The thesis is concerning household indebtedness through credit cards, funding of these credits and how the consumer is affected. The focus is mainly on the current situation in the United States, nevertheless some observations from the former Korean credit card crisis are pointed out. The thesis describes the development of the credit card debt and identifies reasons and consequences of the expansion. The role of the state as setting conditions of the market and solving possible market failure is also mentioned.

Vulnerable consumers are likely to fall victim to negative marketplace outcomes due to the secondary effects of marketing practices. In particular, credit card targeting directed towards young people elicits ethical criticisms because of the perceived vulnerability of the target segment, the targeting efforts that are deemed more predatory than informative, and the stigmatising protectionist policies that limit the youths’ financial freedom. Vulnerable consumers are often overlooked in marketing considerations, leaving it to the public policy to intervene. This thesis aims to show that vulnerability is a marketing problem as much as it is a public policy issue, by highlighting the social effects of unethical marketing directed towards vulnerable consumers. The study depicts how young people, supposedly representing the most educated segment of the population, come to experience vulnerability due to credit card misuse and indebtedness. In addition, the study introduces a new concept and measure of susceptibility to credit card misuse and indebtedness (SCCMI) to investigate the extent to which youths are influenced by credit card temptations, which affect their likelihood to experience negative credit card outcomes. This study examines youth vulnerability and susceptibility to credit card misuse and indebtedness in a cross-country context, as the issue of vulnerability and power imbalance is arguably more pressing in the international market. The sampling involves young (18-25 years) credit card users in Singapore, Malaysia and the UK. These countries represent different degrees of credit card issuance and consumer protection regulations, which affect the youths’ credit card attitude and behaviour. The first study utilises qualitative methodology to examine youth vulnerability to credit card misuse. Baker et al.’s (2005) situational framework of actual consumer vulnerability helps to identify relevant themes pertained to the youths’ experience of credit card misuse and indebtedness. The qualitative study also serves as an exploratory phase to the subsequent quantitative study. The qualitative results enhance the conceptualisation and measurement scale development of SCCMI measure. The study then tested the validity, reliability and parsimony of the SCCMI measure and its proposed antecedent and consequent factors across the Malaysian, Singaporean and UK country samples. Vulnerability and susceptibility assessments in this study yield theoretical, methodological and practical implications. Vulnerability analysis draws upon the internal characteristics and external conditions that both facilitate and impede such vulnerability. Meanwhile, assessment of susceptibility provides an analytical tool to foresee and pre-empt future vulnerability. This study offers methodological contributions in its use of mixed methods, as a qualitative inquiry aids in understanding vulnerability while quantitative inquiry focuses on foreseeing potential vulnerability. A cross-country study analysis is valuable as it sheds light on the differences and similarities of consumer vulnerability and susceptibility across developing and developed countries. The study inform marketers that there are negative social consequences arising from unethical targeting practices, which leads to distrust and scepticism over credit card marketing directed towards youths. However, the youths’ experience of vulnerability also varies across individuals, which indicate that protectionist policies that shield the entire youth population from credit card exposure are not always necessary. Both credit card marketers and policy makers have the capacities to redress and pre-empt vulnerability without sacrificing the youths’ financial welfare and rights to harness the benefits that credit cards have to offer.

This study extends outshopping knowledge from North America to Europe. Outshoppers and non-outshoppers in a Finnish town are compared in terms of socioeconomic and demographic characteristics, importance of shopping area choice attributes, perceptions of the local trading area, and purchase localities of products. Implications are drawn and comparisons of results are made to general findings from North America.

According to security.org, as of 2023, 65% of credit card (CC) users in the US have been subjected to fraud at some point in their lives, which equates to about 151 million Americans. The proliferation of advanced machine learning (ML) algorithms has also contributed to detecting credit card fraud (CCF). However, using a single or static ML-based defense model against a constantly evolving adversary takes its structural advantage, which enables the adversary to reverse engineer the defense's strategy over the rounds of an iterated game. This paper proposes an adaptive moving target defense (MTD) approach based on deep reinforcement learning (DRL), termed RESONANT to identify the optimal switching points to another ML classifier for credit card fraud detection. It identifies optimal moments to strategically switch between different ML-based defense models (i.e., classifiers) to invalidate any adversarial progress and always stay a step ahead of the adversary. We take this approach in an iterated game theoretic manner where the adversary and defender take turns to take their action in the CCF detection contexts. Via extensive simulation experiments, we investigate the performance of our proposed RESONANT against that of the existing state-of-the-art counterparts in terms of the mean and variance of detection accuracy and attack success ratio to measure the defensive performance. Our results demonstrate the superiority of RESONANT over other counterparts, including static and naïve ML and MTD selecting a defense model at random (i.e., Random-MTD). Via extensive simulation experiments, our results show that our proposed RESONANT can outperform the existing counterparts up to two times better performance in detection accuracy using AUC (i.e., Area Under the Curve of the Receiver Operating Characteristic (ROC) curve) and system security against attacks using attack success ratio (ASR). / Master of Science / According to security.org, as of 2023, 65% of credit card (CC) users in the US have been subjected to fraud at some point in their lives, which equates to about 151 million Americans. The proliferation of advanced machine learning (ML) algorithms has also contributed to detecting credit card fraud (CCF). However, using a single or static ML-based defense model against a constantly evolving adversary takes its structural advantage, which enables the adversary to reverse engineer the defense's strategy over the rounds of an iterated game. This paper proposes an adaptive defense approach based on artificial intelligence (AI), termed RESONANT, to identify the optimal switching points to another ML classifiers for credit card fraud detection. It identifies optimal moments to strategically switch between different ML-based defense models (i.e., classifiers) to invalidate any adversarial progress and always stay a step ahead of the adversary. We take this approach in an iterated game theoretic manner where the adversary and defender take turns to take their action in the CCF detection contexts. Via extensive simulation experiments, we investigate the performance of our proposed RESONANT against that of the existing state-of-the-art counterparts in terms of the mean and variance of detection accuracy and attack success ratio to measure the defensive performance. Our results demonstrate the superiority of RESONANT over other counterparts, showing that our proposed RESONANT can outperform the existing counterparts by up to two times better performance in detection accuracy and system security against attacks.

近年來，信用卡市場快速地成長，發卡銀行亦大量地發卡，然而目前國內 發卡銀行在整個信用卡信用風險管理上，大都採行人類專家經驗判斷的方 式進行。發卡銀行隨著持卡人數快速地增加，其信用資料亦呈等比例急速 上升，若仍採用人工處理方式，除了會大幅增加工作負荷外，其授信品質 也不易控制。因此，本研究擬引進資訊技術來解決大量信用卡信用資料之 信用管理問題。 首先，我們探討信用卡信用管理業務，並根據其作業 流程來建構一信用卡信用管理自動化的架構，此架構包括徵信驗證系統、 審核系統、預警系統、高風險客戶管理系統、及催收系統等五個系統，其 目的在於輔助授信管理之業務、減少授管人員的工作負荷、以有效控制授 信品質、及降低授信的風險。 其次，本研究針對上述信用卡信用管理 自動化中的預警系統，利用範例學習法來建立信用卡信用風險預警範例學 習系統，且實際以一家發卡銀行的信用資料來建立並驗證四個預警模式， 期能事先讓系統自動查核信用不良之客戶。此四類預警模式為: (一)提前 預警模式(二)群體決策預警模式(三)追蹤管理預警模式(四)例外管理預警 模式 最後，我們亦提出一些未來研究之課題，期能進一步發展本研究 之信用卡信用管理自動化系統及預警模式，以推廣應用至各發卡機構。

Författare: Emil Herder, Filip Nilsson och Mats Seglarvik Handledare: Anders Hytter Examinator: Bertil Hultén Kurs: Examensarbete 30hp, Civilekonomprogrammet inriktning marknadsföring, Linnéuniversitetet Kalmar, VT 2016, 4FE63E. Rapportens namn: Kredit-kort och gott Frågeställning: Hur värderar svenska konsumenter olika kreditkortsattribut? Syfte: Det primära syftet med vår studie är att undersöka hur svenska konsumenter värderar kreditkortsattribut på den svenska marknaden och att undersöka eventuella samband som existerar mellan deras värderingar och olika valda variabler. Som delsyfte ämnar vi att utifrån vår undersökning komma med relevanta rekommendationer som kan hjälpa Resurs Bank att skapa attraktiva kreditkortserbjudanden för konsumenter på den svenska marknaden. Metod: Uppsatsen har till en början en induktiv ansats som sedan övergår till en mer deduktiv ansats. Datainsamling har i huvudsak skett genom enkätundersökning, men även genom intervjuer. Resultat & slutsatser: Vi har rangordnat hur svenska konsumenter har värderat olika kreditkortsattribut och funnit att det existerar samband mellan hur olika individer värderat olika kreditkortsattribut baserat på andra värderingar och grupptillhörighet. Teoretiskt och praktiskt bidrag: Teoretiskt har vi bidragit med en grund för hur svenska konsumenter värderar olika kreditkortsattribut samt en undersökningsmodell som kan användas som underlag för vidare forskning. Våra praktiska bidrag är att informationen kan användas som beslutsunderlag när ett kreditkortserbjudande som inriktar sig mot kundens upplevda värde ska läggas fram. Nyckelord: Kreditkortsattribut, kreditkort, bonussystem, välgörenhet, konsumentbeteende, konsumentvärde / Author: Emil Herder, Filip Nilsson and Mats Seglarvik Mentor: Anders Hytter Examiner: Bertil Hultén Course: Master Thesis 30 credits, Master of Business and Economics, Marketing, Linnaeus University, Kalmar, Spring 2016 4FE63E. Name of report: Kredit-kort och gott Research question: How do swedish consumers value different credit card attributes? Purpose: The primary purpose of our study is to reserach how swedish consumers value credit card attributes on the swedish market and research possible correlations that exists between their values and different chosen vairables. The subpurpose of the study is to come up with relevant recommendations which may help Resurs Bank to create attractive credit card offerings for the swedish credit card market. Method: The study starts off with and inductive approach that later turns into a more deductive approach. The data was collected from a survey and interviews. Results and conclusion: We’ve ranked how swedish consumers value different credit card attributes based on other values and group membership. Theoretical and practical contribution: Theoretically we’ve contributed with a foundation on how swedish consumers value different credit card attributes and a researchmodel that can be used as a basis for further research. Our practical contribution is that the information from our study can be used to support decisions when it comes to creating a credit card offering that targets the customers percieved value. Keywords: Credit card attributes, credit card, bonus system, charity, consumer behaviour, consumer value.

顧客獲利性分析，係將顧客對公司之貢獻予以量化，並據以進行顧客利潤管理。藉由顧客獲利性分析，企業除了可深入瞭解獲利原因外，更可針對不同獲利性之顧客擬定不同的策略，使資源之運用更有效率，以提高整體之獲利。 □□ □□賑膍s透過與個案公司之合作，採取實地實證之研究方式，並以複迴歸模型分析銀行信用卡顧客獲利性影響因素。研究結果顯示，影響信用卡顧客獲利性之因素有往來期間、持卡等級、顧客所持正卡數、資金需求狀況、帳戶循環狀況、消費類別、信用額度使用率、顧客忠誠度…等。 □□ □□根據研究結果之隱含意涵，本研究亦對個案公司提出若干行銷與管理策略之建議，作為個案公司擬定平衡計分卡策略議題之參考。 / Customer profitability analysis is a method to manage customer profit based on the quantification of customers’ net contribution. By customer profitability analysis, companies can not only find reasons for making profit, but also draft different strategies for customers with different profitability, optimize the allocation of resources, and increase the overall profits. This study adopts field empirical method through the cooperation with the case company, and analyzes the factors influencing customer profitability by building multiple-regression models. The study results show that the factors influencing customer profitability are business period, card class, numbers of primary card, capital needs, revolving situation, categories of spending, card usage rate, customer loyalty, etc. According to the research results, this study also proposes certain suggestions about the marketing and management strategies, helping the case company frame the strategy themes in the balaced scorecard

Doctor of Philosophy / Department of Human Ecology-Personal Financial Planning / Kristy L. Pederson-Archuleta / The purpose of this dissertation was to examine the factors associated with students’ intentions to not possess and use a credit card. This dissertation focused on exploring a sample of undergraduate college students who do not possess a credit card. There is little known research on this group of students. The dissertation was directed by the following over-arching research question: The goal of this study was to explore college students’ beliefs about not possessing a credit card using the Theory of Reasoned Action (TRA). The research questions for this dissertation were: (a) How is personality (i.e., individual background factor) of undergraduate college students associated with their behavioral, normative, and control beliefs to not possess a credit card, (b) How are education level, age, gender, income level, religiosity, marital status, and ethnicity (i.e., social background factors) of undergraduate college students associated with their behavioral, normative, and control beliefs to not possess a credit card, and (c) How is financial knowledge (i.e., information background factor) of undergraduate college students associated with their behavioral, normative, and control beliefs to not possess a credit card. This study collected primary data. A pilot study was conducted to set the stage for the data collection of the current study. The data analysis methodology for this study consisted of the following four methods: (a) Factor Analysis, (b) Correlation Analysis, (c) MANOVA, and (d) Discriminant Function Analysis. Factor analysis identified questions were used to develop scales to measure the dependent variables. Strong reliability estimates were obtained, ranging from .84 to .94. The MANOVA test identified seven hypotheses with statistically significant results < .05. Control beliefs were significantly associated with personality. The five personality types, extraversion, agreeableness, conscientiousness, neuroticism, and openness, were all found to be significantly associated with either behavioral beliefs, control beliefs, or injunctive normative beliefs. Extraversion, agreeableness, conscientiousness, and neuroticism were all found to be associated with control beliefs. While agreeableness was also associated with injunctive normative beliefs, openness was found to be associated with behavioral beliefs. Financial knowledge was found to be associated with control beliefs. Discriminant function analysis was performed as a confirmatory test of the results from the MANOVA test, and supported the results of the MANOVA for six of the hypotheses.

Submitted by JOSIANE SANTOS DE OLIVEIRA (josianeso) on 2017-05-29T11:43:19Z No. of bitstreams: 1 Leonardo Jantsch_.pdf: 653935 bytes, checksum: 68ed1b170adfa489130eb6ea553f2c75 (MD5) / Made available in DSpace on 2017-05-29T11:43:19Z (GMT). No. of bitstreams: 1 Leonardo Jantsch_.pdf: 653935 bytes, checksum: 68ed1b170adfa489130eb6ea553f2c75 (MD5) Previous issue date: 2017-02-22 / Nenhuma / O objetivo deste trabalho foi mensurar a probabilidade de atraso nos pagamentos e posterior inadimplência como medida de análise do risco de crédito e de suporte a tomada de decisão em empréstimos de cartão de crédito para pessoas físicas em instituição financeira comercial. Como método de pesquisa buscou-se no Design Science Research a base para a prescrição de soluções e construção de artefatos, sendo que as análises foram efetivadas utilizando-se das cadeias de Markov. Os resultados encontrados evidenciam que indivíduos se comportam de forma distinta em termos de utilização e manutenção das carteiras, o que permite atribuir características próprias aos usuários de maior risco pelos atributos selecionados neste estudo. A principal contribuição deste trabalho está em evidenciar que o processo de entendimento prévio, contemplando o levantamento dos requisitos de negócio, necessidade de dados, tratamento de dados, modelagem, avaliação e implementação, pode se tornar um fator de sucesso no momento de definição e aplicação das análises de perfil por meio das cadeias de Markov. / The objective of this study was to measure the probability of late payment and subsequent delinquency as a measure of credit risk analysis and support decision making in credit card loans to individuals in a commercial financial institution. As a research method, Design Science Research was the basis for the prescription of solutions and the construction of artifacts, and the analyzes were carried out using Markov chains. The results show that individuals behave differently in terms of the use and maintenance of the portfolios, which allows to assign characteristics of the users of higher risk to the attributes selected in this study. The main contribution of this work is to show that the process of prior understanding, contemplating the survey of business requirements, data requirements, data processing, modeling, evaluation and implementation, can become a success factor when defining and applying of the profile analyzes through the Markov chains.

Made available in DSpace on 2016-04-29T14:23:30Z (GMT). No. of bitstreams: 1 Valter Lacerda de Andrade Junior.pdf: 673552 bytes, checksum: 68480511c98995570354a0166d2bb577 (MD5) Previous issue date: 2014-08-13 / The need for expert assessment of Data Mining in textual data fields and other unstructured information is increasingly present in the public and private sector. Through probabilistic models and analytical studies, it is possible to broaden the understanding of a particular information source. In recent years, technology progress caused exponential growth of the information produced and accessed in the virtual media (web and private). It is estimated that by 2003 humanity had historically generated a total of 5 exabytes of content; today that asset volume can be produced in a few days. With the increasing demand, this project aims to work with probabilistic models related to the financial market in order to check whether the textual data fields, or unstructured information, contained within the business environment, can predict certain customers behaviors. It is assumed that in the corporate environment and on the web, there is great valuable information that, due to the complexity and lack of structure, they are barely considered in probabilistic studies. This material may represent competitive and strategic advantage for business, so analyzing unstructured information one can acquire important data on behaviors and mode of user interaction in the environment in which it operates, providing data as to obtain psychographic profile and satisfaction degree. The corpus of this study consists of the results of experiments made in negotiating environment of a financial company in São Paulo. On the foregoing analysis, it was applied statistical bias semiotic concepts. Among the findings of this study, it is possible to get a critical review and thorough understanding of the processes of textual data assessment / A necessidade de análise especializada de Mineração de Dados (Data Mining) em campos textuais e em outras informações não estruturadas estão, cada vez mais, presente nas instituições dos setores públicos e privados. Por meio de modelos probabilísticos e estudos analíticos, torna-se possível ampliar o entendimento sobre determinada fonte de informação. Nos últimos anos, devido ao avanço tecnológico, observa-se um crescimento exponencial na quantidade de informação produzida e acessada nas mídias virtuais (web e privada). Até 2003, a humanidade havia gerado, historicamente, um total de 5 exabytes de conteúdo; hoje estima-se que esse volume possa ser produzido em poucos dias. Assim, a partir desta crescente demanda identificada, este projeto visa trabalhar com modelos probabilísticos relacionados ao mercado financeiro com o intuito de analisar se os campos textuais e ilustrativos, ou informações não estruturadas, contidas dentro do ambiente de negócio, podem prever certos comportamentos de clientes. Parte-se do pressuposto que, no ambiente corporativo e na web, existem informações de grande valor e que, devido à complexidade e falta de estrutura, não são consideradas em estudos probabilísticos. Isso pode representar vantagem competitiva e estratégica para o negócio, pois, por meio da análise da informação não estruturada, podem-se conhecer comportamentos e modos de interação do usuário nestes ambientes, proporcionando obter dados como perfil psicográfico e grau de satisfação. O corpus deste estudo constitui-se de resultados de experimentos efetuados no ambiente negocial de uma empresa do setor financeiro. Para as análises, foram aplicados conceitos estatísticos com viés semiótico. Entre as informações obtidas por esta pesquisa, verifica-se a compreensão crítica e aprofundada dos processos de análise textual