Credit card fraud detection using a hierarchical behavior-knowledge space model.
Ontology highlight
ABSTRACT:
SUBMITTER: Nandi AK
PROVIDER: S-EPMC8775357 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Due to the surge of interest in online retailing, the use of credit cards has been rapidly expanded in recent years. Stealing the card details to perform online transactions, which is called fraud, has also seen more frequently. Preventive solutions and instant fraud detection methods are widely studied due to critical financial losses in many industries. In this work, a Gradient Boosting Tree (GBT) model for the real-time detection of credit card frauds on the streaming Card-Not-Present (CNP) transactions is investigated with the use of different attributes of card transactions. Numerical, hand-crafted numerical, categorical and textual attributes are combined to form a feature vector to be used as a training instance. One of the contributions of this work is to employ transaction aggregation for the categorical values and inclusion of vectors from a character level word embedding model which is trained on the merchant names of the transactions. The other contribution is introducing a new strategy for training dataset generation employing the sliding window approach in a given time frame to adapt to the changes on the trends of fraudulent transactions. In the experiments, the feature engineering strategy and the automated training set generation methodology are evaluated on the real credit card transactions.
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Project description:Fraudulent activities especially in auto insurance and credit card transactions impose significant financial losses on businesses and individuals. To overcome this issue, we propose a novel approach for fraud detection, combining convolutional neural networks (CNNs) with support vector machine (SVM), k nearest neighbor (KNN), naive Bayes (NB), and decision tree (DT) algorithms. The core of this methodology lies in utilizing the deep features extracted from the CNNs as inputs to various machine learning models, thus significantly contributing to the enhancement of fraud detection accuracy and efficiency. Our results demonstrate superior performance compared to previous studies, highlighting our model's potential for widespread adoption in combating fraudulent activities.
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Project description:Statistical fraud detection consists in making a system that automatically selects a subset of all cases (insurance claims, financial transactions, etc.) that are the most interesting for further investigation. The reason why such a system is needed is that the total number of cases typically is much higher than one realistically could investigate manually and that fraud tends to be quite rare. Further, the investigator is typically limited to controlling a restricted number k of cases, due to limited resources. The most efficient manner of allocating these resources is then to try selecting the k cases with the highest probability of being fraudulent. The prediction model used for this purpose must normally be regularised to avoid overfitting and consequently bad prediction performance. A loss function, denoted the fraud loss, is proposed for selecting the model complexity via a tuning parameter. A simulation study is performed to find the optimal settings for validation. Further, the performance of the proposed procedure is compared to the most relevant competing procedure, based on the area under the receiver operating characteristic curve (AUC), in a set of simulations, as well as on a credit card default dataset. Choosing the complexity of the model by the fraud loss resulted in either comparable or better results in terms of the fraud loss than choosing it according to the AUC.
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Project description:Credit cards have often been blamed for consumer overspending and for the growth in household debt. Indeed, laboratory studies of purchase behavior have shown that credit cards can facilitate spending in ways that are difficult to justify on purely financial grounds. However, the psychological mechanisms behind this spending facilitation effect remain conjectural. A leading hypothesis is that credit cards reduce the pain of payment and so 'release the brakes' that hold expenditures in check. Alternatively, credit cards could provide a 'step on the gas,' increasing motivation to spend. Here we present the first evidence of differences in brain activation in the presence of real credit and cash purchase opportunities. In an fMRI shopping task, participants purchased items tailored to their interests, either by using a personal credit card or their own cash. Credit card purchases were associated with strong activation in the striatum, which coincided with onset of the credit card cue and was not related to product price. In contrast, reward network activation weakly predicted cash purchases, and only among relatively cheaper items. The presence of reward network activation differences highlights the potential neural impact of novel payment instruments in stimulating spending-these fundamental reward mechanisms could be exploited by new payment methods as we transition to a purely cashless society.
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Project description:We study the dynamic effect of the COVID-19 shock on credit card use in 2020. Local case incidence had a strong negative effect on credit card spending in the early months of the pandemic, which diminished over time. This time-varying pattern was driven by the fear of the virus, rather than government support programs, consistent with the "pandemic fatigue" of consumers. Local pandemic severity also had a strong effect on credit card repayments. These spending and repayment effects offset each other, resulting in no effect on credit card borrowing, consistent with credit-smoothing behavior. The local stringency of nonpharmaceutical interventions also had a negative effect on spending and repayments, albeit smaller in magnitude. We conclude that the pandemic itself was a more important driver of changes in credit card use than the public health policy response.
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Project description:BACKGROUND:Fraud, Waste, and Abuse (FWA) detection is a significant yet challenging problem in the health insurance industry. An essential step in FWA detection is to check whether the medication is clinically reasonable with respect to the diagnosis. Currently, human experts with sufficient medical knowledge are required to perform this task. To reduce the cost, insurance inspectors tend to build an intelligent system to detect suspicious claims with inappropriate diagnoses/medications automatically. OBJECTIVE:The aim of this study was to develop an automated method for making use of a medical knowledge graph to identify clinically suspected claims for FWA detection. METHODS:First, we identified the medical knowledge that is required to assess the clinical rationality of the claims. We then searched for data sources that contain information to build such knowledge. In this study, we focused on Chinese medical knowledge. Second, we constructed a medical knowledge graph using unstructured knowledge. We used a deep learning-based method to extract the entities and relationships from the knowledge sources and developed a multilevel similarity matching approach to conduct the entity linking. To guarantee the quality of the medical knowledge graph, we involved human experts to review the entity and relationships with lower confidence. These reviewed results could be used to further improve the machine-learning models. Finally, we developed the rules to identify the suspected claims by reasoning according to the medical knowledge graph. RESULTS:We collected 185,796 drug labels from the China Food and Drug Administration, 3390 types of disease information from medical textbooks (eg, symptoms, diagnosis, treatment, and prognosis), and information from 5272 examinations as the knowledge sources. The final medical knowledge graph includes 1,616,549 nodes and 5,963,444 edges. We designed three knowledge graph reasoning rules to identify three kinds of inappropriate diagnosis/medications. The experimental results showed that the medical knowledge graph helps to detect 70% of the suspected claims. CONCLUSIONS:The medical knowledge graph-based method successfully identified suspected cases of FWA (such as fraud diagnosis, excess prescription, and irrational prescription) from the claim documents, which helped to improve the efficiency of claim processing.
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Project description:This study uses a laboratory experiment to examine whether prior knowledge of food fraud persistently affects consumer behavior. We invited regular consumers of olive oil to participate in an olive oil valuation experiment. We used a within-subject design to compare consumers' willingness to pay (WTP) for Italian extra virgin olive oil (EVOO) before and after receiving information about labeling scandals in the Italian olive oil industry. After the first round of bidding, but before introducing information about labeling scandals or otherwise mentioning food fraud, we surveyed participants about whether they had heard of food fraud. Results indicate that prior knowledge of food fraud plays an important role in explaining consumers' valuation behavior, both in the pre-information baseline bidding and in how they update their valuation in response to information about a food fraud scandal. Consumers who reported prior knowledge of food fraud partially accounted for the possibility of food fraud in their initial pre-information valuation, submitting significantly lower bids than participants who did not report prior knowledge. They also reacted less to olive oil fraud information than consumers who reported no prior knowledge of food fraud. Findings of this study highlight the potential long-term consequences of increasing consumer awareness of food fraud incidents on consumer WTP for products in industries that have experienced food fraud scandals.
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Project description:Today, food safety is recognized as one of the most important human priorities, so effective and new policies have been implemented to improve and develop the position of effective laws in the food industry. Extra virgin olive oil (EVOO) has many amazing benefits for human body's health. Due to the nutritional value and high price of EVOO, there is a lot of cheating in it. The ultrasound approach has many advantages in the food studies, and it is fast and nondestructive for quality evaluation. In this study, to fraud detection of EVOO four ultrasonic properties of oil in five levels of adulteration (5%, 10%, 20%, 35%, and 50%) were extracted. The 2 MHz ultrasonic probes were used in the DOI 1,000 STARMANS diagnostic ultrasonic device in a "probe holding mechanism." The four extracted ultrasonic features include the following: "percentage of amplitude reduction, time of flight (TOF), the difference between the first and second maximum amplitudes of the domain (in the time-amplitude diagram), and the ratio of the first and second maximum of amplitude." Seven classification algorithms including "Naïve Bayes, support vector machine, gradient boosting classifier, K-nearest neighbors, artificial neural network, logistic regression, and AdaBoost" were used to classify the preprocessed data. Results showed that the Naïve Bayes algorithm with 90.2% provided the highest accuracy among the others, and the support vector machine and gradient boosting classifier with 88.2% were in the next ranks.
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