Credit Card Security System and Fraud Detection Algorithm

Credit card fraud is one of the most critical threats affecting individuals and companies worldwide, particularly with the growing number of financial transactions involving credit cards every day. The most common threats are likely to come from database breaches and identity theft. All these threats threat put the security of financial transactions at severe risk and require a fundamental solution. This dissertation aims to suggest a secure online payment system that significantly improves credit card security. Our system can be particularly resilient to potential cyber-attacks, unauthorized users, man-in-the-middle, and guessing attacks for credit card number generation or illegal financial activities by utilizing a secure communication channel between the cardholder and server. Our system uses a shared secret and a verification token that allow both sides to communicate through encrypted information. Furthermore, our system is designed to generate a one-time credit card number at the user’s machine that is verified by the server without sharing the credit card number over the network. Our approach combines the machine learning (ML) algorithms with unique temporary credit card numbers in one integrated system, which is the first approach in the online credit card protection system. The new security system generates a one-time-use credit card number for each transaction with a predetermined amount of money. Simultaneously, the system can detect potential fraud utilizing ML algorithm with new critical features such as the IMEI or I.P. address, the transaction’s location, and other features. The contribution of this research is two-fold: (1) a method is proposed to generate a unique, authenticatable one-time credit card number to effectively defend against the database breaches, and (2) a credit card fraud prevention system is proposed with multiple security layers that are achieved by the integration of authentication, ML-based fraud detection, and the one-time credit card number generation. The dissertation improves consumers’ trust and confidence in the credit card system’s security and enhances satisfaction with credit cards’ various financial transactions. Further, the system uses the current online credit card infrastructure; hence it can be implemented without tangible infrastructure cost

Credit risk evaluation by using nearest subspace method

AbstractIn this paper, a classification method named nearest subspace method is applied for credit risk evaluation. Virtually credit risk evaluation is a very typical classification problem to identify “good” and “bad” creditors. Currently some machine learning technologies, such as support vector machine (SVM), have been discussed widely in credit risk evaluation. But there are many effective classification methods in pattern recognition and artificial intelligence have not been tested for credit evaluation. This paper presents to use nearest subspace classification method, a successful face recognition method, for credit evaluation. The nearest subspace credit evaluation method use the subspaces spanned by the creditors in same class to extend the training set, and the Euclidean distance from a test creditor to the subspace is taken as the similarity measure for classification, then the test creditor belongs to the class of nearest subspace. Experiments on real world credit dataset show that the nearest subspace credit risk evaluation method is a competitive method

Predictive Modelling of Retail Banking Transactions for Credit Scoring, Cross-Selling and Payment Pattern Discovery

Evaluating transactional payment behaviour offers a competitive advantage in the modern payment ecosystem, not only for confirming the presence of good credit applicants or unlocking the cross-selling potential between the respective product and service portfolios of financial institutions, but also to rule out bad credit applicants precisely in transactional payments streams. In a diagnostic test for analysing the payment behaviour, I have used a hybrid approach comprising a combination of supervised and unsupervised learning algorithms to discover behavioural patterns. Supervised learning algorithms can compute a range of credit scores and cross-sell candidates, although the applied methods only discover limited behavioural patterns across the payment streams. Moreover, the performance of the applied supervised learning algorithms varies across the different data models and their optimisation is inversely related to the pre-processed dataset. Subsequently, the research experiments conducted suggest that the Two-Class Decision Forest is an effective algorithm to determine both the cross-sell candidates and creditworthiness of their customers. In addition, a deep-learning model using neural network has been considered with a meaningful interpretation of future payment behaviour through categorised payment transactions, in particular by providing additional deep insights through graph-based visualisations. However, the research shows that unsupervised learning algorithms play a central role in evaluating the transactional payment behaviour of customers to discover associations using market basket analysis based on previous payment transactions, finding the frequent transactions categories, and developing interesting rules when each transaction category is performed on the same payment stream. Current research also reveals that the transactional payment behaviour analysis is multifaceted in the financial industry for assessing the diagnostic ability of promotion candidates and classifying bad credit applicants from among the entire customer base. The developed predictive models can also be commonly used to estimate the credit risk of any credit applicant based on his/her transactional payment behaviour profile, combined with deep insights from the categorised payment transactions analysis. The research study provides a full review of the performance characteristic results from different developed data models. Thus, the demonstrated data science approach is a possible proof of how machine learning models can be turned into cost-sensitive data models

Statistical modelling applied to perceptions of fraud

This study aims to investigate and identify attitudes, behaviours and perceptions harboured by bank cardholders and merchants, which are associated with a higher susceptibility towards experiencing bank card fraud. Primary data was obtained from bank cardholders and merchants, from various business categories, in both the Nelson Mandela Bay Metropolitan Municipality and the City of Johannesburg Metropolitan Municipality. Following the use of parametric Multinomial Logistic Regression (MLR) and nonparametric conditional density estimation to analyse the data, the results are compared and relevant covariates/perceptions are determined from the more accurate of the two techniques. The results of the analysed survey data serve as a tool, highlighting areas which require further education and awareness on the part of merchants and bank clients

Investigation of artificial immune systems and variable selection techniques for credit scoring

Most lending institutions are aware of the importance of having a well-performing credit scoring model or scorecard and know that, in order to remain competitive in the credit industry, it is necessary to continuously improve their scorecards. This is because better scorecards result in substantial monetary savings that can be stated in terms of millions of dollars. Thus, there has been increasing interest in the application of new classifiers in credit scoring from both practitioners and researchers in the last few decades. Most of the recent work in this field has focused on the use of new and innovative techniques to classify applicants as either 'credit-worthy' or 'non-credit-worthy', with the aim of improving scorecard performance. In this thesis, we investigate the suitability of intelligent systems techniques for credit scoring. In particular, intelligent systems that use immunological metaphors are examined and used to build a learning and evolutionary classification algorithm. Our model, named Simple Artificial Immune System (SAIS), is based on the concepts of the natural immune system. The model uses applicants' credit details to classify them as either 'credit-worthy' or 'non-credit-worthy'. As part of the model development, we also investigate several techniques for selecting variables from the applicants' credit details. Variable selection is important as choosing the best set of variables can have a significant effect on the performance of scorecards. Interestingly, our results demonstrate that the traditional stepwise regression variable selection technique seems to perform better than many of the more recent techniques. A further contribution offered by this thesis is a detailed description of the scorecard development process. A detailed explanation of this process is not readily available in the literature and our description of the process is based on our own experiences and discussions with industry credit risk practitioners. We evaluate our model using both publicly available datasets as well as a very large set of real-world consumer credit scoring data obtained from a leading Australian bank. The evaluation results reveal that SAIS is a competitive classifier and is appropriate for developing scorecards which require a class decision as an outcome. Another conclusion reached is one confirmed by the existing literature, that even though more sophisticated scorecard development techniques, including SAIS, perform well compared to the traditional statistical methods, their performances are not statistically significantly different from the statistical methods. As with other intelligent systems techniques, SAIS is not explicitly designed to develop practical scorecards which require the generation of a score that represents the degree of confidence that an applicant will belong to a particular group. However, it is comparable to other intelligent systems techniques which are outperformed by statistical techniques for generating p ractical scorecards. Our final remark on this research is that even though SAIS does not seem to be quite suitable for developing practical scorecards, we still believe that there is room for improvement and that the natural immune system of the body has a number of avenues yet to be explored which could assist with the development of practical scorecards

Credit risk analysis using artificial intelligence : evidence from a leading South African banking institution

Credit risk analysis is an important topic in financial risk management. Financial institutions (e.g. commercial banks) that grant consumers credit need reliable models that can accurately detect and predict defaults. This research investigates the ability of artificial neural networks as a decision support system that can automatically detect and predict “bad” credit risks based on customers demographic, biographic and behavioural characteristics. The study focuses specifically on the learning vector quantization neural network algorithm. This thesis contains a short overview of credit scoring models, an introduction to artificial neural networks and their applications and presents the performance evaluation results of a credit risk detection model based on learning vector quantization networks.Graduate School of Business LeadershipM.B.L

Expert systems in optometry

Expert systems in optometr