Number Of Online Transactions Research Articles

DETECTING CREDIT CARD FRAUD USING MACHINE LEARNING

Bank fraud is "The unauthorized use of an individual's confidential information to make purchases or withdraw funds from a user's account." E-commerce is growing rapidly, and the world is moving towards digitization, cashless transactions, the use of credit cards, the number of users is rapidly increasing, and with it the number of frauds associated with it. Due to the development of technology and the increase in the number of online transactions, fraud is also increasing, leading to huge financial losses. Therefore, effective methods to reduce losses are needed. In addition, scammers find ways to steal the user's credit card information by sending fake SMS and calls, as well as by masquerade attacks, phishing attacks, and so on. This article aims to use several machine learning algorithms such as Support Vector Machine (SVM), Decision Tree, Bayesian Belief Networks, Logistic Regression, k-Nearest Neighbor (Knn), and Artificial Neural Network (ANN) to predict the occurrence of fraud. In addition, we differentiate between the implemented supervised machine learning and deep learning methods to distinguish between fraudulent and non-fraudulent transactions.

Online Transaction Fraud Detection Using Efficient Dimensionality Reduction and Machine Learning Techniques

In recent years, there has been a rapid increase in the number of online transactions. Substantial growth has been reported in e-commerce and e-governance in the past few years. Due to this the number of people using online payment methods has also increased. This has led to an exponential rise in the number of transactions that happen every day. This increase in online transactions has further led to an increase in the number of frauds in the transactions. There is an ever-growing need to detect these fraudulent transactions as early as possible so that appropriate actions could be taken and losses due to these frauds could be minimized. This work proposes machine learning models which could use the previously known data and try to predict frauds based on information learned through the old data. We propose a statistical based dimensionality reduction technique and various machine learning models were tried for classification purpose. We experimented our proposed method on IEEE-CIS Fraud Detection dataset and the best results were obtained on the XGBoost model which is demonstrated in this paper.

Tuning Machine Learning Models Using a Group Search Firefly Algorithm for Credit Card Fraud Detection

Recent advances in online payment technologies combined with the impact of the COVID-19 global pandemic has led to a significant escalation in the number of online transactions and credit card payments being executed every day. Naturally, there has also been an escalation in credit card frauds, which is having a significant impact on the banking institutions, corporations that issue credit cards, and finally, the vendors and merchants. Consequently, there is an urgent need to implement and establish proper mechanisms that can secure the integrity of online card transactions. The research presented in this paper proposes a hybrid machine learning and swarm metaheuristic approach to address the challenge of credit card fraud detection. The novel, enhanced firefly algorithm, named group search firefly algorithm, was devised and then used to a tune support vector machine, an extreme learning machine, and extreme gradient-boosting machine learning models. Boosted models were tested on the real-world credit card fraud detection dataset, gathered from the transactions of the European credit card users. The original dataset is highly imbalanced; to further analyze the performance of tuned machine learning models, in the second experiment performed for the purpose of this research, the dataset has been expanded by utilizing the synthetic minority over-sampling approach. The performance of the proposed group search firefly metaheuristic was compared with other recent state-of-the-art approaches. Standard machine learning performance indicators have been used for the evaluation, such as the accuracy of the classifier, recall, precision, and area under the curve. The experimental findings clearly demonstrate that the models tuned by the proposed algorithm obtained superior results in comparison to other models hybridized with competitor metaheuristics.

Real-Time Credit Card Fraud Detection Using Machine Learning

Credit card fraud occurs often and results in significant financial losses [1]. The number of online transactions has increased dramatically, and online shopping has become increasingly popular. Credit card transactions account for a significant portion of these transactions. As a result, banks and financial institutions provide services. credit card fraud detection software has a lot of utility. demand. Fraudulent transactions can take many forms. and can be classified into several types. The subject of this paper is four main fraud occasions in real-world transactions. Each a series of machine learning models are used to combat fraud. An evaluation is used to choose the optimal method. This assessment gives you a step-by-step approach to picking the right company. With regard to the type of frauds and the appropriate method with a suitable performance, we demonstrate the evaluation. Real-time credit card fraud detection is another important aspect of our project. As a result, we Consider the implementation of predictive analytics to determine if a machine learning model and an API module are appropriate. Is a specific transaction authentic or fraudulent? We also evaluate an innovative technique for dealing with the skewed data distribution. The information we used in our research came from a variety of sources according to an open source and community- maintained website. Keywords: Credit Card, Fraud Detection Software, Open Source, Realtime credit card fraud, Skewed Data Distribution

Aging and Financial Capacity: Cognitive Correlates of an Early Indicator of Functional Decline

Financial capacity describes the ability to make and carry out sound financial decisions sufficient to meet an individual’s needs for health and well-being. Impairments in financial capacity have been shown to be one of the earliest functional changes in patients with mild cognitive impairment (MCI), the precursor to Alzheimer’s disease and related dementias. Recent developments using online automated monitoring of financial transactions promise a new way to identify the earliest signs of cognitive decline. We examine the feasibility of using secure online technology to link ongoing financial activity monitoring data with other objective measures of function and cognition in a cohort of independent living older adults. To date, 73 older adults (mean age = 76.8, MoCA = 25.9) have enrolled and are participating in a 12-month online financial monitoring program that tracks account activity and generates alerts for unusual or irregular transactions. At baseline participants are administered a battery of neuropsychological tests and the Financial Capacity Instrument (FCI), a measure of financial capacity using tasks of everyday financial activity. Financial monitoring data are collected continuously, and are summarized and reported monthly. Younger participants had more online transactions and higher FCI scores. FCI total score was positively correlated with animal fluency (p < .02), Trails A (p < .03) and B (p < .0001), and visual memory (p < .0008). Number of online transactions in one month was correlated with FCI score, and Trails B (faster time to completion). Lower MoCA scores were associated with higher number of alerts per month.

New E-Commerce Business Models can Boost its Development: Case of North Macedonia

The Internet and new technology has transformed many business models and made new to appear. The advent of e-commerce has led to the creation of many new business models and affected the way of shopping goods and services. A new interesting business model that emerged in 2008 is the group buying model. It was launched by Groupon, and very soon the concept attracted interest around the world and spread through the clones in many countries all over the world. In North Macedonia Grouper introduced this concept at the beginning of 2011, and few more group buying web sites emerged soon. The new business model initiated the revolution of the e-commerce in the country, a field that was rather disappointing so far. The goal of this article is to show that attractiveness of the deals offered by group buying site can influence the online shopping and ecommerce development. Secondary data research from relevant sources is done. The number of online transactions in 2011 rose for over 1200 % comparing to 2010 and one-third of these transactions were made by group buying sites. In the following years the number of Internet users who have made orders online constantly increases.

ONLINE SHOPPING AND FOLKLORE - A REVIEW: WITH SPECIAL REFERENCE TO INDIA

The purpose of this research paper is to classify factors affecting the online shopping in India. The weighty growth of online shopping is noticeable in India. More and more consumer is transferring to online shopping rather than traditional buying methods. Many myths and misconceptions emerge in India regarding online shopping. Every new thing, when it is not cleared by all the people properly, it is only a nightmare. Online shopping is intricate in nature. As we know the number of online transactions have been increased, but it is still under the dark room. Government and E-commerce companies should take necessary steps for reducing the level of misconceptions and myths of online shopping in India. This research paper’s purpose is to identifying ‘How’ the companies can be able to overcome these fake information regarding online shopping. Also improve company’s sales figure, for the upliftment of company as well as an Indian economy.

A Model of Vertical Differentiation for Online Retailing

The Internet has changed the nature of doing business as well as the nature of competition in many industries. The number of online transactions has seen a steady increase as more consumers turn to online retailing (e-tailing) for a wide range of product choices and shopping convenience. Yet, despite the availability of online comparison shopping sites and the ease of finding alternative e-tailers for a given product, the persistent and substantial price dispersion has not been fully understood. In this paper, we view the online purchase of a product as a bundle of utilities consisting of the satisfaction with the product itself, the service received from the purchase experience, as well as the assurance and trust associated with the e-tailer's brand recognition. E-tailers, accordingly, can differentiate themselves in the service, brand recognition, and price dimensions. We extend the two dimensional competitive model to three dimensions in which e-tailers sequentially decide on a recognition level, then a service quality, and finally compete on price. We solve the model analytically and obtain closed-form equilibrium solutions. By explicitly incorporating differentiation costs, we show the existence of previously unknown equilibria, among which an asymmetric Max-Max equilibrium that is frequently observed in practice. In addition, the e-tailer's optimal competitive strategy is determined by the structure of the differentiation costs.

Design and Implementation of SeTiA: Secure Multi Auction System II: Architecture and Implementation Issues

In this two-part paper, we describe the design and implementation of a secure online e-auction system called SeTiA (Secure TIFR Auction System). Various protocols have been developed/adapted to cater to different auction mechanisms, negotiations, payments, certified email, etc. Specifically, the system includes the classical auctions (English, Dutch, sealed bid first and second price), multi-unit bids, combinational (bundle) auctions and call markets (wherein buyers and sellers are matched repeatedly over time). Further, it provides features such as bid confidentiality (open or sealed), bid cancellations, identity confidentiality and auction closure (expiration time or timeout). With the growing number of online transactions over the Internet, security of negotiations and transactions is a serious issue. In this system, we achieve various levels of security using the SPKI/SDSI frameworks as well as secure payment systems. In the second part of the paper, we shall discuss the overall architecture and the auction protocols used in SeTiA to achieve a secure multi e-auction system for a spectrum of auction schemes.

Design and Implementation of SeTiA: Secure Multi Auction System I: Auction Mechanisms

In this two part paper, we describe the design and implementation of a secure online e-auction system called SeTiA (Secure TIFR Auction System). Various protocols have been developed/adapted to cater to different auction mechanisms, negotiations, payments, certified email, etc. Specifically, the system includes the classical auctions (English, Dutch, sealed bid first and second price), multi-unit bids, combinational (bundle) auctions and call markets (wherein buyers and sellers are matched repeatedly over time). Further, it provides features such as bid confidentiality (open or sealed), bid cancellations, identity confidentiality, auction closure (expiration time or timeout). With the growing number of online transactions over the Internet, security of negotiations and transactions is a serious issue. In this system, we achieve various levels of security using the SPKI/SDSI frameworks as well as secure payment systems. In the first part of the paper, we shall discuss new algorithmic mechanisms for multi-unit and combinational auctions, along with matching and pricing issues.