A discrete choice approach to model credit card fraud

This paper analyses the demographic, socio-economics and banking specific determinants that influence the risk of fraud in a portfolio of credit cards. The data are from recent account archives for cards issued throughout Italy. A logit framework is employed that incorporates cards at a risk of fraud as the dependent variable and a set of explanatory variables (e.g. gender, location, credit line, number of transactions in euros and in non euros currency). The empirical results provide useful indicators on the factors that are responsible for potential risk of fraud.credit card; fraud; demographic and socio-economics factors; logit modelling.

Consumer finance: challenges for operational research

Consumer finance has become one of the most important areas of banking, both because of the amount of money being lent and the impact of such credit on global economy and the realisation that the credit crunch of 2008 was partly due to incorrect modelling of the risks in such lending. This paper reviews the development of credit scoring—the way of assessing risk in consumer finance—and what is meant by a credit score. It then outlines 10 challenges for Operational Research to support modelling in consumer finance. Some of these involve developing more robust risk assessment systems, whereas others are to expand the use of such modelling to deal with the current objectives of lenders and the new decisions they have to make in consumer finance. <br/

Operations research in consumer finance: challenges for operational research

Consumer finance has become one of the most important areas of banking both because of the amount of money being lent and the impact of such credit on the global economy and the realisation that the credit crunch of 2008 was partly due to incorrect modelling of the risks in such lending. This paper reviews the development of credit scoring,-the way of assessing risk in consumer finance- and what is meant by a credit score. It then outlines ten challenges for Operational Research to support modelling in consumer finance. Some of these are to developing more robust risk assessment systems while others are to expand the use of such modelling to deal with the current objectives of lenders and the new decisions they have to make in consumer financ

Ensemble of Example-Dependent Cost-Sensitive Decision Trees

Several real-world classification problems are example-dependent cost-sensitive in nature, where the costs due to misclassification vary between examples and not only within classes. However, standard classification methods do not take these costs into account, and assume a constant cost of misclassification errors. In previous works, some methods that take into account the financial costs into the training of different algorithms have been proposed, with the example-dependent cost-sensitive decision tree algorithm being the one that gives the highest savings. In this paper we propose a new framework of ensembles of example-dependent cost-sensitive decision-trees. The framework consists in creating different example-dependent cost-sensitive decision trees on random subsamples of the training set, and then combining them using three different combination approaches. Moreover, we propose two new cost-sensitive combination approaches; cost-sensitive weighted voting and cost-sensitive stacking, the latter being based on the cost-sensitive logistic regression method. Finally, using five different databases, from four real-world applications: credit card fraud detection, churn modeling, credit scoring and direct marketing, we evaluate the proposed method against state-of-the-art example-dependent cost-sensitive techniques, namely, cost-proportionate sampling, Bayes minimum risk and cost-sensitive decision trees. The results show that the proposed algorithms have better results for all databases, in the sense of higher savings.Comment: 13 pages, 6 figures, Submitted for possible publicatio

HETEROSCEDASTIC DISCRIMINANT ANALYSIS COMBINED WITH FEATURE SELECTION FOR CREDIT SCORING

Credit granting is a fundamental question and one of the most complex tasks that every credit institution is faced with. Typically, credit scoring databases are often large and characterized by redundant and irrelevant features. An effective classification model will objectively help managers instead of intuitive experience. This study proposes an approach for building a credit scoring model based on the combination of heteroscedastic extension (Loog, Duin, 2002) of classical Fisher Linear Discriminant Analysis (Fisher, 1936, Krzyśko, 1990) and a feature selection algorithm that retains sufficient information for classification purpose. We have tested five feature subset selection algorithms: two filters and three wrappers. To evaluate the accuracy of the proposed credit scoring model and to compare it with the existing approaches we have used the German credit data set from the study (Chen, Li, 2010). The results of our study suggest that the proposed hybrid approach is an effective and promising method for building credit scoring models

Credit-worthiness Prediction in Microfinance using Mobile Data: A Spatio-network Approach

Many communities in underdeveloped and developing economies of the world suffer from lack of access to personal credit via formal financial institutions, like banks. However, with the rapid increase in Internet and mobile phone penetration rates, firms are now trying to circumvent this problem using novel technology-enabled approaches. In this research, we leverage a real-world dataset obtained in collaboration with a microfinance firm to show that locational data from mobile phones, coupled with information about communication networks, can be effectively exploited to improve prediction of loan default rates. Specifically, we draw upon recent work in network cohesion based regression modeling to develop a model that uses locational predictors, but within a networked context. We contend that the results from our research can not only illuminate how locational data might be used in assessing creditworthiness, but also empower microfinance firms in resource-poor communities with novel methods for credit scoring

Exploring the Barriers towards the Adoption of Credit Cards Usage in Pakistan

Credit card is a widely used financial instrument in consumer financing, facilitating consumers to full fill their number of desires. The purpose of this research is to present an explanatory perception into the current and non-users of credit card. The aim is to recognize the diverse factors that becomes the basis of low adoption and usage rate of credit card among the Pakistani population. Moreover, this paper also aims to highlight the barriers responsible in the diminutive growth of a potential credit card market in Pakistan. This research is explanatory in nature wherein structured questionnaire was used to collect the data. Moreover, non-probability convenience sampling method was adopted as a sampling technique. Furthermore, structured questionnaire was adapted from the base study and distributed among 302 current and non-credit card users. The research indicates that, to create and tap a large market credit card financial institutions should focus on cost and improve the security of credit cards. Findings further reveals that religious belief has a huge impact towards the decline of credit card usage. Consumer bankers dealing with credit card proposition should focus on aggressive brand promotion which will end up in changing the consumer perception. Furthermore, cost/financial charges are highly correlated with the usage of credit card and thus barrier in credit card usage. Therefore, credit cards issuers should keep the stated factors in mind for easy penetration. This research will be instrumental for credit card issuers, advertising agencies responsible for developing the content of credit card communication and can be used in academics to understand this area from both micro and macro perspective. Keywords: Credit Card, Adaptation, Consumer Finance

Modelling credit card customer behaviour

Work Project presented as a partial requirement for Degree of Master of Statistics and Information Management, with a specialization in Information Analysis and ManagementCredit cards have great influence over consumers’ daily lives, mainly because they provide functionalities that other financial products do not. Studies have been performed in order to research over which are the best clients. To put it in other words, which clients spend more money with credit cards. The aim of this study is to understand the behavior of a credit card consumer depending on whether they do or not many payment transactions with a huge amount of money. With this objective a logistic regression model was investigated, based on many potential explanatory variables (sociodemographic variables, customer profile in the company and customer profile in Banco de Portugal). Several diagnosis tests and goodness of fit tools were used to select the final model, which allows to forecast the client type behavior based on 10 variables. Results show that clients who live in Central North and Central region of Portugal, who have Plafond between 1500 and 9000 euros, who are homemaker or student, who receive cashback and who have seniority in the company between 32 and 84 days ago are the best clients for our case study. We expect that with the proposed model, the company1 will know how to appropriately manage each specific client and its needs.Os cartões de crédito têm uma grande influência no dia-a-dia dos consumidores, principalmente porque fornecem benefícios que outros produtos financeiros não oferecem. Alguns estudos foram realizados com o objetivo de pesquisar quais são os melhores clientes. Por outras palavras, quais são os clientes que gastam mais dinheiro com a utilização do cartão de crédito. O objetivo deste estudo é entender o comportamento de um consumidor de cartão de crédito, dependendo se ele faz ou não muitos pagamentos de transações e se os mesmos são de elevado valor. Com este objetivo, foi proposto um modelo de Regressão Logística com base em potenciais variáveis explicativas (como por exemplo variáveis sociodemográficas, perfil do cliente na empresa2 e perfil do cliente no Banco de Portugal). Diversos testes de diagnóstico e ferramentas de “goodness of fit” foram utilizados para selecionar o modelo final, o que permitiu prever o comportamento do tipo de cliente com base em 10 variáveis. Os resultados mostram que os clientes que vivem na região Centro Norte e Centro de Portugal, que têm Plafond entre 1500 e 9000 euros, que são donas de casa ou estudantes, que recebem cashback e que têm uma antiguidade na empresa entre 32 e 84 dias são os melhores clientes para o nosso caso estudo. Esperamos que, com o modelo proposto, a empresa saiba como acompanhar adequadamente cada cliente e as suas necessidades