fraud is a specific case of identity crime. The existing non-data mining detection systems of business rules and scorecards, and known fraud matching have limitations. To overcome these limitations and combat identity crime in real-time, this paper proposes a new multi-layered detection system complemented with two additional layers: clique Detection and suspicion score Detection. Clique finds real social relationships to reduce the suspicion score, and is tamper-resistant to synthetic social relationships. It is the whitelist-oriented approach on a fixed set of attributes. Suspicion score finds spikes in duplicates to increase the suspicion score, and is probe-resistant for attributes. Research has been carried out on clique and suspicion score with several and huge set of real credit applications. Although this method is specific to credit application fraud detection, but the concept of deformation, together with adaptivity and quality of data discussed in the paper, are general to the design, implementation, and evaluation of all detection systems.
Multilayer approach for Identity fraud detection System using Communal Detection and Spike Detection
In the e-governance system there is many application or digital form are filled from online so that there is much chances for fraud using Identity crime. The important reason is so much real identity data available on the Internet, and confidential data accessible through unsecured database. It has also become easy for perpetrators to hide their true identities. In case of e-governance system now day they provide government services and subsidies to peoples. This can also happened in registration of insurance, credit, and telecommunications enrolments, as well as other more serious crimes. To detect this type of fraud we can use adaptively and quality data in online data miningbased detection algorithms in the form of multilayered including layer Communal Detection (CD) by working on a fixed set of attributes. The second new layer is Spike Detection (SD): by working on a variable-size set of attributes. To defines identity crime as broadly as possible. To detect the identity crime using resilient and data mining-based layers detection system. Fraud prevention system uses a variety of policy rules to determine the likelihood of a fraudulent application. These rules are applied to the current application, past applications, fraud records and credit data to achieve the overall view of an applicant. Policy rules identify inconsistencies that indicate identity theft or other types of potential fraud. System detects suspicious applications attributes by checking whether the application: contains consistent data, contains valid data, has any indication of fraud, is a known fraud.
In the e-governance system there is many application or digital form are filled from online so that there is much chances for fraud using Identity crime. The important reason is so much real identity data available on the Internet, and confidential data accessible through unsecured database. It has also become easy for perpetrators to hide their true identities. In case of e-governance system now day they provide government services and subsidies to peoples. This can also happened in registration of insurance, credit, and telecommunications enrolments, as well as other more serious crimes. To detect this type of fraud we can use adaptively and quality data in online data mining-based detection algorithms in the form of multilayered including layer Communal Detection (CD) by working on a fixed set of attributes. The second new layer is Spike Detection (SD): by working on a variable-size set of attributes. To defines identity crime as broadly as possible. To detect the identity crime using resilient and data mining-based layers detection system. Fraud prevention system uses a variety of policy rules to determine the likelihood of a fraudulent application. These rules are applied to the current application, past applications, fraud records and credit data to achieve the overall view of an applicant. Policy rules identify inconsistencies that indicate identity theft or other types of potential fraud. System detects suspicious applications attributes by checking whether the application: contains consistent data, contains valid data, has any indication of fraud, is a known fraud.
On the communal analysis suspicion scoring for identity crime in streaming credit applications
European Journal of Operational Research, 2009
This paper describes a rapid technique: communal analysis suspicion scoring (CASS), for generating numeric suspicion scores on streaming credit applications based on implicit links to each other, over both time and space. CASS includes pair-wise communal scoring of identifier attributes for applications, definition of categories of suspiciousness for application-pairs, the incorporation of temporal and spatial weights, and smoothed k-wise scoring of multiple linked application-pairs. Results on mining several hundred thousand real credit applications demonstrate that CASS reduces false alarm rates while maintaining reasonable hit rates. CASS is scalable for this large data sample, and can rapidly detect early symptoms of identity crime. In addition, new insights have been observed from the relationships between applications.
A Supervised Approach to Credit Card Fraud
The wide acceptability and usage of credit card-based transactions can be attributed to improved technological availability and increased demand due to ease of use. As a result of the increased adoption levels, this domain has become profitable and one of the most popular targets for fraudsters who use it to conduct regular exploitation or assaults. Merchants and financial processing providers that sell credit cards suffer substantial financial damages as a result of credit card theft. Because of the possibility of large casualties, it is one of the most serious risks to these organizations and individuals. Credit card fraudulent transaction can be viewed as a binary classification task in which a supervised machine learning technique could be used to analyze and classify a credit card transaction dataset into genuine or fraudulent cases. Therefore, this study explored the use of Artificial Neural Network (ANN) for credit card fraud detection. ULB Machine Learning Group dataset that has 284, 315 legitimate and 492 fraudulent transaction were used to validate the proposed model. Performance evaluation results revealed that model achieved a 100% and 99.95% classification accuracy during training and testing respectively. This affirmed the fact that ANN model could be efficiently used to predict credit card fraudulent transactions
Silencing the Scammers: Effective Strategies in Credit Card Fraud Detection
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023
The amount of fraud has recently grown. This fraud includes your personal information or sensitive data, which someone can steal. Financial fraud is increasing significantly with the development of modern technology. Companies and financial institutions lose large amounts due to fraud. Fraud analytics combines big data analysis techniques with human interactions to help detect fraud. Credit card fraud is a pervasive and costly problem for individuals, businesses, and financial institutions worldwide. It involves the unauthorized use of someone's credit card information to make fraudulent transactions, leading to financial losses and potential harm to the cardholder's credit score. Detecting and preventing credit card fraud is a critical concern in the modern financial ecosystem, and it relies on advanced technologies and data analysis techniques.
NO-CREDIT-CARD-PRESENT FRAUD DETECTION THROUGH DATA MINING Submitted by: Batul
Today, in the world of e-commerce, credit card payment is most popular way of payment due to the rapid change of technology. As the number of credit cards being used in transactions, the number of fraud transaction is also increasing. The online credit-card fraud is committed by using someone's card for their personal reasons without the owner of the card being aware that his card is being used by another individual. And this has become serious problem throughout the world. Due to credit card fraud, companies loses a huge amount of money annually.
Credit Card Fraud Detection project
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
For some time, there has been a strong interest in the ethics of banking (Molyneaux, 2007; George, 1992), as well as the moral complexity of fraudulent behavior (Clarke, 1994). Fraud means obtaining services/goods and/or money by unethical means, and is a growing problem all over the world nowadays. Fraud deals with cases involving criminal purposes that, mostly, are difficult to identify. Credit cards are one of the most famous targets of fraud but not the only one; fraud can occur with any type of credit products, such as personal loans, home loans, and retail. Furthermore, the face of fraud has changed dramatically during the last few decades as technologies have changed and developed. A critical task to help businesses and financial institutions including banks is to take steps to prevent fraud and to deal with it efficiently and effectively, when it does happen (Anderson, 2007). Anderson (2007) has identified and explained the different types of fraud, which are as many and varied as the financial institution's products and technologies, such as Transaction products: credit and debit cards and checks, Relationship to accounts first, second and third parties, Business processes: application and transaction, Manner and timing short versus long term, Identify misrepresentation: embellishment, theft and fabrication, Handling of transaction: lost or stolen, not received, skimming and at hand, Utilization counterfeit, not present, altered or unaltered, Technologies ATM and Internet. Solutions for integrating sequential information in the feature set exist in the literature. The predominant one consists in creating a set of features which are descriptive statistics obtained by aggregating the sequences of transactions of the cardholders (sum of amount, count of transactions, location from where the payment is being made etc..). We used this method as a benchmark feature engineering method for credit card fraud detection. However, this feature engineering strategy raised several research questions. First of all, we assumed that these descriptive statistics cannot fully describe the sequential properties of fraud and genuine patterns and that modelling the sequences of transactions could be beneficial for fraud detection. Moreover, the creation of these aggregated features is guided by expert knowledge whereas sequence modelling could be automated thanks to the class labels available for past transactions. Finally, the aggregated features are point estimates that may be complemented by a multi-perspective univariate description of the transaction context. We proposed a multi-perspective HMM-based automated feature engineering strategy in order to incorporate a broad spectrum of sequential information in the transactions feature sets. In fact, we model the genuine and fraudulent behaviors of the merchants and the card-holders according to two univariate features: the country from where the payment is being made and the amount of each of the transactions being made. Moreover, the HMMbased features are created in a supervised way and therefore lower the need of expert knowledge for the creation of the fraud detection system. In the end, our multiple perspectives HMM-based approach offers automated feature engineering to model temporal correlations so as to complement and possibly supplement the use of transaction aggregation strategies in order to improve the effectiveness of the classification task. Experiments conducted on a large real world credit card transaction dataset (46 million transactions from belgium card-holders between March and May 2015) have shown that the proposed HMMbased feature engineering allows for an increase in the detection of fraudulent transactions when combined with the state-ofthe-art expert-based feature engineering strategy for credit card fraud detection. To conclude, this work leads to a better understanding of what can be considered contextual knowledge for a credit card fraud detection task and how to include it in the classification task in order to get an increase in fraud detection. The method proposed can be extended to any supervised task with sequential datasets. The main aims are, firstly, to identify the different types of credit card fraud, and, secondly, to review alternative techniques that have been used in fraud detection. Indeed, transaction products, including credit cards, are the most vulnerable to fraud. On the other hand, other products such as personal loans and retail are also at risk, and have serious ethical conflicts.