外文翻译---商业银行的信用评分步骤.docx

英文原文：A credit scoring approach for the commercial banking sectorAhmet Burak Emel, Arnold Reisman and Reha Yolalan Yapi Kredi Bank, Levent, 80620, Istanbul, Turkey. The Graduate School of Management, Sabanci University, Istanbul, Turkey Available online 15 March 2007The economic and, therefore, the social well-being of developing countries with fairly privatized economies is highly dependent on the behavior of a countrys commercial banking sector. Banks provide credit to sustain anufacturing, agricultural, commercial and service enterprises. These, in turn, provide jobs thus enhancing purchasing power, consumption, and savings. Bank failures, especially in such settings, send shockwaves affecting the social fabric of the country as a whole and, as experienced recently, (Latin America and Asia) have the potential of a quick global impact. Thus, it is imperative that lending/credit decisions are made as prudently as possible while keeping the decision making process both efficient and effective. Commercial banks provide financial products and services to clients while managing a set of multi-dimensional risks associated with liquidity, capital adequacy, credit, interest and foreign exchange rates, operating and sovereign risks, etc. In this sense, banks may be considered to be “risk machines”. They take risks, and transform or embed such risks to provide products and services.Banks are also “profit-seeking” organizations basically formed to make money for shareholders. In their typical decision-making processes (i.e. pricing, lending, funding, hedging, etc.), they try to optimize their “risk-return” trade-off. Management of risk and of profitability are very closely related. Risk taking is the basic requirement for future profitability. In other words, todays risks may turn up as tomorrows realities. Therefore, banks may not live without managing these risks. Among the different banking risks, credit risk has a potential “social” impact because of the number and diversity of stakeholders affected. Business failures affect shareholders, managers, lenders (banks), suppliers, clients, the financial community, government, competitors, and regulatory bodies, among others. In the age of telecommunications, the ripple effect of a bank failure is virtually instantaneous and such ripples hold the potential of global impact. In order to effectively manage the credit risk exposure of a modern bank, there is thus a strong need for sophisticated decision support systems backed by analytical tools to measure, monitor, manage, and control, financial and operational risks and inefficiencies. Conscious risk-taking decisions call for quantitative risk-management systems, which, in turn, provide the bank early warnings for predicting potential business failures. Thus, an effective risk-monitoring unit supports managers judgments and, hence, the profitability of the bank. A potential clients credit risk level is often evaluated by the banks internal credit scoring models. Such models offer banks a means for evaluating the risk of their credit portfolio, in a timely manner, by centralizing global-exposures data and by analyzing marginal as well as absolute contributions to risk components. These models can offer useful insight and do provide an important body of information to help a bank formulate its risk management strategies. Models that are conceptually sound, empirically validated, backed by good historical data, understood and implemented by management, augment the business success of credit quality. Over the past decade, several financial crises observed in some emerging markets enjoying a recent financial liberalization experience, showed that debt financing built on capital inflow may result in large and sudden capital outflows, thereby causing a domestic “credit crunch”. Experience with these recent crises forced banking authorities, i.e. the Bank of International Settlements (BIS), the World Bank, the IMF, as well as the Federal Reserve. to draw a number of lessons. Hence, they all encourage commercial banks to develop internal models to better quantify financial risks. The Basel Committee on Banking Supervision, English and Nelson, the Federal Reserve System Task Force on Internal Credit Risk Models.Lopez and Saidenberg and Treacy and Carey represent some recent documents addressing these issues. Credit scoring has both financial and non-financial aspects. The scope of the current paper, however, is limited to the evaluation of a bank clients financial performance. Studies attempting to measure firm performance on the basis of qualitative data are exemplified by Bertels et al. Formal or mathematical modeling of finance theory began in the late 1950s. The work of Markowitz represents a major milestone. The practice reached its “take-off” stage as a sub-discipline of Finance during the early 1960s. Some of the early efforts were directed at evaluating a firm for purposes of mergers and acquisitions; some dealt with using investment portfolios to manage risk; others dealt with improvement/optimization of a firms financing mix. They were all directed at enhancing extant finance theory toward the goal of guiding decision-makers. One of the fields in which formal or mathematical modeling of finance theory has found widespread application is risk measurement. A firms financial information plays a vital role in decision making of risk-taking activities by different parties in the economy. An extensive literature dedicated to the prediction of business failure as well as credit scoring concepts has emerged in recent years. Financial ratios are the simplest tools for evaluating and predicting the financial performance of firms. They have been used in the literature for many decades. The benefits and limitations of financial ratio analysis are addressed in a widely used text on managerial finance. Financial statements report both on a firms position at a point in time and on its operations over some past period. However, there are still some limitations in using ratio analysis: (i) many large firms operate in a number of different industries. In such cases it is difficult to develop a meaningful set of industry averages for comparative purposes; (ii) inflation badly distorts a firms balance sheet. Moreover, recorded values are often substantially different from their “true” values; (iii) seasonal factors can distort a ratio analysis; (iv) firms can employ “window dressing techniques” to make their financial statements look stronger; (v) it is difficult to generalize about whether a particular ratio is “good” or “bad”; and (vi) a firm may have some ratios looking “good” and others looking “bad” making it difficult to tell whether the firm is, on balance, strong or weak. Across different countries, sectors and/or periods of time, financial ratios that have been found useful in predicting failure differ from study to study. To deal with the above shortcomings of unidimensional financial ratio analysis, a variety of methods have appeared in the literature for modeling the business failure prediction process. An excellent comprehensive literature survey can be found in Dimitras et al. In the late 1960s, discriminant analysis (DA) was introduced to create a composite empirical indicator of financial ratios. Using financial ratios, Beaver developed an indicator that best differentiated between failed and non-failed firms using univariate analysis techniques. Altman established that ratios found not to be very significant by univariate models, could prove somewhat useful in a discriminant function which considers the relationships among variables. Hence, he considered several variables simultaneously using multiple discriminant analysis (MDA). He argued that MDA had the advantage of considering an entire profile of interrelated characteristics common to the relevant firms. That study also aimed to predict future failure on the basis of financial ratios. He concluded that his bankruptcy prediction model was an accurate forecaster of failure for up to 2 years prior to bankruptcy and that the models accuracy diminishes substantially as the lead-time increases. In spite of widespread use of MDA, Altman, confesses to the following weakness of discriminant analysis: Up to this point the sample firms were chosen either by their bankruptcy status (Group 1) or by their similarity to Group 1 in all aspects except their economic well being. But what of the many firms which suffer temporary profitability difficulties, but in actuality do not become bankrupt.During the years that followed, many researchers attempted to increase the success of MDA in predicting business failure. Among these are Eisenbeis; Peel et al.; and Falbo. Such work also involved Turkish firms. Examples are Unal, and Ganamukkala and Karan. Linear probability and multivariate conditional probability models (Logit and Probit) were introduced to the business failure prediction literature in late 1970s. The contribution of these methods was in estimating the probability of a firms failure. The linear probability model is a special case of ordinary least-squares regression with a dichotomous dependent variable. In the 1980s, studies utilizing the recursive partitioning algorithm (RPA) based on a binary classification tree rationale were applied to this problem by Frydman et al. and Srinivasan and Kim. In the 1980s and 1990s, the use of several mathematical programming techniques enriched the literature. The basic goals of these methods were to escape the assumptions and restrictions of previous techniques and to improve classification accuracy. In the early 1990s, decision support systems (DSS) in conjunction with the paradigm of multi-criteria decision-making (MCDM), were introduced to financial classification problems. Zopounidis, Mareschal and Brans Zopounidis et al. Diakoulaki et al., Siskos et al. and Zopounidis and Doumpos were among the studies that measured firm performance aiming at predicting business failure by making use of DSS and MCDM. The ELECTRE method of Roy and the Rough Sets Method of Dimitras et al. represent studies addressing these issues. Development and application of artificial intelligence resulted in the use of expert systems. Neural Network methods were applied to the bankruptcy problem as well. In the late 1990s, data envelopment analysis (DEA) was introduced to the analysis of credit scoring as in Troutt et al., Simak, and Cielen and Vanhoof. As opposed to the broadly known MDA approach for business failure prediction (which requires extra a priori information, i.e. good/bad classification), DEA requires solely ex-post information, i.e. the observed set of inputs and outputs, to calculate the credit scores. Thus, it opened new horizons for credit scoring. DEA, widely known as a non-parametric approach, is basically a mathematical programming technique developed by Charnes, Cooper and Rhodes (CCR) to evaluate the relative efficiency of “decision making units” (DMUs). DEA converts a multiplicity of input and output measures into a unit-free single performance index formed as a ratio of aggregated output to aggregated input. A productivity maximization rationale is elegantly embedded in its original fractional formulation. The capability of dealing with multi-input/multi-output settings provides DEA an edge over other analytical tools. Conceptually, DEA compares the DMUs observed outputs and inputs in order to identify the relative “best practices” for a chosen observation set. Based on these best observations, an efficient frontier is established and the degree of efficiency of other units with respect to the efficient frontier is then measured. Based on its input-oriented DEA formulation, the resulting performance index value (the credibility score, in our context) provides a numerical value E. E lies between zero and one. If E is less than one, the DMU is considered “inefficient” as compared to the efficient frontier derived from best practices. If E is equal to one, the DMU is located on the efficient frontier. Therefore, it can be said that E measures the relative credit riskiness of firms within the bank portfolio. A number of studies have attempted to use statistical methods (such as discriminant, Logit and Probit analyses) with financial ratios to generate early warning signals for distressed banking institutions The idea is to develop meaningful “peer group analysis”, that is, to develop specific financial characteristics that distinguish between two or more groups, for example, failed and non-failed banks, or problem and non-problem banks, with relatively “good” or “bad” financial conditions. However, except when a priori groups are available to provide certain financial profiles for comparison, identifying appropriate peer group analysis is a difficult task. Data envelopment analysis (DEA), which computes a firms efficiency by transforming inputs into outputs relative to its peers, may provide a fine mechanism for deriving appropriate categories for this purpose. An advantage of DEA is that, it uses actual sample data to derive the efficiency frontier against which each unit in the sample is evaluated with no a priori information regarding which inputs and outputs are most important in the evaluation procedure. Instead, the efficient frontier is generated, when a mathematical algorithm is used to calculate the DEA efficiency score for each unit.Although DEA was introduced in the early 1980s, its applications are acquiring more widespread recognition in the financial literature as time passes.中文翻译：商业银行的信用评分步骤在经济相当被私有化的发展中国家，经济福利和社会福利与国家的商业银行业的行为有相当高的依赖性。银行给制造业、农业、商业服务和服务企业提供信贷。这些能提供工作、提高购买力、影响消费和储蓄。特别是在此背景下，银行倒闭其冲击波会影响到该国的整个社会结构。因此，这是当务之急，贷款/信贷决定都是一样谨慎，尽量保持决策过程的效率性和有效性。商业银行对客户提供金融产品和服务的同时，还要管理一套联系了流动资产、资本充足、信用、利率及汇率方面、操作和主权风险等多维风险，从这个意义上讲，银行可能会被认为是“风险机器”。他们在提供产品和服务时，必须承担风险，嵌入或改造这种风险。银行也是“追求利润”组织， 其股东基本是以赚钱为主要目的。在典型的决策过程（即价格，贷款，资金，套期保值等），他们试图优化其“风险-收益”权衡。 风险管理和赢利的关系非常密切。风险追求是未来盈利能力的基本要求，换句话说，今天的风险也许作为明天的现实出现。所以，商业银行部管理好风险就无法生存。在不同的银行业务风险之中， 由于赌金保管人数量和变化影响，信用危险有潜在的“社会” 冲击。在电讯日趋成熟的现代社会，银行倒闭的波动行为几乎是在瞬间产生全球性冲击。为了有效管理现代银行的信用风险，辅助决策支持系统就需要精密的分析工具来衡量，监测和管理，和控制财务与业务风险和低效率。意识到冒险的决定，呼吁定量风险管理系统提供银行预警来预测潜在的企业倒闭。因此，盈利的银行必须使有效的风险监控单位支持经理人的判断。一个潜在客户的信用风险水平常常用来评价银行的内部信用评分模型。这些目标，以确定申请人是否有能力偿还的评估信用风险贷款，这通常是利用历史数据和统计方法。这些模型能给银行提供一种手段，以及时评估它们的风险信用组合，集中了全球风险数据并对此进行了边际分析。这些模型还可以提供有用的见解，并提供了一个重要的信息，以帮助银行制定风险管理战略。实验验证，数学模型是在概念上健全，辅以良好的历史数据，并且对此执行管理和理解，以充实业务成功的授信品质。过去十年，对几个金融危机的观测说明，在一些新兴市场金融自由化的经验，表明债务融资兴建的资本流入可能导致大资金突然外流，从而造成国内的“信贷紧缩”。纵观这些金融危机的起因表明，信贷扩张的资金主要来自资本流入导致投资过高，使得银行和公司部门易受冲击。最近这些危机迫使银行业监管当局，即国际清算银行、世界银行、国际货币基金组织以及美国联邦储备委员会，吸取一些教训。因此，他们鼓励各商业银行发展的内部模式，以更好地量化金融风险。巴塞尔银行监督委员会，English和Nelson、联邦储备系统专责小组内部信用风险模型，Lopez、Saidenberg、Treacy与Carey用最近的一些观点和文献来解决这些问题。信用计分有财政和非财务两个方面。然而，当前文件的范围被限制对银行客户的财政表现的评估，Bertels试图研究以衡量公司业绩的基础上的定性数据。数学建模金融理论始于50年代末， Markowitz的工作是一个重大的里程碑。财政部在60年代初，将其作为一个分学科，使其从实践中达到了“起飞”阶段。早期一些尝试，是针对评价一个公司用于兼并和收购；一些处理利用投资组合风险管理；一些人处理改进/优化企业的融资结构。他们都是针对增强现有金融理论的指导决策者。其中1948年的数学建模的金融理论已广泛应用，称作为风险度量。在决策中形成不同党派经济活动的风险