scholarly journals Abnormal Detection of Cash-Out Groups in IoT Based Payment

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7507
Author(s):  
Hao Zhou ◽  
Ming Zhang ◽  
Lei Pang ◽  
Jian-Hua Li

With the rise of online/mobile transactions, the cost of cash-out has decreased and the cost of detection has increased. In the world of online/mobile payment in IoT, merchants and credit cards can be applied and approved online and used in the form of a QR code but not a physical card or Point of Sale equipment, making it easy for these systems to be controlled by a group of fraudsters. In mainland China, where the credit card transaction fee is, on average, lower than a retail loan rate, the credit card cash-out option is attractive for people for an investment or business operation, which, after investigation, can be considered unlawful if over a certain amount is used. Because cash-out will incur fees for the merchants, while bringing money to the credit cards’ owners, it is difficult to confirm, as nobody will declare or admit it. Furthermore, it is more difficult to detect cash-out groups than individuals, because cash-out groups are more hidden, which leads to bigger transaction amounts. We propose a new method for the detection of cash-out groups. First, the seed cards are mined and the seed cards’ diffusion is then performed through the local graph clustering algorithm (Approximate PageRank, APR). Second, a merchant association network in IoT is constructed based on the suspicious cards, using the graph embedding algorithm (Node2Vec). Third, we use the clustering algorithm (DBSCAN) to cluster the nodes in the Euclidean space, which divides the merchants into groups. Finally, we design a method to classify the severity of the groups to facilitate the following risk investigation. The proposed method covers 145 merchants from 195 known risky merchants in groups that acquire cash-out from four banks, which shows that this method can identify most (74.4%) cash-out groups. In addition, the proposed method identifies a further 178 cash-out merchants in the group within the same four acquirers, resulting in a total of 30,586 merchants. The results and framework are already adopted and absorbed into the design for a cash-out group detection system in IoT by the Chinese payment processor.

Author(s):  
Kathleen W. Johnson

Abstract I argue that the measure of credit card debt used by researchers has grown rapidly in part because it captures debt arising from transactions in which a credit card is used because of its advantages over other payment instruments. Increases in debt stemming from such use may not signal greater household financial vulnerability if households are willing and able to repay this short-term debt. However, it may suggest that the cost of using credit cards to pay for purchases has declined relative to other payment instruments. I conclude that had transactions demand remained at its real 1992 levels, rather than growing almost 15 percent per year, measured credit card debt would have grown a bit less than 1 percentage point slower per year between 1992 and 2001. Moreover, I show that removing transactions demand from aggregate consumer credit can alter conclusions about the relationship between credit and consumption.


2019 ◽  
Vol 3 (1) ◽  
pp. 1-2
Author(s):  
Marisa Bidois

Hospitality businesses in New Zealand are seeing fewer and fewer payments made by cash, as customers opt for the convenience of paying their bill electronically. If customers love the convenience of paying by credit card, who should be responsible for the cost of this convenience – the business or the customer? In a Restaurant Association survey conducted at the end of last year, members overwhelmingly (71%) indicated that the use of cash by customers is declining, with a Mastercard New Zealand survey last year backing this up. This widespread adoption of electronic payment by consumers sees merchants bearing the significant cost of the transaction through their merchant fees. New Zealand merchants pay substantially more to process credit and contactless debit card transactions than their counterparts in Australia and the UK (on average, New Zealand merchants pay merchant service fees of around 1.4%, while in Australia it is around 0.85%, according to estimates by COVEC and data from the Reserve Bank of Australia). Restaurant Association members typically pay even higher – between 1.8% and 2% in fees for each credit card transaction; members say they are charged the same rate for any card type. Forty-two percent have a ‘fixed bundled rate’, although another 26% say they are charged a split rate for credit card and debit cards. Only 5% have an ‘unbundled’ merchant fee, where different types of cards are charged different fees and merchants pay this cost plus an acquiring service fee from the bank. There are undoubtedly advantages for businesses in accepting electronic payments, primarily in the speed of the transaction – particularly with several customers waiting to pay – and the speed in which the payment is deposited into your bank account. However, it comes at a large cost, which is challenging for an industry that runs on very small margins already. One member pointed out in the Association’s recent survey: As the average return in New Zealand is 6% net profit, the banks are effectively charging 1/3 of the profit of the average business, which is diabolical. With technology advancements their costs have gone down but charges have gone up, clearly shown in their bottom line profits. It is a collective monopoly like a lot of big business in New Zealand. (Restaurant Association member) Of our members, 66% say they would switch if they could receive a saving equating to an overall 2.5–5% reduction in the cost of accepting credit cards. Currently though, short of refusing to accept credit card payments, it is difficult to avoid merchant fees. Emerging payment options and growing trends via NFC (Near Field Communication) capable mobile phones (such as ApplePay, GooglePay and Digital Wallets) are now more widely available. Whilst offering convenience and arguably faster transaction speed, these payment methods offer no relief to the fee incurred by a business for acceptance. Alternative payment solutions now exist in New Zealand, but there are few choices. To date, most are aimed at the Chinese market, with payment methods restricted to tourist and student visitors, and immigrants retaining banking capability in their country of origin. The Restaurant Association’s survey indicated that only 24% of members currently accept other payment channels like China Union Pay, Alipay or WeChat. In reality these alternative payment solutions currently only form a small portion of the total volume of transactions a business processes, so will not affect any meaningful reduction in the total costs of cards/payment processing. Surcharging, however, is a way for operators to offset the merchant fee imposed upon them by the banks. Surcharging simply means a charge to cover a merchant’s cost for processing a credit card. They are now being used by increasing numbers of tourism and hospitality businesses. Feedback from member businesses is that there is little reaction or negative feedback from customers. A Restaurant Association member commented on the survey: We added a surcharge to cover the transaction fee on credit cards and have had no complaints. It’s just a matter of cents and gives us an opportunity to explain that we have always worn the cost of the surcharges but this is increasingly difficult.  Feedback from some members is that they find the practice unfriendly and others would prefer to incorporate this fee into their menu pricing structure, as this member pointed out: “I don’t care about the cost. It is added into the budgets and is picked up at menu price changes time, so it is paid for by the customer anyway.” Individual businesses need to decide if a surcharge would create tension in the business/customer relationship however, it is reassuring to know that, if a business does decide to add a surcharge, it is becoming a far more mainstream option than it used to be. From a legal standpoint, merchants are required under the Fair Trading Act to ensure representations around their card payment fees are accurate and not misleading. This means if you are being charged a 1.8% merchant fee by your bank, it is not reasonable to apply a 3% credit card convenience fee to your customer. We’ve noticed some merchants prefer to pass on only a portion of the cost with a surcharge – say 1% – as a cost recovery practice. For a $100 bill, that is just a $1 addition to the bill for the consumer. The payments landscape is changing rapidly, and in the future new technology will dramatically change the way we pay and receive payments. In the meantime, the Restaurant Association are developing further information for members around surcharging, with implementation and training for staff. We’ll also continue advocating on behalf of members to ensure the payment system delivers good outcomes for both consumers and our member merchants. Corresponding author Marisa Bidois can be contacted at: [email protected]  


In recent times, usage of credit cards has increased exponentially which has given way to an increase in the number of cybercrimes related to transactions using credit cards. In this paper, the aim is to reduce the fraudulent credit card transactions happening around the world. Latest technologies like machine learning algorithms, cloud computing and web service implementation has been used in this paper. The model uses Local outlier factor algorithm and Isolation forest algorithm to develop the credit card fraud detection model using unsupervised learning techniques. The model has been implemented as a Web service to make the solution integratable with other applications and clients across the world. A third party prototype application is developed and integrated to the Fraud Detection Model using Web Services. The complete Fraud Detection System is deployed on the cloud. The Fraud Detection Model shows exceptionally high accuracy when compared to other models already existing.


Credit card fraud is one of the most important problems that financial institutions are currently facing. Although the technology has allowed to increase the security in the credit cards with the use of PIN keys, the introduction of chips in the cards, the use of additional keys such as tokens and improvements in the regulation of its use is also a necessity for banks, to act preventively against this crime. To act preventively, it is necessary to monitor in real time the operations that are carried out and have the ability to react in a timely manner against any doubtful operation that is performed. This paper presents an implementation of automatic credit card fraud detection system using Particle Swarm Optimized Neural Network classifier on Kaggle dataset. The selection of proper attributes for reducing the training overhead and claiming higher accuracy for the fraud detection using soft computing. Performance evaluation is achieved using confusion matrix plot with accuracy, sensitivity and precision values.


Author(s):  
B.A. Abdulsalami ◽  
A. A. Kolawole ◽  
M.A. Ogunrinde ◽  
M Lawal ◽  
R.A. Azeez ◽  
...  

The ubiquitous nature of the internet had been a major driving force of the digital transformation in our world today. It has necessarily become the main medium for conducting electronic commerce (e-commerce) and online transactions. With this development, various means of possible payment methods have also emerged, such as electronic cash/ cheques, debit/credit cards, and electronic wallets. However, debit/credit cards are by far the most common payment methods employed. As a result, different credit card fraud activities have rapidly increased all over the world and are still evolving. This menace has drawn a lot of research interest and a number of techniques, with special emphasis on Data Mining, Expert System and Machine Learning (ML), as a means of identifying fraudulent behaviors. This paper examines and investigates two ML algorithms trained on public online credit card datasets, to analyze and identify fraudulent transactions. The BPNN and the K-means clustering ML algorithms were designed and implemented using Python Programming Languages. It was determined that the BPNN has a much higher accuracy of 93.1% as compared to the K-means which has an accuracy of 79.9%. Other metrics used to evaluate their performance also shows that the BPNN algorithm outperformed K-means algorithm, while the low prediction time of K-means gave it an advantage over the BPNN.


2015 ◽  
Vol 3 (1) ◽  
pp. 51 ◽  
Author(s):  
Zaimy Johana Johan ◽  
Lennora Putit

Many past researches have been carried out in an attempt to continuously understand individuals‟ consumption behaviour. This study was conducted to investigate key factors influencing consumers‟ potential acceptance of halal (or permissible) financial credit card services. Specifically, it anticipated the influence of attitude, social influences and perceived control on consumers‟ behavioural intention to accept such services. In addition, factors such as religiosity and product knowledge were also postulated to affect consumers‟ attitude towards the act of using halal credit cards for any retail or business transactions. Using non-probability sampling approach, a total of 500 survey questionnaires was distributed to targeted respondents in a developing nation but only 220 usable feedbacks were received for subsequent data analysis. Regression results revealed that religiosity and product knowledge significantly influence consumers‟ attitude toward using halal credit card services.  Attitude in turn, subsequently has a significant impact on consumers‟ intention to accept halal financial credit card services. Several theoretical and managerial contributions were observed in this study.   


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sachin Banker ◽  
Derek Dunfield ◽  
Alex Huang ◽  
Drazen Prelec

AbstractCredit 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.


2021 ◽  
pp. 1-18
Author(s):  
Matthew D. Hilchey ◽  
Matthew Osborne ◽  
Dilip Soman

Abstract Regulators require lenders to display a subset of credit card features in summary tables before customers finalize a credit card choice. Some jurisdictions require some features to be displayed more prominently than others to help ensure that consumers are made aware of them. This approach could lead to untoward effects on choice, such that relevant but nonprominent product features do not factor in as significantly. To test this possibility, we instructed a random sample of 1615 adults to choose between two hypothetical credit cards whose features were shown side by side in tables. The sample was instructed to select the card that would result in the lowest financial charges, given a hypothetical scenario. Critically, we randomly varied whether the annual interest rates and fees were made visually salient by making one, both, or neither brighter than other features. The findings show that even among credit-savvy individuals, choice tends strongly toward the product that outperforms the other on a salient feature. As a result, we encourage regulators to consider not only whether a key feature should be made more salient, but also the guidelines regarding when a key feature should be displayed prominently during credit card acquisition.


2020 ◽  
Vol 24 (5) ◽  
Author(s):  
Jinan Liu ◽  
Apostolos Serletis

Abstract We reexamine the effects of the variability of money growth on output, raised by Mascaro and Meltzer (1983), in the era of the increasing use of alternative payments, such as credit cards. Using a bivariate VARMA, GARCH-in-Mean, asymmetric BEKK model, we find that the volatility of the credit card-augmented Divisia M4 monetary aggregate has a statistically significant negative impact on output from 2006:7 to 2019:3. However, there is no effect of the traditional Divisia M4 growth volatility on real economic activity. We conclude that the balance sheet targeting monetary policies after the financial crisis in 2007–2009 should pay more attention on the broad credit card-augmented Divisia M4 aggregate to address economic and financial stability.


Sign in / Sign up

Export Citation Format

Share Document