Re-assignment and consolidation of credit authorizations

When a customer is granted several credit lines with different risk levels, the bank usually stipulates an authorization for each credit line and a total authorization; moreover authorizations are sometimes given for sets of credit lines. The purpose of the paper is, given a set of credit lines with corresponding authorizations, and a customer's current credit utilizations, to find new utilizations of greatest risk to the bank, within the credit authorization limits and with regard to the current utilizations. These new utilizations, used as re-assigned authorizations enable the bank to assess the risks relative to residual commitment and possible overstepping for each credit line. Furthermore these risks can be aggregated over a set of customers for each credit line. An algorithm has been developed to compute these re-assigned authorizations; it is based on classical linear programming methods. This paper describes this algorithm and recommends its use to consolidate risks.     

A Two-stage screening procedure for mailing credit assessment

Direct mailing is one of the tools of business marketing. Itcan stimulate the purchase of mail-order products or financialservices. When selecting a mailing list, we hope to obtain ahigher mailing response; on the other hand, we also have toconsider the risk of defaulting. In the case of selling financialservices, customers who respond must be examined for credit.In order to avoid jeopardizing the existing relations with suchcustomers, companies try to reduce their rate of declining suchapplicants by controlling, at the mailing stage, for the riskof choosing customers who will default. In this article, a two-stage screening procedure is constructedto solve a problem of mailing credit assessment with mailingand credit-assessment stages;A screening method can be appliedto select a mailing list at the mailing stage, while the needfor a credit assessment depends on the types of product or service.Therefore, this problem may include four possible models: screeningwith or without credit assessment and random sampling with orwithout credit assessment. Moreover, the optimal cutoff scoresare determined by maximizing total profit. A mailing exampleis then given to illustrate the use of the proposed models formailing credit assessment. Compared with a random sampling method,a screening method has a significant improvement in terms ofresponse rate, decline rate, bad rate, and total profit.     

Enhanced decision support in credit scoring using Bayesian binary quantile regression

Fierce competition as well as the recent financial crisis in financial and banking industries made credit scoring gain importance. An accurate estimation of credit risk helps organizations to decide whether or not to grant credit to potential customers. Many classification methods have been suggested to handle this problem in the literature. This paper proposes a model for evaluating credit risk based on binary quantile regression, using Bayesian estimation. This paper points out the distinct advantages of the latter approach: that is (i) the method provides accurate predictions of which customers may default in the future, (ii) the approach provides detailed insight into the effects of the explanatory variables on the probability of default, and (iii) the methodology is ideally suited to build a segmentation scheme of the customers in terms of risk of default and the corresponding uncertainty about the prediction. An often studied dataset from a German bank is used to show the applicability of the method proposed. The results demonstrate that the methodology can be an important tool for credit companies that want to take the credit risk of their customer fully into account.     

A comparison of strategies to develop a customer default scoring model

Behavioural scoring models are generally used to estimate the probability that a customer of a financial institution who owns a credit product will default on this product in a fixed time horizon. However, one single customer usually purchases many credit products from an institution while behavioural scoring models generally treat each of these products independently. In order to make credit risk management easier and more efficient, it is interesting to develop customer default scoring models. These models estimate the probability that a customer of a certain financial institution will have credit issues with at least one product in a fixed time horizon. In this study, three strategies to develop customer default scoring models are described. One of the strategies is regularly utilized by financial institutions and the other two will be proposed herein. The performance of these strategies is compared by means of an actual data bank supplied by a financial institution and a Monte Carlo simulation study.     

基于客户信用度的物流配送车辆路径问题研究

针对当前城市物流配送过程中普遍存在的客户中途取消订单、无故退换货等交易违约问题,引入客户信用度的测度方法。根据客户历史交易违约数据计算客户信用值,并转化求解客户信用度,构建了包含车辆配送成本、租赁成本以及违反时间窗惩罚成本的配送路径优化模型。设计了一种遗传(GA)-禁忌搜索(TS)混合算法进行模型求解,在算法过程中应用精英保留策略进行循环迭代寻优。结合重庆某外卖物流配送网络的实例数据,验证了模型和算法的有效性和可行性。实验结果给出了不同服务策略下的物流配送调度方案,并进行了基于客户信用度的客户配送服务序列调整比较和敏感度分析。研究表明客户信用等级的合理划分可以有效降低物流配送成本和提高客户服务水平。     

Optimal ordering policies when the supplier provides a progressive interest scheme

In fact, most credit card issuers (or home equity banks) frequently offer cardholders (or customers) a teaser interest rate (say, I₁), which is significantly lower than the regular interest rate of I₂ (with I₂ > I₁) for only 6 months or a year (say, M₂) to lure new customers from their competitors. Consequently, the customer faces a progressive interest charge from the bank. If the customer pays the outstanding balance by the grace period (say, M₁ which is generally 25 days), then the bank does not charge any interest. If the outstanding amount is paid after M₁, but by M₂ (with M₂ > M₁), then the bank charges the customer the teaser interest rate of I₁ on the unpaid balance. If the customer pays the outstanding amount after M₂, then the bank charges the regular interest rate of I₂. In this paper, we first establish an appropriate EOQ model for a retailer when the bank (or the supplier) offers a progressive interest charge, and then provide an easy-to-use closed-form solution to the problem.     

On the customer lifetime value: a mathematical perspective

The customer lifetime value (CLV) is an important concept increasingly considered in the field of general marketing and in the management of firms, of organizations to increase the captured profitability. It represents the total value that a customer produces during his or her lifetime, or better represents the measure of the potential profit generating by a customer. The companies use the customer lifetime value to segment customers, analyze probability of churn, allocate resources or formulate strategies and, therefore, they increasingly derive revenue from the creation and from sustenance of long-term relationships with their customers. For this reason, the customer lifetime value is increasingly considered a touchstone for the management of customer relationships. In this article, the authors deepen the concept and use of customer lifetime value and present some mathematical models for its determination. There is many models for this purpose but most of them are theoretic, complex and not applicable. Though not exhaustive, the major contribution of this paper is that it provides a general mathematical formulation to estimate the CLV and that it has a context less specific compared to papers, present in literature, on the customer lifetime.     

Developing a measure of risk adjusted revenue (RAR) in credit cards market: Implications for customer relationship management

Current models of customer lifetime value (CLV) consider the discounted value of profits that a customer generates over an expected lifetime of relationship with the firm. This practice can be misleading in the financial services markets because it ignores the risk posed by the customer (such as delinquency and default). Specifically, in the credit card market, the correlation between revenue and risk is positive. Therefore, firms need to adjust a customer’s profits for the associated risk before developing a measure of customer lifetime value. We propose a new measure, risk adjusted revenue (RAR), that can incorporate multiple sources of risk and demonstrate the usefulness of the proposed measure in correctly assessing the value of a customer in the credit card market. The model can be extended to compute risk adjusted lifetime value (RALTV). We use the RAR metric to understand the effectiveness of different modes of acquisition, and of retention strategies such as affinity cards and reward cards. We find that both reward- and affinity-cardholders generate higher RAR than non-reward and non-affinity cardholders respectively. The ordering of different modes of acquisition with respect to RAR (in decreasing order) is as follows: Internet, direct mail, telesales, and direct selling.     

Single line queue with repeated demands

We analyze a model of a queueing system in which customers can only call in to request service: if the server is free, the customer enters service immediately, but if the service system is occupied, the unsatisfied customer must break contact and reinitiate his request later. Such a customer is said to be in “orbit”. In this paper we consider three models characterized by the discipline governing the order of re-request of service from orbit. First, all customers in orbit can reapply, but are discouraged and reduce their rate of demand as more customers join the orbit. Secondly, the FCFS discipline operates for the unsatisfied customers in orbit. Finally, the LCFS discipline governs the customers in orbit and the server takes an exponentially distributed vacation after each service is completed. We calculate several characteristics quantities of such systems, assuming a general service-time distribution and different exponential distributions for the times between arrivals of first and repeat requests.     

Customer retention: a behavioural model

One of the main problems currently facing CTedit-card issuersis the increasing number of cardholders who are using theircards less often (i.e. attrition) and/or returning their cards(closures). This problem is of particular concern as the totalnumber of credit cards held by consumers is declining (by approximately0.6 per cent per month in 1992) and the number of new applicantsis also running at an all time-low (less than 1 per cent permonth in 1992). Most of the published literature in the broad area of creditcards looks at credit scoring, rather than the need for cardissuers to identify and retain a profitable portfolio of cardcustomers. The overall objective of our research is ‘segmentationfor customer retention’, and this paper aims to identifythe characteristics of card customers who initiate the closureof their accounts Linear discriminant analysis is applied toa sample of approximately 17,000 UK holders of bank credit cards,using various behavioural and sociodemographic variables, andtested on a holdout sample of 10,000 cases.     

Trade credit competition between two retailers in a supply chain under credit-linked retail price and market demand

The paper considers a supply chain system in which the sole manufacturer supplies the same product to two retailers who compete in offering trade credit period to customers. Both the market demand and retail prices vary with the trade credit periods offered by the retailers. The manufacturer also provides a trade credit period to both the retailers to settle down their accounts. The net profit function of the supply chain is derived considering possible relationships among the trade credit periods offered by the manufacturer and the retailers and the time when each retailer receives the last payment from his customer. An algorithm is developed to find the optimal solution of the proposed model. From the numerical study, it is observed that a two-level trade credit financing can increase profits not only for the manufacturer and the retailers but also for the whole supply chain.     

A location–allocation heuristic for the capacitated multi-facility Weber problem with probabilistic customer locations

The capacitated multi-facility Weber problem is concerned with locating m facilities in the Euclidean plane, and allocating their capacities to n customers at minimum total cost. The deterministic version of the problem, which assumes that customer locations and demands are known with certainty, is a non-convex optimization problem and difficult to solve. In this work, we focus on a probabilistic extension and consider the situation where the customer locations are randomly distributed according to a bivariate distribution. We first present a mathematical programming formulation, which is even more difficult than its deterministic version. We then propose an alternate location–allocation local search heuristic generalizing the ideas used originally for the deterministic problem. In its original form, the applicability of the heuristic depends on the calculation of the expected distances between the facilities and customers, which can be done for only very few distance and probability density function combinations. We therefore propose approximation methods which make the method applicable for any distance function and bivariate location distribution.     

Performance of service policies in a specialized service system with parallel servers

This paper considers a scheduling problem occurring in a specialized service system with parallel servers. In the system, customers are divided into the “ordinary” and “special” categories according to their service needs. Ordinary customers can be served by any server, while special customers can be served only by the flexible servers. We assume that the service time for any ordinary customer is the same and all special customers have another common service time. We analyze three classes of service policies used in practice, namely, policies with priority, policies without priority and mixed policies. The worst-case performance ratios are obtained for all of these service policies.     

Two Queues with Weighted One-Way Overflow

We consider an open queueing network consisting of two queues with Poisson arrivals and exponential service times and having some overflow capability from the first to the second queue. Each queue is equipped with a finite number of servers and a waiting room with finite or infinite capacity. Arriving customers may be blocked at one of the queues depending on whether all servers and/or waiting positions are occupied. Blocked customers from the first queue can overflow to the second queue according to specific overflow routines. Using a separation method for the balance equations of the two-dimensional server and waiting room demand process, we reduce the dimension of the problem of solving these balance equations substantially. We extend the existing results in the literature in three directions. Firstly, we allow different service rates at the two queues. Secondly, the overflow stream is weighted with a parameter p ∈ [0,1], i.e., an arriving customer who is blocked and overflows, joins the overflow queue with probability p and leaves the system with probability 1 − p. Thirdly, we consider several new blocking and overflow routines. An erratum to this article can be found at     

Quantile regression for modelling distributions of profit and loss

Quantile regression is applied in two retail credit risk assessment exercises exemplifying the power of the technique to account for the diverse distributions that arise in the financial service industry. The first application is to predict loss given default for secured loans, in particular retail mortgages. This is an asymmetric process since where the security (such as a property) value exceeds the loan balance the banks cannot retain the profit, whereas when the security does not cover the value of the defaulting loan then the bank realises a loss. In the light of this asymmetry it becomes apparent that estimating the low tail of the house value is much more relevant for estimating likely losses than estimates of the average value where in most cases no loss is realised. In our application quantile regression is used to estimate the distribution of property values realised on repossession that is then used to calculate loss given default estimates. An illustration is given for a mortgage portfolio from a European mortgage lender. A second application is to revenue modelling. While credit issuing organisations have access to large databases, they also build models to assess the likely effects of new strategies for which, by definition, there is no existing data. Certain strategies are aimed at increasing the revenue stream or decreasing the risk in specific market segments. Using a simple artificial revenue model, quantile regression is applied to elucidate the details of subsets of accounts, such as the least profitable, as predicted from their covariates. The application uses standard linear and kernel smoothed quantile regression.     

Social Relationship and Transactional Marketing Policies—Maximizing Customer Lifetime Value

We present an analytic approach to address the problem of how sellers can establish and maintain a long-lasting relationship with a buyer and, at the same time, maximize customer lifetime value (CLV). To model the evolution of a relational exchange between a seller and a buyer, we extend a well-known mathematical model of “love dynamics.” The growth of each partner’s commitment to the relationship is a sum of negative and positive terms. The negative term describes each partner’s propensities for opportunism, while the positive terms describe each partner’s trust in the commitment of the other, and the reaction to marketing efforts. The seller controls the evolution of the relationship through social relationships and transactional marketing efforts. The main findings are as follows: (1) Loyal (committed) customers and long-term relationships do not always generate better cash flows, especially when buyers either look for superior current value in each purchase opportunity or are short-term oriented. (2) Without mutual trust between partners, the seller should treat old customers over time as new ones, making the reduction of retention costs impossible. (3) It is only cheaper to retain current customers rather than acquiring new ones if mutual trust between partners overcomes propensities for opportunism and the seller slightly discounts future cash flows.     

Question selection responding to information on customers from heterogeneous populations to select offers that maximize expected profit

The advent of Internet broking pages allows customers to ‘apply’ to a number of different companies at one time, leading to multiple offers made to a customer. The saturated condition of the personal financial products has led to falling ‘take’ rates. Financial institutions are trying to increase the ‘take’ rates of their personal financial products. Applicants for credit will have to provide information for risk assessment, which can be used to assess the probability of a customer accepting an offer. Interactive channels such as the Internet and telephone allow questions that are asked to depend on previous answers. The questions selected need to provide information to assess the probability of acceptance of a particular variant of financial product. In this paper, we investigate a model to predict the best offer to extend next to a customer based on the response for the questions, as well as the question selection itself.     

Artificial immune algorithm-based credit evaluation for mobile telephone customers

The arrearage problem is a critical concern for China’s mobile communication services industry. Analysis of customer credit evaluation provides this study with a potential viable solution to the arrearage problem in China. By employing an artificial immune algorithm (AIA), a measure of customer credit based on customer attributes is proposed. This method was applied to one China mobile communication services company with approximately 400?000 customers yielding satisfying results. Utilizing traditional predictive accuracy and alternative metrics, performance comparisons of the proposed AIA were made using the feed-forward back propagation artificial neural network and the logistic regression model. A decision tree analysis of anticipated benefits was performed and indicates workability of the proposed method based on customer credit evaluation.     

On a Two-Queue Priority System with Impatience and its Application to a Call Center*

We consider an s-server priority system with a protected and an unprotected queue. The arrival rates at the queues and the service rate may depend on the number n of customers being in service or in the protected queue, but the service rate is assumed to be constant for n > s. As soon as any server is idle, a customer from the protected queue will be served according to the FCFS discipline. However, the customers in the protected queue are impatient. If the offered waiting time exceeds a random maximal waiting time I, then the customer leaves the protected queue after time I. If I is less than a given deterministic time, then he leaves the system, else he will be transferred by the system to the unprotected queue. The service of a customer from the unprotected queue will be started if the protected queue is empty and more than a given number of servers become idle. The model is a generalization of the many-server queue with impatient customers. The global balance conditions seem to have no explicit solution. However, the balance conditions for the density of the stationary state process for the subsystem of customers being in service or in the protected queue can be solved. This yields the stability conditions and the probabilities that precisely n customers are in service or in the protected queue. For obtaining performance measures for the unprotected queue, a system approximation based on fitting impatience intensities is constructed. The results are applied to the performance analysis of a call center with an integrated voice-mail-server.     

Linking structural equation modeling to Bayesian networks: Decision support for customer retention in virtual communities

Bayesian networks are limited in differentiating between causal and spurious relationships among decision factors. Decision making without differentiating the two relationships cannot be effective. To overcome this limitation of Bayesian networks, this study proposes linking Bayesian networks to structural equation modeling (SEM), which has an advantage in testing causal relationships between factors. The capability of SEM in empirical validation combined with the prediction and diagnosis capabilities of Bayesian modeling facilitates effective decision making from identification of causal relationships to decision support. This study applies the proposed integrated approach to decision support for customer retention in a virtual community. The application results provide insights for practitioners on how to retain their customers. This research benefits Bayesian researchers by providing the application of modeling causal relationships at latent variable level, and helps SEM researchers in extending their models for managerial prediction and diagnosis.