The assignment of credit limits with a behaviour-scoring system

Received on 1 July 1991. Behaviour-scoring systems for authorizations enable the riskof a customer defaulting to be quantified. These risks mustbe incorporated into a credit strategy which assigns creditlimits and makes authorization decisions in the most effectivemanner. This paper introduces the concept of marginal risk whichhas proved a useful tool in defining credit limit strategiesfor a mail-order company. Behaviour scores for authorizations are similar to credit applicationscores in that they predict the overall risk of a customer defaulting.If a cut-off risk can be established, then the optimal strategywould appear to be to withhold credit for customers exceedingthis risk and to grant unlimited credit for the remainder (thisis analogous to application strategies). The notion of grantingunlimited credit is often commercially unacceptable (particularlyif customers are to be informed of their credit limits!) andso strategies which give all or nothing are of limited valueand need further refinement. In order to overcome this problem, the concept of marginal riskhas been devised. The marginal risk is the risk of the ‘last£’ of an account being defaulted. This reflectsthe fact that small-balance customers may well pay off theircurrent balance only to default on larger subsequent purchases.Although the overall risk of customers with a given behaviourscore defaulting is relatively constant, their marginal riskwill vary according to their outstanding balance. This paperexplores the relationships between marginal risk and overallrisk and between marginal risk and outstanding balance. A modelwhich summarizes these relationships is proposed, and contoursof equal marginal risk are built on the basis of this model.These contours provide strategies for allocating credit limitswhich are both practical and optimal for a well formulated cut-offrisk and which suggest that the probability of defaulting isnot the best criterion for allocating credit limits. The results of the application of this approach will be demonstrated.Some of the problems that have been overcome are discussed,as are some of the outstanding problems.     

Environmental corporate responsibility for investments evaluation: an alternative multi-objective programming model

The manufacturer who is a supplier of trade credit may face non-payment risk from customers and a capital shortage problem simultaneously. Trade credit insurance, as one of the most important risk management tools, has been widely used in companies’ daily operation. In this study, the manufacturer who allows customers to delay payment for goods already delivered purchases trade credit insurance to transfer and reduce non-payment risk and borrows money from a bank to accommodate the capital constraint problem. The Stackelberg game and loss-averse theory are used to establish a newsboy model including trade credit insurance, and the optimal insurance coverage and total sales of the manufacturer are thereby investigated. Subsequently, the interest rate decision of the bank under different risk-averse situations is also characterized. We find that the interest rate set by a loss-averse bank is equal to or greater than that given by a risk-neutral bank. The use of trade credit insurance can help the manufacturer expand sales and dramatically reduce its default risk. Both the bank and the manufacturer are better off due to the use of trade credit insurance, but contrary to what one might expect, the bank prefers giving a higher interest rate to the manufacturer when the premium rate is in a reasonable region, which indicates that the manufacturer cannot use the insurance to negotiate better financing terms.     

A fraud-alert model for credit cards during the authorization process

^*Correspondence regarding this paper should be addressed to the first author In this paper, we set forth an expert-system model to help alertbanks and other financial institutions to fraudulent usage ofconsumer credit during the authorization process. The paperfirst addresses the model-building process, then briefly describesprototype development, and finally presents results from analysisof real data from a Canadian bank.     

基于银行下侧风险规避的第三方B2B平台激励策略研究

以电子仓单融资为例, 基于银行下侧风险规避角度, 研究联合授信和委托授信下当第三方B2B平台存在行为隐匿的道德风险时, 银行对B2B平台的激励策略设计问题。研究发现:B2B平台的最优努力水平随收益分配比例、回购比例的增大而减小, 随质押率、贷款利率、产品采购量、损失补偿比例的增大而增大;同时银行为规避违约风险, 需设置质押率、贷款利率和贷款额上限及回购比例下限, 并且银行最优收益分配比例与损失补偿比例、最优损失补偿比例与贷款损失率均成正相关关系。此外, 随着B2B平台工作效率的提高, 联合授信下最优收益分配比例将减小, 最优损失补偿比例将增大, 最终近似于委托授信下的最优损失补偿比例。最后给出数值分析。     

银行项目信贷风险决策分析

利用实物期权的方法对银行项目信贷的期权特性进行了讨论,对项目的收益与风险进行了更为准确的数理分析.为使结论更加准确可靠,并对具体的实例进行了剖析,说明了衍生产品在银行的项目信贷过程中也可以很好地理论指导实践,使银行能够更好地对贷前风险进行有效控制.     

Effects of risk aversion and decision preference on equilibriums in supply chain finance incorporating bank credit with credit guarantee

We constructed a Stackelberg game in a supply chain finance (SCF) system including a manufacturer, a capital‐constrained retailer, and a bank that provides loans on the basis of the manufacturer's credit guarantee. To emphasize the financial service providers' risks, we assumed that both the bank and the manufacturer are risk‐averse and formulated trade‐off objective functions for both of them as the convex combination of the expected profit and conditional value‐at‐risk. To explore the effects of the risk preferences and decision preferences on SCF equilibriums, we mathematically analyzed the optimal order quantities, wholesale prices, and interest rates under different risk preference scenarios and performed numerical analyses to quantify the effects. We found that incorporating bank credit with a credit guarantee can effectively balance the retailer's financing risk between the bank and the manufacturer through interest rate charging and wholesale pricing. Moreover, SCF equilibriums with risk aversion are highly affected by the degree of both the lender's and guarantor's risk tolerance in regard to the borrower's default probability and will be more conservative than those in the risk‐neutral cases that only maximize expected profit.     

模糊影响图评价算法在供应链金融信用风险评估中的应用

传统的银行信贷模式风险评价专注于个体企业的财务数据.供应链金融新融资模式下的信用风险评价不同于传统的融资模式风险评价,它的评价范围更宽,不确定性因素更加复杂.在分析供应链金融模式的信用风险评价体系的基础上,结合模糊集和影响图理论建立了模糊影响图评价模型,对评估中难以量化的问题进行模糊处理,对变量之间的模糊影响关系进行分析,最后计算出信用风险概率分布.方法定性与定量相结合,为供应链金融新模式下的风险评估提供了一种新思路.     

出口海陆仓融资决策多目标优化模型与算法

授信额度分配决策是出口海陆仓融资业务中的重要问题,为权衡该项业务中的风险和收益,综合考虑出口国家政治风险、风险价值约束以及信用风险等因素,构建以收益最高和风险最低为目标的优化模型。针对所构建模型的特点,提出一种将模拟退火与NSGA-II算法相结合的混合算法对模型进行求解。最后,通过具体实例,分别以现实中常见的1~5个月质押期为例,验证了模型和算法的适用性及有效性。     

Enterprise risk management: coping with model risk in a large bank

Enterprise risk management (ERM) has become an important topic in today's more complex, interrelated global business environment, replete with threats from natural, political, economic, and technical sources. Banks especially face financial risks, as the news makes ever more apparent in 2008. This paper demonstrates support to risk management through validation of predictive scorecards for a large bank. The bank developed a model to assess account creditworthiness. The model is validated and compared to credit bureau scores. Alternative methods of risk measurement are compared.     

Multistage decision-making fuzzy methodology for optimal investments based on experts’ evaluations

A new methodology of making a decision on an optimal investment in several projects is proposed. The methodology is based on experts’ evaluations and consists of three stages. In the first stage, Kaufmann’s expertons method is used to reduce a possibly large number of applicants for credit. Using the combined expert data, the credit risk level is determined for each project. Only the projects with low risks are selected.     

基于BP算法的信用风险评价模型研究

本文利用神经网络技术建立基于 BP算法的信用风险评价模型 ,为我国某商业银行 12 0家贷款企业进行信用风险评价 ,按照企业的信用等级分为“信用好”、“信用中等”和“信用差”三个小组 .仿真结果表明 ,本文所建立的神经网络信用风险评价模型的分类准确率高于传统的参数统计分类方法——线性判别分析法的分类准确率 .文中还详细给出神经网络信用风险评价模型的网络构建方法及基于 BP网络的学习算法和步骤 .     

基于信用额度和外生违约概率的出口海陆仓融资质押率优化

质押率优化是出口海陆仓融资决策的核心内容。针对出口商信用、出口商与进口商外生违约以及质押物价值波动三重叠加风险下的出口海陆仓融资决策问题,在设定出口商信用额度,且给定出口商和进口商外生违约概率的前提下,依据双重Stackelberg博弈原理,以供应链协同均衡下的出口商、进口商和船公司的期望利润最大化为目标,建立了质押率与订货量及货物价格联动优化模型,设计了微分法和逆向归纳法求解模型。算例验证了模型和方法的适用性和有效性。敏感性分析结果表明质押率与出口商信用额度呈负相关关系,对出口商和进口商外生违约概率不敏感。研究结论可为出口海陆仓融资优化决策提供科学参考。     

基于供应链金融的供应商链式融资策略研究

供应链中由于核心企业延期付款而给上游供应商带来资金压力,供应商、核心企业以及银行组成供应商链式融资系统以缓解供应商资金压力。针对供应商初始资金以及银行给定信用期的不同情况,分别建立了供应商不贷款、贷款且在信用期内收回所有货款以及贷款但在信用期内未收回所有货款三种情形下的数学模型。通过分析得到了核心企业的最优订货策略和供应商的最优生产策略。研究表明供应商链式融资在一定程度上可以达到供应商、核心企业、银行三方共赢的效果:缓解供应商、核心企业资金压力并增加其利润,降低银行贷款风险扩大其贷款业务。     

Discovering Credit Cardholders’ Behavior by Multiple Criteria Linear Programming

In credit card portfolio management, predicting the cardholder’s spending behavior is a key to reduce the risk of bankruptcy. Given a set of attributes for major aspects of credit cardholders and predefined classes for spending behaviors, this paper proposes a classification model by using multiple criteria linear programming to discover behavior patterns of credit cardholders. It shows a general classification model that can theoretically handle any class-size. Then, it focuses on a typical case where the cardholders’ behaviors are predefined as four classes. A dataset from a major US bank is used to demonstrate the applicability of the proposed method.     

上市公司信用风险分析模型中的变量选择

当前上市公司信用风险数据所呈现出的高维度以及高相关性的特点严重影响了信用风险模型的准确性。为此本文结合已有算法以及信用风险模型的特点设计了一种新的基于非参数的变量选择方法。通过该方法对上市公司用风险相关变量进行分析筛选可以消除数据集中包含的噪声变量以及线性相关变量。本文同时还针对该方法设计了高变量维度下最优解求解算法。文章以Logistic模型为例对上市公司信用风险做了实证分析,研究结果表明与以往的变量选择方法相比该方法可以有效的降低数据维度,消除变量间的相关性,并同时提高模型的可靠性和预测精度。     

信贷风险决策问题的一种相互逼近算法

基于最速下降法的基本思想 ,提出了相互逼近算法 ,用以解决信贷风险决策过程中 ,利润曲线和风险曲线寻求公共最优近似解的问题 .该算法表明 ,当利润曲线和风险曲线不存在公共最优近似解时 ,银行追求利润最大化的结果将导致风险上升 ,无法在可接受的风险指数范围内实现其既定的盈利目标 .但当利润曲线和风险曲线存在公共最优近似解时 ,银行根据其所掌握的私有信息以及所观测到企业理性的反应 ,作出相应的决策 .公共最优近似解的存在 ,说明了银行是在风险可接受的前提下按最优性原则给企业发放贷款 .     

A heuristic multi-start decomposition approach for optimal design of serial machining lines

We study an optimal design problem for serial machining lines. Such lines consist of a sequence of stations. At every station, the operations to manufacture a product are grouped into blocks. The operations within each block are performed simultaneously by the same spindle head and the blocks of the same station are executed sequentially. The inclusion and exclusion constraints for combining operations into blocks and stations as well as the precedence constraints on the set of operations are given. The problem is to group the operations into blocks and stations minimizing the total line cost. A feasible solution must respect the given cycle time and all given constraints. In this paper, a heuristic multi-start decomposition approach is proposed. It utilizes a decomposition of the initial problem into several sub-problems on the basis of a heuristic solution. Then each obtained sub-problem is solved by an exact algorithm. This procedure is repeated many times, each time it starts with a new heuristic solution. Computational tests show that the proposed approach outperforms simple heuristic algorithms for large-scale problems.     

The challenge in managing new financial risks: adopting an heuristic or theoretical approach

The financial crisis began with the collapse of Lehman Brothers and the subprime asset backed securities debacle. Credit risk was turned into liquidity risk, resulting in a lack of confidence among financial institutions. In this article, we will propose a way to model liquidity risk and the credit risk in best practices. We will show that liquidity risk is a new type of risk and the current way to deal with it is based solely on observed variables without any theoretical link. We propose an heuristic approach to combine the numerous liquidity risk indicators with a logistic regression for the first time. In regards to credit risk, several articles prove that the best practice is to use an option model to appreciate this risk. We will present our methodology using stochastic diffusion for the interest rate because currently the yield curves aren’t liquid. This approach is more relevant because the basis model in prior publications has a constant interest rate or a forward rate. Both models allow a better understanding of liquidity and credit risks and the further development of research deals with the link between these two financial risks.     

基于综合风险收益的贷款组合优化决策

商业银行货款组合决策的过程,是遵循“效益性、安全性、流动性”的原则,在综合考虑贷款收益和风险的前提下,从众多的贷款对象中选择一组合适的贷款对象的过程。建立综合考虑贷款收益和风险的贷款决策模型,有利于银行通过量化计算进行科学决策,以提高信贷质量,达到商业银行的经营目标。     

随机产出与需求下农户融资模式选择研究

农业是国民经济的基础,但农业融资一直存在许多困难。本文在考虑农户的破产风险,产出随机性以及市场需求不确定的基础之上,构建由单一公司与资金约束的单一农户构成的订单农业供应链决策模型,得到了农户在面对银行信贷、贸易信贷、组合信贷时的最优决策的选择策略。研究表明,当公司利率大于银行利率且农产品生长周期较长,或者当公司利率小于银行利率且农产品生产周期较短时,农户应在银行利率较小时选择银行信贷,在银行利率较大时选择贸易信贷。而在其余情况中,农户需要综合考虑农产品价格敏感系数等因素来决定是否选择组合信贷模式。