Fraud Detection Algorithms Research Articles

A Survey on Fraud Detection Techniques in Ecommerce

commerce becomes popular for online shopping, banking, financial institution and government. Fraudulent activity exits in many areas of businesses and our daily life. Such activities are most prevalent in telecommunication, credit card fraud detection, network intrusion, finance and insurance and scientific applications, Billions of dollars are loss every day due to the increase in the number of credit card transactions by using online as well as offline. To design potent and efficient fraud detection algorithms is the key for reducing the losses in transaction. There were numerous approaches have been implemented for the fraud detection. This paper shows the approaches used in fraud detection in e-commerce.

Learned lessons in credit card fraud detection from a practitioner perspective

Billions of dollars of loss are caused every year due to fraudulent credit card transactions. The design of efficient fraud detection algorithms is key for reducing these losses, and more algorithms rely on advanced machine learning techniques to assist fraud investigators. The design of fraud detection algorithms is however particularly challenging due to non-stationary distribution of the data, highly imbalanced classes distributions and continuous streams of transactions.At the same time public data are scarcely available for confidentiality issues, leaving unanswered many questions about which is the best strategy to deal with them.In this paper we provide some answers from the practitioner’s perspective by focusing on three crucial issues: unbalancedness, non-stationarity and assessment. The analysis is made possible by a real credit card dataset provided by our industrial partner.

Crowdsourcing Fraud Detection Algorithm Based on Ebbinghaus Forgetting Curve

In the practical application of crowdsourcing, some unreliable workers have emerged due to profit driven. Their results seriously reduce the quality and bring about the initiator’s judgment biases. In this paper, we creatively put forward a crowdsourcing fraud detection method based on psychological behavior analysis to find out the spammer according to the psychological difference between deception and reliable behavior by means of Ebbinghaus forgetting curve. Furthermore, we constructed an online crowdsourcing experiment platform to verify the validity of our method. As a result, filtration recall and precision attained 0.93 and 0.79 respectively. Moreover, the overall LAM was decreased by 3.7% and AUC increased by 8% before and after filtration. Experimental results show that our method is effective and feasible.

Detecting online auction shilling frauds using supervised learning

Online auction sites are a target for fraud due to their anonymity, number of potential targets and low likelihood of identification. Researchers have developed methods for identifying fraud. However, these methods must be individually tailored for each type of fraud, since each differs in the characteristics important for their identification. Using supervised learning methods, it is possible to produce classifiers for specific types of fraud by providing a dataset where instances with behaviours of interest are assigned to a separate class. However this requires multiple labelled datasets: one for each fraud type of interest. It is difficult to use real-world datasets for this purpose since they are difficult to label, often limited in size, and contain zero or multiple suspicious behaviours that may or may not be under investigation.The aims of this work are to: (1) demonstrate the approach of using supervised learning together with a validated synthetic data generator to create fraud detection models that are experimentally more accurate than existing methods and that is effective over real data, and (2) to evaluate a set of features for use in general fraud detection is shown to further improve the performance of the created detection models.The approach is as follows: the data generator is an agent-based simulation modelled on users in commercial online auction data. The simulation is extended using fraud agents which model a known type of online auction fraud called competitive shilling. These agents are added to the simulation to produce the synthetic datasets. Features extracted from this data are used as training data for supervised learning. Using this approach, we optimise an existing fraud detection algorithm, and produce classifiers capable of detecting shilling fraud.Experimental results with synthetic data show the new models have significant improvements in detection accuracy. Results with commercial data show the models identify users with suspicious behaviour.

Improving Credit Card Fraud Detection using a Meta-Classification Strategy

of the issues facing credit card fraud detection systems is that a significant percentage of transactions labeled as fraudulent are in fact legitimate. These false alarms delay the detection of fraudulent transactions and can cause unnecessary concerns for customers. In this study, over 1 million unique credit card transactions from 11 months of data from a large Canadian bank were analyzed. A meta-classifier model was applied to the transactions after being analyzed by the Bank's existing neural network based fraud detection algorithm. This meta-classifier model consists of 3 base classifiers constructed using the decision tree, naive Bayesian, and k-nearest neighbour algorithms. The naive Bayesian algorithm was also used as the meta-level algorithm to combine the base classifier predictions to produce the final classifier. Results from the research show that when a meta- classifier was deployed in series with the Bank's existing fraud detection algorithm improvements of up to 28% to their existing system can be achieved.

The Securitization of Financial Identity and the Expansion of the Consumer Credit Industry

This article examines the efforts on the part of the retail banking and credit card industry to securitize identity during the period of economic activity that led to the financial crisis. The article examines how, during this period, these industries built an expansive network infrastructure and devoted considerable attention to developing ways of automatically identifying and tracking the individuals who accessed that network. Thanks significantly to this process, “identity” itself has come to be understood as a disembodied aggregate of transaction-generated data, a digital representation of the person constructed over time and space based on the perpetual collection of more data. It also has come tobe understood as something that needs to be secured, through both institutional and individual efforts, using techniques like fraud detection algorithms, vigilant self-monitoring of accounts, the adoption of identity theft protection services, and the integration of biometric technologies into documents and devices. The conceptualization of identity in these terms, with financial data (and the security of that data) at its core, is a fundamental aspect of the reorganization of the U.S. economy around the priorities of financialization. The institutional and individual measures undertaken for the securitization of identity serve the finance industry’s need to define, measure, and differentiate the population in terms of its financial capacities. However, these measures have proven to have limited utility in protecting individuals against the most significant threats to their financial security in a newly financialized, credit-oriented economy.

Click Fraud

Click fraud is the practice of deceptively clicking on search ads with the intention of either increasing third-party website revenues or exhausting an advertiser's budget. Search advertisers are forced to trust that search engines detect and prevent click fraud even though the engines get paid for every undetected fraudulent click. We find conditions under which it is in a search engine's interest to allow some click fraud. Under full information in a second-price auction, if x% of clicks are fraudulent, advertisers will lower their bids by x%, leaving the auction outcome and search engine revenues unchanged. However, if we allow for uncertainty in the amount of click fraud or change the auction type to include a click-through component, search engine revenues may rise or fall with click fraud. A decrease occurs when the keyword auction is relatively competitive because advertisers lower their budgets to hedge against downside risk. If the keyword auction is less competitive, click fraud may transfer surplus from the winning advertiser to the search engine. Our results suggest that the search advertising industry would benefit from using a neutral third party to audit search engines' click fraud detection algorithms.