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

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

Credit Card Fraud Detection and Identification using Machine Learning Techniques

Fraudulent internet transactions have caused considerable harm and losses for both people and organizations over time. The growth of cutting-edge technology and worldwide connectivity has exacerbated the rise in online fraud instances. To offset these losses, robust fraud detection systems must be developed. ML and statistical approaches are critical components in properly recognizing fraudulent transactions. However, implementing fraud detection models presents challenges such as limited data availability, data sensitivity, and imbalanced class distributions. The confidentiality of records adds complexity to drawing inferences and constructing improved models in this domain. This research explores multiple algorithms suitable for classifying transactions as either genuine or fraudulent using the Credit Card Fraud dataset. Given the extremely unbalanced nature of the dataset, the SMOTE approach was used for oversampling to alleviate the class distribution imbalance. In addition, feature selection was carried out, and the dataset was divided into training and test data. The experiments utilized NB, RF, and MLP algorithms, all of which demonstrated high accuracy in detecting credit card fraud. MLP method achieved 99.95% accuracy as compared to other methods

Fraud Detection and Identification in Credit Card Based on Machine Learning Techniques

Fraudulent internet transactions have caused considerable harm and losses for both people and organizations over time. The growth of cutting-edge technology and worldwide connectivity has exacerbated the rise in online fraud instances. To offset these losses, robust fraud detection systems must be developed. ML and statistical approaches are critical components in properly recognizing fraudulent transactions. However, implementing fraud detection models presents challenges such as limited data availability, data sensitivity, and imbalanced class distributions. The confidentiality of records adds complexity to drawing inferences and constructing improved models in this domain. This research explores multiple algorithms suitable for classifying transactions as either genuine or fraudulent using the Credit Card Fraud dataset. Given the extremely unbalanced nature of the dataset, the SMOTE approach was used for oversampling to alleviate the class distribution imbalance. In addition, feature selection was carried out, and the dataset was divided into training and test data. The experiments utilized NB, RF, and MLP algorithms, all of which demonstrated high accuracy in detecting credit card fraud. MLP method achieved 99.95% accuracy as compared to other method

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

Avoin ja yleispätevä numeeriseen ohjaukseen ja konenäköteknologioihin pohjautuva maksupäätteiden automaattisen hyväksymistestausympäristön arkkitehtuuri

Software testing is a crucial part of modern software development and it is commonly accepted fact that the earlier software defects and errors are found, the lower the cost of correcting those will be. Early detection of errors also increases the possibility to correct them properly. Acceptance testing is a process of comparing the developed program to the initial requirements. Acceptance testing of a system should be executed in an environment as similar as possible to the production environment of the final product. This master's thesis will discuss how to address these in automated acceptance testing environment of payment terminal software. This master's thesis will discuss the theories related to software testing, testing of embedded systems and the challenges related to the topic. Master's thesis will present an architecture for automated acceptance testing of payment terminals including the needed hardware and software.Ohjelmistotestaus on tärkeä osa modernia ohjelmistotuotantoa ja on yleisesti tunnustettu, että mitä aiemmin virheet ohjelmistosta löytyvät, sitä edullisempaa niiden korjaaminen tulee olemaan. Aikainen virheiden havaitseminen myös edesauttaa virheiden perusteellista ja laadukasta korjaamista. Hyväksymistestaus on ohjelmistotestauksen vaihe, jossa kehitettyä ohjelmistoa verrataan alkuperäisiin ohjelmistovaatimuksiin. Ohjelmiston hyväksymistestaus tulisi suorittaa lopullista tuotantoympäristöä mahdollisimman hyvin vastaavassa ympäristössä. Tämä diplomityö käsittelee näitä ohjeistuksia maksupäätteiden automaattisen hyväksymistestauksen ympäristössä. Tämä diplomityö käsittelee ohjelmistotestaukseen liittyvää teoriaa, sulautettujen järjestelmien testausta sekä aiheeseen liittyviä haasteita. Lisäksi diplomityö esittelee ympäristön maksupäätteiden automaattiseen hyväksymistestaukseen ja käsittelee siihen tarvittuja ohjelmistoja ja fyysisiä komponentteja

Credit scoring: a management methodology for the prevention and reduction of bad credit

Classificação JEL: G17, G21, G32 e C13.O crescimento do crédito malparado ou crédito de cobrança duvidosa tem merecido das instituições financeiras uma atenção permanente na melhoria do controlo do risco de crédito. Este controlo visa regular a concessão de crédito segundo práticas que minimizem a probabilidade de incumprimento. Quando a gestão do risco de crédito adopta políticas de crédito mais liberais, a probabilidade de ocorrerem créditos de cobrança duvidosa aumenta. Uma parte do crédito malparado converte-se em incobrável, provocando prejuízos avultados. A constatação deste problema há muito que foi reconhecida e consagrada pelo Acordo de Basileia II (Anexo 1) que entre várias recomendações sugeriu aos Bancos formas mais rigorosas de controlar o risco de crédito. Segundo o European Payment Index (Anexo 3) o risco de pagamento na Europa evidenciou em 2008 um agravamento dos incobráveis, situando-se em 2% do total do crédito concedido. De acordo com este estudo, “Portugal, Grécia e Chipre são os países onde se demora mais tempo a pagar” (EPI 2008, p.4, Anexo 3). Para mitigar este problema têm sido propostas diversas práticas, entre elas a quantificação probabilística do incumprimento traduzida por uma pontuação de risco, cuja identificação na gíria do discurso financeiro se designa por scoring ou credit scoring. Neste contexto, o objectivo deste estudo é identificar factores explicativos capazes de prever a probabilidade de um devedor ser no futuro um Bom ou Mau pagador e avaliar a robustez preditiva do modelo utilizado para este efeito. O projecto de investigação incidiu sobre o crédito ao consumo tendo a identificação daqueles factores explicativos sido feita através da utilização de uma base de dados de 4000 utilizadores de cartões de crédito, cujos hábitos de pagamento se conhecem a priori. A metodologia de investigação empírica seguida neste projecto consistiu na aplicação do modelo de regressão logística binária aos dados em análise, por ser especialmente adequado ao estudo em causa e devido à sua simplicidade. A identificação dos factores explicativos (atributos) mais relevantes foi realizada através do método iterativo forward stepwise (Likelihood Ratio) e que consiste em seleccionar entre as variáveis independentes aquelas cuja capacidade preditiva do comportamento de Bom ou Mau pagador é estatisticamente significativa. A presente tese está estruturada em cinco capítulos: o Capítulo 1 faz a introdução da investigação; o Capítulo 2 trata a Revisão da Literatura; o Capítulo 3 descreve o referencial metodológico; o Capítulo 4 apresenta os resultados da metodologia aplicada; e o Capítulo 5 fecha o estudo com conclusões, contribuições esperadas e sugestões.The increasing of bad debts or credits of doubtful collection has deserved a constant attention from financial institutions in order to improve credit risk control. This control aim to guide credit granting process in accordance with practices that can minimize the probability default (PD). When credit risk management reduces the appraisal risk methods the probability to get more credits of doubtful collections increases. Part of bad debts turns into loans loss provoking huge damages to lenders. The observation of such problem was recognized by Basel II Accord (Attached 1) who among several recommendations, was suggested to Banks to be more accurate in credit granting process and its risk control. According to European Payment Index (Attached 3) the non-payment risk in Europe shown in 2008 an increase of loans losses getting 2% of total credit granting. In that survey “Portugal, Greece and Cyprus are the countries where it (payment) take longest to be paid…” (EPI 2008, p.4, Attached 3). The mitigation of this problem will apply on several practices among them the quantification of a probability default translated by a risk measure, whose identification among financial institutions is known by scoring or credit scoring. In this particular context, the aim of this study is to identify explanatory factors which are able to predict the likelihood of a borrower to be in a near future a Good or Bad payer and to evaluate the predictive robustness of the model used in this application. The research project was focused on consumer credit segment and the identification of above explanatory factors was made through 4000 credit card users data base, whose payment behavior is a priori known. The research methodology followed in this project lay in the application of the binary logistic regression model once this is especially suitable to this study and also due to its simplicity. The identification of the explanatory factors (attributes) has been carried out by forward stepwise (Likelihood Ratio) iterative method. This consists in selecting among the independent variables the most powerful predictive attribute, adding afterwards the following attributes according to their predictive power until no more attributes under certain level of significance were found. The study comprises five chapters: Chapter 1 introduces the subject of the research presenting a review of the work done; Chapter 2 shows the Literature Review presenting some researches using statistical methods on credit scoring methodology; Chapter 3 describes the state of the art of credit scoring processes; Chapter 4 presents the Results of the study and the applied methodology; Chapter 5 makes the Conclusions and Suggestions for further woks. The second part is divided into two chapters dealing with the empirical side: the fourth chapter reports the way how the data was collected, how this was analyzed and transformed in order to be integrated in the statistical model; the fifth chapter deals with the application of logistic regression algorithm in the in-sample set data and a holdout sample was used as a final test of model performanc