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Journal ArticleDOI

Credit card fraud detection using neural network and geolocation

Aman Gulati1, Prakash Dubey1, C MdFuzail1, Jasmine Norman1, R. Mangayarkarasi1 
01 Nov 2017-Vol. 263, Iss: 4, pp 042039
TL;DR: The proposed system presents a methodology which facilitates the detection of fraudulent exchanges while they are being processed by means of Behaviour and Locational Analysis(Neural Logic) which considers a cardholder's way of managing money and spending pattern.
Abstract: The most acknowledged payment mode is credit card for both disconnected and online mediums in today's day and age. It facilitates cashless shopping everywhere in the world. It is the most widespread and reasonable approach with regards to web based shopping, paying bills, what's more, performing other related errands. Thus danger of fraud exchanges utilizing credit card has likewise been expanding. In the Current Fraud Detection framework, false exchange is recognized after the transaction is completed. As opposed to the current system, the proposed system presents a methodology which facilitates the detection of fraudulent exchanges while they are being processed, this is achieved by means of Behaviour and Locational Analysis(Neural Logic) which considers a cardholder's way of managing money and spending pattern. A deviation from such a pattern will then lead to the system classifying it as suspicious transaction and will then be handled accordingly.
Citations
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Journal ArticleDOI
01 Jan 2018
TL;DR: A dense review of up-to-date techniques for fraud detection and credit scoring, a general analysis on the results achieved and upcoming challenges for further researches is provided.
Abstract: Credit risk has been a widespread and deep penetrating problem for centuries, but not until various credit derivatives and products were developed and novel technologies began radically changing the human society, have fraud detection, credit scoring and other risk management systems become so important not only to some specific firms, but to industries and governments worldwide. Frauds and unpredictable defaults cost billions of dollars each year, thus, forcing financial institutions to continuously improve their systems for loss reduction. In the past twenty years, amounts of studies have proposed the use of data mining techniques to detect frauds, score credits and manage risks, but issues such as data selection, algorithm design, and hyperparameter optimization affect the perceived ability of the proposed solutions and it is difficult for auditors and researchers to explore and figure out the highest level of general development in this area. In this survey we focus on a state of the art survey of recently developed data mining techniques for fraud detection and credit scoring. Several outstanding experiments are recorded and highlighted, and the corresponding techniques, which are mostly based on supervised learning algorithms, unsupervised learning algorithms, semisupervised algorithms, ensemble learning, transfer learning, or some hybrid ideas are explained and analysed. The goal of this paper is to provide a dense review of up-to-date techniques for fraud detection and credit scoring, a general analysis on the results achieved and upcoming challenges for further researches.

34 citations

20 Apr 2019
TL;DR: A real-world dataset (European Credit card), with PCA transformation applied is being used, and it is found that the regression technique has the best performance among the others.
Abstract: Credit card fraud is increasing considerably with the development of modern technology and the global superhighways of communication. Credit card fraudsters continuously try to come out with a new tactic challenged the present technology and system. It cost both, providers and consumers a lot of money. Thus, quick and accurate model become essential for companies and credit card providers, to decrease their financial and customer trust losses. However, there is a lack of published literature on credit card fraud detection techniques, due to the unlabeled credit card transactions dataset for researchers. High dimensional data refer to data that have multiple variables. The dataset consist of the credit card details, amount transaction, location, time, and personal details of the cardholders that are anonymized. Thus, in this study, a real-world dataset (European Credit card), with PCA transformation applied is being used. The common problem happened in this kind of research is the data tend to be imbalanced. Imbalanced data will often introduce bias which the accuracy of the prediction is not accurate. In this study, the dataset has been train with an oversampling pre-processing technique called SAS Sample and various data mining technique such as Random Forest, KNN, Decision Tree, and Logistic Regression. After several trials, we found out that the regression technique has the best performance among the others.

8 citations


Cites background from "Credit card fraud detection using n..."

  • ...Credit card fraud refers to the misuse of information or physical credit card by another person without the owner’s acknowledgment [14]....

    [...]

Journal ArticleDOI
C. Sudha1, D. Akila1
TL;DR: In this technique, the behaviour, operational and transactional features of users are combined into a single feature to design a majority vote ensemble classifier for accurate detection of credit card frauds.

8 citations

Journal ArticleDOI
TL;DR: This paper used asexual reproduction optimization (ARO) algorithm to classify the bank transactions into fraud and legitimate, and achieved a high precision value indicating that if ARO detects a record as a fraud, with a high probability, it is a fraud one.
Abstract: Purpose – The best algorithm that was implemented on this Brazilian dataset was artificial immune system (AIS) algorithm. But the time and cost of this algorithm are high. Using asexual reproduction optimization (ARO) algorithm, the authors achieved better results in less time. So the authors achieved less cost in a shorter time. Their framework addressed the problems such as high costs and training time in credit card fraud detection. This simple and effective approach has achieved better results than the best techniques implemented on our dataset so far. The purpose of this paper is to detect credit card fraud using ARO. Design/methodology/approach – In this paper, the authors used ARO algorithm to classify the bank transactions into fraud and legitimate. ARO is taken from asexual reproduction. Asexual reproduction refers to a kind of production in which one parent produces offspring identical to herself. In ARO algorithm, an individual is shown by a vector of variables. Each variable is considered as a chromosome. A binary string represents a chromosome consisted of genes. It is supposed that every generated answer exists in the environment, and because of limited resources, only the best solution can remain alive. The algorithm starts with a random individual in the answer scope. This parent reproduces the offspring named bud. Either the parent or the offspring can survive. In this competition, the one which outperforms in fitness function remains alive. If the offspring has suitable performance,it will be the next parent, and the current parent becomes obsolete.Otherwise, the offspring perishes, and the present parent survives. The algorithm recurs until the stop condition occurs. Findings – Results showed that ARO had increased the AUC (i.e. area under a receiver operating characteristic (ROC) curve), sensitivity, precision, specificity and accuracy by 13%, 25%, 56%, 3% and 3%, in comparison with AIS, respectively. The authors achieved a high precision value indicating that if ARO detects a record as a fraud, with a high probability, it is a fraud one. Supporting a real-time fraud detection system is another vital issue. ARO outperforms AIS not only in the mentioned criteria, but also decreases the training time by 75% in comparison with the AIS, which is a significant figure. Originality/value – In this paper, the authors implemented the ARO in credit card fraud detection. The authors compared the results with those of the AIS, which was one of the best methods ever implemented on the benchmark dataset. The chief focus of the fraud detection studies is finding the algorithms that can detect legal transactions from the fraudulent ones with high detection accuracy in the shortest time and at a low cost. That ARO meets all these demands.

4 citations

Book ChapterDOI
01 Jan 2020
TL;DR: The aim of this paper is to review selected machine learning techniques where it can be used to develop a fraud detection system which identifies fraudulent activities in financial transactions.
Abstract: Fraud is a costly business problem which causes every organization to face huge loss. Fraud may lead to risk of financial loss and loss of the confidence of customers and stakeholders of the company. Cyber security teams and internal audit departments of most of the organizations try to monitor such fraudulent activities using traditional rule-based fraud detection systems. However, with the rapid adaptation of online financial transactions, it is more difficult to identify fraudulent activities by static methods and via data analysis. Further, as traditional rule-based fraud detection systems cannot dynamically adjust the rule set based on the behavioral changes of the fraudsters, there is a high possibility of detecting false positive alerts. The aim of this paper is to review selected machine learning techniques where it can be used to develop a fraud detection system which identifies fraudulent activities in financial transactions.

3 citations

References
More filters
Journal ArticleDOI
TL;DR: This paper presents a general introduction and discussion of recent applications of the multilayer perceptron, one type of artificial neural network, in the atmospheric sciences.

2,389 citations

Journal ArticleDOI
TL;DR: This paper model the sequence of operations in credit card transaction processing using a hidden Markov model (HMM) and shows how it can be used for the detection of frauds and compares it with other techniques available in the literature.
Abstract: Due to a rapid advancement in the electronic commerce technology, the use 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. In this paper, we model the sequence of operations in credit card transaction processing using a hidden Markov model (HMM) and show how it can be used for the detection of frauds. An HMM is initially trained with the normal behavior of a cardholder. If an incoming credit card transaction is not accepted by the trained HMM with sufficiently high probability, it is considered to be fraudulent. At the same time, we try to ensure that genuine transactions are not rejected. We present detailed experimental results to show the effectiveness of our approach and compare it with other techniques available in the literature.

430 citations


"Credit card fraud detection using n..." refers background in this paper

  • ...The hidden markov model implementation could be found in [4-6]....

    [...]

Journal ArticleDOI
TL;DR: Extensive simulation with stochastic models shows that fusion of different evidences has a very high positive impact on the performance of a credit card fraud detection system as compared to other methods.

323 citations

Journal ArticleDOI
TL;DR: A novel combination of the two well known meta-heuristic approaches, namely the genetic algorithms and the scatter search is suggested, which improves a credit card fraud detection solution currently being used in a bank.
Abstract: In this study we develop a method which improves a credit card fraud detection solution currently being used in a bank. With this solution each transaction is scored and based on these scores the transactions are classified as fraudulent or legitimate. In fraud detection solutions the typical objective is to minimize the wrongly classified number of transactions. However, in reality, wrong classification of each transaction do not have the same effect in that if a card is in the hand of fraudsters its whole available limit is used up. Thus, the misclassification cost should be taken as the available limit of the card. This is what we aim at minimizing in this study. As for the solution method, we suggest a novel combination of the two well known meta-heuristic approaches, namely the genetic algorithms and the scatter search. The method is applied to real data and very successful results are obtained compared to current practice.

183 citations

Journal Article
TL;DR: The proposals made in this paper are likely to have beneficial attributes in terms of cost savings and time efficiency and the significance of the application of the techniques reviewed here is in the minimization of credit card fraud.
Abstract: Fraud is one of the major ethical issues in the credit card industry. 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. The sub-aim is to present, compare and analyze recently published findings in credit card fraud detection. This article defines common terms in credit card fraud and highlights key statistics and figures in this field. Depending on the type of fraud faced by banks or credit card companies, various measures can be adopted and implemented. The proposals made in this paper are likely to have beneficial attributes in terms of cost savings and time efficiency. The significance of the application of the techniques reviewed here is in the minimization of credit card fraud. Yet there are still ethical issues when genuine credit card customers are misclassified as fraudulent.

153 citations