No limits : exploring the psychology of unsolicited credit card limit increase offers

The issue of credit card debt has become an increasing concern in recent years. In Australia, for example, there is currently $42.5 billion worth of outstanding debt on credit cards, with $30 billion (over 70 per cent) bearing interest. Further, in 2001, Visa reported that 32 per cent of consumers had not paid their card off in the previous 12 months, which suggests that interest-bearing debt in Australia is held by approximately only 113 of credit card borrowers. An important element of credit card marketing is the use of psychological manipulations to encourage consumers to take-up credit. In this article, we examine the use of language and imagery in unsolicited credit card limit increase offers, and how these might influence consumers' decisions to increase their credit card limit. The analysis found that the use of terms that focused on the benefits of credit card use, such as "choice", "freedom", and "peace of mind" were used consistently to convince consumers to increase their credit card limit, whereas the use the of terms that could be considered more pragmatic and with direct reference to the nature of the product, such as "debt", "repayment" and "loan", were rarely used. Similarly, the use of colour, text changes, and images, were used which may have an influence over a consumer's ability to rationally consider whether the increase is appropriate for them. The paper concludes by recommending that government and representative bodies need to take into account the psychological manipulations used by credit card providers when developing consumer policy and codes of ethics.

A comparative study of the credit card market dynamics in Mexico and Brazil

This research analyzes and compares the attractiveness of the Brazilian and Mexican credit card markets from a financial firm’s perspective. The market dynamics in Latin America’s two economic powerhouses are fleshed out with qualitative and quantitative data, using a strategic framework to structure the analysis. Since its adoption by both countries in 1956, credit card usage has experienced many years of double digit growth. However, penetration levels remain low compared with most developed countries. Brazil has a more developed credit card infrastructure, with more potential profit, and issuers might face fewer competitive challenges.  Alternatively, Mexico, is witnessing a more favorable economy, a friendlier business and regulatory environment, combined with fewer financial products that compete with the credit card. Therefore, this paper concludes that Brazil and Mexico both offer market opportunities for credit card companies that can navigate the different technological, demographic, macroeconomic, and regulatory shifts in each country.

The Family Partners Credit Card: A Token Economy System Adapted for Intensive Family Preservation Services to Enable Families to Manage Difficult Behavior of Adolescents

Increasingly, families referred for Intensive Family Preservation Services have not experienced a crisis of maltreatment, focused on the parent; rather these families have children with chronic behavioral difficulties for which their parents lack the skills to cope. These are the same families whose children were formerly placed in residential programs. This paper presents The Family Partners Credit Card System, incorporating behavioral techniques developed to treat children in out-of-home placements into a family preservation model. Two case examples illustrate how the system has been modified to train biological or adoptive parents in parenting skills, enable them to teach their children pro-family behaviors, and reinforce new behaviors through a credit card that monitors an ongoing balance of credits and fines.

Subcommittee report on credit card interest rates.

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Marketing strategy and new product development in the UK credit card industry

The development of new products in today's marketing environment is generally accepted as a requirement for the continual growth and prosperity of organisations. The literature is consequently rich with information on the development of various aspects of good products. In the case of service industries, it can be argued that new service product development is of as least equal importance as it is to organisations that produce tangible goods products. Unlike the new goods product literature, the literature on service marketing practices, and in particular, new service product development, is relatively sparse. The main purpose of this thesis is to examine a number of aspects of new service product development practice with respect to financial services and specifically, credit card financial services. The empirical investigation utilises both a case study and a survey approach, to examine aspects of new service product development industry practice relating specifically to gaps and deficiencies in the literature with respect to the financial service industry. The findings of the empirical work are subsequently examined in the context in which they provide guidance and support for a new normative new service product development model. The study examines the UK credit card financial service product sector as an industry case study and perspective. The findings of the field work reveal that the new service product development process is still evolving, and that in the case of credit card financial services can be seen as a well-structured and well-documented process. New product development can also be seen as an incremental, complex, interactive and continuous process which has been applied in a variety of ways. A number of inferences are subsequently presented.

Irresponsible Lending?: A Case Study of a U.K. Credit Industry Reform Initiative

There are major concerns about the level of personal borrowing, particularly sourced from credit cards. This paper charts the progress of an initiative to create a Responsible Lending Index (RLI) for the credit industry. The RLI proposed to voluntarily benchmark lending standards and promote best practice within the credit industry by involving suppliers of credit, customer representatives and regulators. However, despite initial support from some banks, consumer bodies and the Chair of the Treasury Select Committee, it failed to gain sufficient support from financial institutions in its original format. The primary reasons for this were related to the complexity of building such a robust index and the banks trade body’s fear of exposing its members to public scrutiny. A revised alternative, the Responsible Lending Initiative, was proposed which took into account these concerns. However, the Association of Payment Clearing Service (APACS), the trade body of the credit industry, then effectively destroyed the proposal. This article describes an attempt to address the challenges in the credit card industry with the initiation of the RLI, reflected in stakeholder discourse and in the context of a wider concern expressed by the involved stakeholders in terms of the need for greater responsibility in the banking industry’s lending practices.

Congratulations, you're pre-approved! : an analysis of credit limit upselling letters

Consumer Action commissioned Dr Paul Harrison, Deakin Business School, Deakin University and Marta Massi, School of Marketing and Communication, Lumsa University (Rome), to study the psychological aspects of one form of credit marketing – unsolicited credit card limit-increase offers (UCCLIOs). The researchers studied 21 UCCLIO letters – 17 provided by consumers and four provided by banks – and applied theories developed from previous research in the fields of marketing, consumer behavior, behavioural economics and cognitive psychology, to describe likely ways in which UCCLIO’s influenced consumer behavior and decision making. The researchers make some recommendations from a behavioural perspective based on their findings, and Consumer Action proposes how these findings might be applied to consumer policy.

Informational synergies in consumer credit

Lenders can tap into multiple sources of private information to assess consumer credit risk but little is known about the informational synergies between these sources. Using unique panel data on checking accounts and credit card accounts from the same customers during 2007-2014, we find that activity measures from both account types contain information beyond credit scores and other controls. Checking accounts display warning indications earlier and more accurately than credit card accounts. We also investigate the consistency of information, the reasons for defaults, and selection effects. The evidence highlights sizeable informational synergies that lenders can use to manage credit relationships.

Domain-driven classification based on multiple criteria and multiple constraint-level programming for intelligent credit scoring

Extracting knowledge from the transaction records and the personal data of credit card holders has great profit potential for the banking industry. The challenge is to detect/predict bankrupts and to keep and recruit the profitable customers. However, grouping and targeting credit card customers by traditional data-driven mining often does not directly meet the needs of the banking industry, because data-driven mining automatically generates classification outputs that are imprecise, meaningless, and beyond users' control. In this paper, we provide a novel domain-driven classification method that takes advantage of multiple criteria and multiple constraint-level programming for intelligent credit scoring. The method involves credit scoring to produce a set of customers' scores that allows the classification results actionable and controllable by human interaction during the scoring process. Domain knowledge and experts' experience parameters are built into the criteria and constraint functions of mathematical programming and the human and machine conversation is employed to generate an efficient and precise solution. Experiments based on various data sets validated the effectiveness and efficiency of the proposed methods. © 2006 IEEE.

A tri-dimensional approach for auditing brand loyalty

Over the past twenty years brand loyalty has been an important topic for both marketing practitioners and academics. While practitioners have produced proprietary brand loyalty audit models, there has been little academic research to make transparent the methodology that underpins these audits and to enable practitioners to understand, develop and conduct their own audits. In this paper, we propose a framework for a brand loyalty audit that uses a tri-dimensional approach to brand loyalty, which includes behavioural loyalty and the two components of attitudinal loyalty: emotional and cognitive loyalty. In allowing for different levels and intensity of brand loyalty, this tri-dimensional approach is important from a managerial perspective. It means that loyalty strategies that arise from a brand audit can be made more effective by targeting the market segments that demonstrate the most appropriate combination of brand loyalty components. We propose a matrix with three dimensions (emotional, cognitive and behavioural loyalty) and two levels (high and low loyalty) to facilitate a brand loyalty audit. To demonstrate this matrix, we use the example of financial services, in particular a rewards-based credit card.

Sequential decision fusion for controlled detection errors

Information fusion in biometrics has received considerable attention. The architecture proposed here is based on the sequential integration of multi-instance and multi-sample fusion schemes. This method is analytically shown to improve the performance and allow a controlled trade-off between false alarms and false rejects when the classifier decisions are statistically independent. Equations developed for detection error rates are experimentally evaluated by considering the proposed architecture for text dependent speaker verification using HMM based digit dependent speaker models. The tuning of parameters, n classifiers and m attempts/samples, is investigated and the resultant detection error trade-off performance is evaluated on individual digits. Results show that performance improvement can be achieved even for weaker classifiers (FRR-19.6%, FAR-16.7%). The architectures investigated apply to speaker verification from spoken digit strings such as credit card numbers in telephone or VOIP or internet based applications.

Automatic speaker recognition under adverse conditions

Speaker verification is the process of verifying the identity of a person by analysing their speech. There are several important applications for automatic speaker verification (ASV) technology including suspect identification, tracking terrorists and detecting a person’s presence at a remote location in the surveillance domain, as well as person authentication for phone banking and credit card transactions in the private sector. Telephones and telephony networks provide a natural medium for these applications. The aim of this work is to improve the usefulness of ASV technology for practical applications in the presence of adverse conditions. In a telephony environment, background noise, handset mismatch, channel distortions, room acoustics and restrictions on the available testing and training data are common sources of errors for ASV systems. Two research themes were pursued to overcome these adverse conditions: Modelling mismatch and modelling uncertainty. To directly address the performance degradation incurred through mismatched conditions it was proposed to directly model this mismatch. Feature mapping was evaluated for combating handset mismatch and was extended through the use of a blind clustering algorithm to remove the need for accurate handset labels for the training data. Mismatch modelling was then generalised by explicitly modelling the session conditions as a constrained offset of the speaker model means. This session variability modelling approach enabled the modelling of arbitrary sources of mismatch, including handset type, and halved the error rates in many cases. Methods to model the uncertainty in speaker model estimates and verification scores were developed to address the difficulties of limited training and testing data. The Bayes factor was introduced to account for the uncertainty of the speaker model estimates in testing by applying Bayesian theory to the verification criterion, with improved performance in matched conditions. Modelling the uncertainty in the verification score itself met with significant success. Estimating a confidence interval for the "true" verification score enabled an order of magnitude reduction in the average quantity of speech required to make a confident verification decision based on a threshold. The confidence measures developed in this work may also have significant applications for forensic speaker verification tasks.