Distance Based Pattern Driven Mining for Outlier Detection in High Dimensional Big Dataset

Detection of outliers or anomalies is one of the vital issues in pattern-driven data mining. Outlier detection detects the inconsistent behavior of individual objects. It is an important sector in the data mining field with several different applications such as detecting credit card fraud, hacking discovery and discovering criminal activities. It is necessary to develop tools used to uncover the critical information established in the extensive data. This paper investigated a novel method for detecting cluster outliers in a multidimensional dataset, capable of identifying the clusters and outliers for datasets containing noise. The proposed method can detect the groups and outliers left by the clustering process, like instant irregular sets of clusters (C) and outliers (O), to boost the results. The results obtained after applying the algorithm to the dataset improved in terms of several parameters. For the comparative analysis, the accurate average value and the recall value parameters are computed. The accurate average value is 74.05% of the existing COID algorithm, and our proposed algorithm has 77.21%. The average recall value is 81.19% and 89.51% of the existing and proposed algorithm, which shows that the proposed work efficiency is better than the existing COID algorithm.

Gotta CAPTCHA ’Em All: A Survey of 20 Years of the Human-or-computer Dilemma

A recent study has found that malicious bots generated nearly a quarter of overall website traffic in 2019 [102]. These malicious bots perform activities such as price and content scraping, account creation and takeover, credit card fraud, denial of service, and so on. Thus, they represent a serious threat to all businesses in general, but are especially troublesome for e-commerce, travel, and financial services. One of the most common defense mechanisms against bots abusing online services is the introduction of Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA), so it is extremely important to understand which CAPTCHA schemes have been designed and their actual effectiveness against the ever-evolving bots. To this end, this work provides an overview of the current state-of-the-art in the field of CAPTCHA schemes and defines a new classification that includes all the emerging schemes. In addition, for each identified CAPTCHA category, the most successful attack methods are summarized by also describing how CAPTCHA schemes evolved to resist bot attacks, and discussing the limitations of different CAPTCHA schemes from the security, usability, and compatibility point of view. Finally, an assessment of the open issues, challenges, and opportunities for further study is provided, paving the road toward the design of the next-generation secure and user-friendly CAPTCHA schemes.

Prediction of Credit Card Approval

Credit risk as the board in banks basically centers around deciding the probability of a customer's default or credit decay and how expensive it will end up being assuming it happens. It is important to consider major factors and predict beforehand the probability of consumers defaulting given their conditions. Which is where a machine learning model comes in handy and allows the banks and major financial institutions to predict whether the customer, they are giving the loan to, will default or not. This project builds a machine learning model with the best accuracy possible using python. First we load and view the dataset. The dataset has a combination of both mathematical and non-mathematical elements, that it contains values from various reaches, in addition to that it contains a few missing passages. We preprocess the dataset to guarantee the AI model we pick can make great expectations. After the information is looking great, some exploratory information examination is done to assemble our instincts. Finally, we will build a machine learning model that can predict if an individual's application for a credit card will be accepted. Using various tools and techniques we then try to improve the accuracy of the model. This project uses Jupyter notebook for python programming to build the machine learning model. Using Data Analysis and Machine Learning, we attempted to determine the most essential parameters for obtaining credit card acceptance in this project. The machine learning model we built gave an 86 % accuracy for predicting whether the credit card will be approved or not, considering the various factors mentioned in the application of the credit card holder. Even though we achieved an accuracy of 86%, we conducted a grid search to see if we could increase the performance even further. However, using both the machine learning models: random forest and logistic regression, the best we could get from this data was 86 percent.

THE USE OF DATA MINING IN DETECTING CREDIT CARD FRAUD

Abstract— Credit card fraud is a serious problem for e-commerce retailers with UK merchants reporting losses of $574.2M in 2020. As a result, effective fraud detection systems must be in place to ensure that payments are processed securely in an online environment. From the literature, the detection of credit card fraud is challenging due to dataset imbalance (genuine versus fraudulent transactions), real-time processing requirements, and the dynamic behavior of fraudsters and customers. It is proposed in this paper that the use of machine learning could be an effective solution for combating credit card fraud.According to research, machine learning techniques can play a role in overcoming the identified challenges while ensuring a high detection rate of fraudulent transactions, both directly and indirectly. Even though both supervised and unsupervised machine learning algorithms have been suggested, the flaws in both methods point to the necessity for hybrid approaches.

Prospects and challenges in the investigation of credit card fraud in Vall Region of the Gauteng Province, South Africa

Today use of Credit Card even in developing countries has become a common scenario. People use it to shop, pay bills and for online transactions. But with increase in number of Credit Card users, the cases of fraud in Credit Card have also been on rise. Credit Card related frauds cause globally a loss of billions of Rands. Credit Card fraud can be done in numerous ways. The article begins with an examination of the extent of the challenge and response by the relevant stakeholders, especially the Criminal Justice System (CJS). This study was carried out utilising a qualitative research approach with a convenience, purposive and snowball sampling techniques. Thirtynine (39) interviews were conducted to solicit the views of the participants and police investigators from Vanderbijlpark, Sebokeng, Sharpeville and Vereeniging police stations, members of the community, and victims of credit card fraud were interviewed. These interviews were analysed according to the phenomenological approach, aided with the inductive Thematic Content Analysis (TCA) to identify the participants’ responses and themes. The findings indicated that the extent of credit card fraud in Vaal region is reaching alarming rates. Based on the findings, the authors provided recommendations such as: police investigators being taken for regular workshops and training on how to investigate sophisticated methods used by perpetrators such as technology, awareness in the society about credit card fraud should be prioritised and enhanced. This study recommends that the CCTV cameras should be installed in the ATM, where cases of credit card are taking place. In addition, the police be visible in the areas which are most prevalent to credit card fraud.

Risk of Adverse Financial Events in Patients With Cancer: Evidence From a Novel Linkage Between Cancer Registry and Credit Records

PURPOSE Although financial toxicity is a growing cancer survivorship issue, no studies have used credit data to estimate the relative risk of financial hardship in patients with cancer versus individuals without cancer. We conducted a population-based retrospective matched cohort study using credit reports to investigate the impact of a cancer diagnosis on the risk of adverse financial events (AFEs). METHODS Western Washington SEER cancer registry (cases) and voter registry (controls) records from 2013 to 2018 were linked to quarterly credit records from TransUnion. Controls were age-, sex-, and zip code–matched to cancer cases and assigned an index date corresponding to the case's diagnosis date. Cases and controls experiencing past-due credit card payments and any of the following AFEs at 24 months from diagnosis or index were compared, using two-sample z tests: third-party collections, charge-offs, tax liens, delinquent mortgage payments, foreclosures, and repossessions. Multivariate logistic regression models were used to evaluate the association of cancer diagnosis with AFEs and past-due credit payments. RESULTS A total of 190,722 individuals (63,574 cases and 127,148 controls, mean age 66 years) were included. AFEs (4.3% v 2.4%, P < .0001) and past-due credit payments (2.6% v 1.9%, P < .0001) were more common in cases than in controls. After adjusting for age, sex, average baseline credit line, area deprivation index, and index/diagnosis year, patients with cancer had a higher risk of AFEs (odds ratio 1.71; 95% CI, 1.61 to 1.81; P < .0001) and past-due credit payments (odds ratio 1.28; 95% CI, 1.19 to 1.37; P < .0001) than controls. CONCLUSION Patients with cancer were at significantly increased risk of experiencing AFEs and past-due credit card payments relative to controls. Studies are needed to investigate the impact of these events on treatment decisions, quality of life, and clinical outcomes.

Trust Perception and Payment Method on Marketplace in Indonesia (Study on Shopee)

The rapid development of e-commerce in Indonesia has led to the emergence of competition. One of the e-commerce platforms that is aggressive in promoting and widely used by Indonesian consumers is Shopee. Shopee conducts promotions in Indonesia to bring new shopping experiences to facilitate easy sales and provide shoppers with secure payment processes and integrated logistics arrangements.The research purpose is to determine reputation’s effect, privacy, size, safeguard, web familiarity, and ease in creating a positive effect on confidence; and trust variable will have a positive effect on payment methods using the electronic payment system (EPS), credit cards and cash on delivery on product purchases at online shops in Indonesia. The research methodology uses quantitative methods with a cross-sectional research design by distributing questionnaires online with a Google Form, with 248 respondents. The study led to the findings of the perceptions of trust significantly influenced by security, benefits, and convenience, while reputation, privacy, size, and web familiarization do not affect trust. Furthermore, trust affects the electronic payment method (EPS), credit card, and cash on delivery. The limitation of the study is in the sampling method which covers not all regions in Indonesia. Contribution: as a company, Shopee should maintain security, the benefits, and convenience to develop strategies using electronic payment methods.

Detection of fraudulent credit card transactions: A comparative analysis of data sampling and classification techniques

Every year there is an increasing loss of a huge amount of money due to fraudulent credit card transactions. Recently there is a focus on using machine learning algorithms to identify fraud transactions. The number of fraud cases to non-fraud transactions is very low. This creates a skewed or unbalanced data, which poses a challenge to training the machine learning models. The availability of a public dataset for this research problem is scarce. The dataset used for this work is obtained from Kaggle. In this paper, we explore different sampling techniques such as under-sampling, Synthetic Minority Oversampling Technique (SMOTE) and SMOTE-Tomek, to work on the unbalanced data. Classification models, such as k-Nearest Neighbour (KNN), logistic regression, random forest and Support Vector Machine (SVM), are trained on the sampled data to detect fraudulent credit card transactions. The performance of the various machine learning approaches are evaluated for its precision, recall and F1-score. The classification results obtained is promising and can be used for credit card fraud detection.