外文翻譯---商業(yè)銀行的信用評分步驟

1、<p><b>　　英文原文：</b></p><p>　　A credit scoring approach for the commercial banking sector</p><p>　　Ahmet Burak Emel, Arnold Reisman and Reha Yolalan </p><p>　　Yapi Kr

2、edi Bank, Levent, 80620, Istanbul, Turkey.</p><p>　　The Graduate School of Management, Sabanci University, Istanbul, Turkey </p><p>　　Available online 15 March 2007</p><p>　　The eco

3、nomic and, therefore, the social well-being of developing countries with fairly privatized economies is highly dependent on the behavior of a country's commercial banking sector. Banks provide credit to sustain anufa

4、cturing, 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

5、 social fabric of the country as a whole and, as experien</p><p>　　Commercial banks provide financial products and services to clients while managing a set of multi-dimensional risks associated with liquidit

6、y, 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 pr

7、ovide products and services.</p><p>　　Banks are also “profit-seeking” organizations basically formed to make money for shareholders. In their typical decision-making processes (i.e. pricing, lending, funding

8、, 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, today

9、's risks may turn up as tomorrow's realities. Therefore, banks may not live without managing these risks. </p><p>　　Among the different banking risks, credit risk has a potential “social” impact beca

10、use 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, amo

11、ng 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 </p><p>　　

12、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

13、managers’ judgments and, hence, the profitability of the bank. A potential client's credit risk level is often evaluated by the bank's internal credit scoring models. Such models offer banks a means for evaluatin

14、g the risk of their credit portfolio, in a timely manner, by central</p><p>　　Over the past decade, several financial crises observed in some emerging markets enjoying a recent financial liberalization exper

15、ience, 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.

16、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 enco</p><p>　　Credit scoring has both financial and non-fina

17、ncial aspects. The scope of the current paper, however, is limited to the evaluation of a bank client's financial performance. Studies attempting to measure firm performance on the basis of qualitative data are exemp

18、lified by Bertels et al. </p><p>　　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

19、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; other

20、s dealt with improvement/optimization of a firm's financing mix. They were all directed at enhancing extant </p><p>　　One of the fields in which formal or mathematical modeling of finance theory has foun

21、d widespread application is risk measurement. A firm's financial information plays a vital role in decision making of risk-taking activities by different parties in the economy. An extensive literature dedicated to t

22、he 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 fi</p><p>

23、　　The benefits and limitations of financial ratio analysis are addressed in a widely used text on managerial finance. Financial statements report both on a firm's position at a point in time and on its operations ove

24、r 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 indust

25、ry averages for comparative purposes; (ii) inflation badly distort</p><p>　　Across different countries, sectors and/or periods of time, financial ratios that have been found useful in predicting failure diff

26、er from study to study. </p><p>　　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 predic

27、tion process. An excellent comprehensive literature survey can be found in Dimitras et al.. </p><p>　　In the late 1960s, discriminant analysis (DA) was introduced to create a composite empirical indicator of

28、 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 v

29、ery significant by univariate models, could prove somewhat useful in a discriminant function which considers the relationships among variables. Hence, he considered several va</p><p>　　Up to this point the s

30、ample 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 diffi

31、culties, but in actuality do not become bankrupt.</p><p>　　During the years that followed, many researchers attempted to increase the success of MDA in predicting business failure. Among these are Eisenbeis;

32、 Peel et al.; and Falbo. Such work also involved Turkish firms. Examples are Unal, and Ganamukkala and Karan. </p><p>　　Linear probability and multivariate conditional probability models (Logit and Probit) w

33、ere introduced to the business failure prediction literature in late 1970s. The contribution of these methods was in estimating the probability of a firm's failure. The linear probability model is a special case of o

34、rdinary least-squares regression with a dichotomous dependent variable. </p><p>　　In the 1980s, studies utilizing the recursive partitioning algorithm (RPA) based on a binary classification tree rationale we

35、re applied to this problem by Frydman et al. and Srinivasan and Kim. </p><p>　　In the 1980s and 1990s, the use of several mathematical programming techniques enriched the literature. The basic goals of these

36、 methods were to escape the assumptions and restrictions of previous techniques and to improve classification accuracy. </p><p>　　In the early 1990s, decision support systems (DSS) in conjunction with the pa

37、radigm 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 a

38、mong 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 studi</p><p

39、>　　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 fail

40、ure 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 ope

41、ned new horizons for credit scoring. </p><p>　　DEA, widely known as a non-parametric approach, is basically a mathematical programming technique developed by Charnes, Cooper and Rhodes (CCR) to evaluate the

42、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 prod

43、uctivity maximization rationale is elegantly embedded in its original fractional formulation. The capability of dealing </p><p>　　A number of studies have attempted to use statistical methods (such as discri

44、minant, 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 financ

45、ial 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,</p><p

46、>　　Although DEA was introduced in the early 1980s, its applications are acquiring more widespread recognition in the financial literature as time passes.</p><p><b>　　中文翻譯：</b></p><p

47、>　　商業(yè)銀行的信用評分步驟</p><p>　　在經(jīng)濟相當被私有化的發(fā)展中國家，經(jīng)濟福利和社會福利與國家的商業(yè)銀行業(yè)的行為有相當高的依賴性。銀行給制造業(yè)、農(nóng)業(yè)、商業(yè)服務和服務企業(yè)提供信貸。這些能提供工作、提高購買力、影響消費和儲蓄。特別是在此背景下，銀行倒閉其沖擊波會影響到該國的整個社會結(jié)構(gòu)。因此，這是當務之急，貸款/信貸決定都是一樣謹慎，盡量保持決策過程的效率性和有效性。</p><

48、p>　　商業(yè)銀行對客戶提供金融產(chǎn)品和服務的同時，還要管理一套聯(lián)系了流動資產(chǎn)、資本充足、信用、利率及匯率方面、操作和主權(quán)風險等多維風險，從這個意義上講，銀行可能會被認為是“風險機器”。他們在提供產(chǎn)品和服務時，必須承擔風險，嵌入或改造這種風險。</p><p>　　銀行也是“追求利潤”組織， 其股東基本是以賺錢為主要目的。在典型的決策過程（即價格，貸款，資金，套期保值等），他們試圖優(yōu)化其“風險-收益”權(quán)衡。 風

49、險管理和贏利的關系非常密切。風險追求是未來盈利能力的基本要求，換句話說，今天的風險也許作為明天的現(xiàn)實出現(xiàn)。所以，商業(yè)銀行部管理好風險就無法生存。</p><p>　　在不同的銀行業(yè)務風險之中， 由于賭金保管人數(shù)量和變化影響，信用危險有潛在的“社會” 沖擊。在電訊日趨成熟的現(xiàn)代社會，銀行倒閉的波動行為幾乎是在瞬間產(chǎn)生全球性沖擊。為了有效管理現(xiàn)代銀行的信用風險，輔助決策支持系統(tǒng)就需要精密的分析工具來衡量，監(jiān)測和管理，

50、和控制財務與業(yè)務風險和低效率。</p><p>　　意識到冒險的決定，呼吁定量風險管理系統(tǒng)提供銀行預警來預測潛在的企業(yè)倒閉。因此，盈利的銀行必須使有效的風險監(jiān)控單位支持經(jīng)理人的判斷。一個潛在客戶的信用風險水平常常用來評價銀行的內(nèi)部信用評分模型。這些目標，以確定申請人是否有能力償還的評估信用風險貸款，這通常是利用歷史數(shù)據(jù)和統(tǒng)計方法。這些模型能給銀行提供一種手段，以及時評估它們的風險信用組合，集中了全球風險數(shù)據(jù)并對此

51、進行了邊際分析。這些模型還可以提供有用的見解，并提供了一個重要的信息，以幫助銀行制定風險管理戰(zhàn)略。實驗驗證，數(shù)學模型是在概念上健全，輔以良好的歷史數(shù)據(jù)，并且對此執(zhí)行管理和理解，以充實業(yè)務成功的授信品質(zhì)。</p><p>　　過去十年，對幾個金融危機的觀測說明，在一些新興市場金融自由化的經(jīng)驗，表明債務融資興建的資本流入可能導致大資金突然外流，從而造成國內(nèi)的“信貸緊縮”。縱觀這些金融危機的起因表明，信貸擴張的資金主要

52、來自資本流入導致投資過高，使得銀行和公司部門易受沖擊。最近這些危機迫使銀行業(yè)監(jiān)管當局，即國際清算銀行、世界銀行、國際貨幣基金組織以及美國聯(lián)邦儲備委員會，吸取一些教訓。因此，他們鼓勵各商業(yè)銀行發(fā)展的內(nèi)部模式，以更好地量化金融風險。巴塞爾銀行監(jiān)督委員會，English和Nelson、聯(lián)邦儲備系統(tǒng)專責小組內(nèi)部信用風險模型，Lopez、Saidenberg、Treacy與Carey用最近的一些觀點和文獻來解決這些問題。</p>&

53、lt;p>　　信用計分有財政和非財務兩個方面。然而，當前文件的范圍被限制對銀行客戶的財政表現(xiàn)的評估，Bertels試圖研究以衡量公司業(yè)績的基礎上的定性數(shù)據(jù)。</p><p>　　數(shù)學建模金融理論始于50年代末， Markowitz的工作是一個重大的里程碑。財政部在60年代初，將其作為一個分學科，使其從實踐中達到了“起飛”階段。早期一些嘗試，是針對評價一個公司用于兼并和收購；一些處理利用投資組合風險管理；一些

54、人處理改進/優(yōu)化企業(yè)的融資結(jié)構(gòu)。他們都是針對增強現(xiàn)有金融理論的指導決策者。</p><p>　　其中1948年的數(shù)學建模的金融理論已廣泛應用，稱作為風險度量。在決策中形成不同黨派經(jīng)濟活動的風險，一家公司的財務信息方面發(fā)揮了重要作用。廣泛的文獻致力企業(yè)倒閉的預言，并且近年來涌現(xiàn)了信用計分的概念。財務比率是為評估和預言企業(yè)財政表現(xiàn)的最簡單的工具，財政比率分析的好處和局限演講廣泛應用在管理財務的文獻。財政決算報告堅定了

55、公司的立場和關于過去某一期間的業(yè)務。但是，仍然有一些在使用比率分析上的局限：①在許多大公司的運作，在多個不同行業(yè)。在這種情況下，很難建立有意義的一套行業(yè)平均數(shù)為比較目的；②通貨膨脹嚴重扭曲了公司的資產(chǎn)負債表。此外，記錄的價值往往大大不同于他們的“真實”的價值觀；③季節(jié)性因素可以扭曲比率分析；④公司可聘請“門面技巧”，使他們的財務報表中尋找出路；⑤很難一概而論，對某一比率，是“好”或“壞”；⑥公司可能有一些的比例，別人很難權(quán)其衡輕重和強弱

56、。</p><p>　　不同國家、部門或特定時期，在不同的學習研究中，財務比率已經(jīng)發(fā)現(xiàn)有助于預測企業(yè)是否倒閉。在文獻中，各種各樣方法的出現(xiàn)為塑造企業(yè)倒閉進行預測分析。Dimitras等人發(fā)現(xiàn)一份全面的文獻研究，來探討這些問題。</p><p>　　在60年代晚期， 判別分析（DA）被用來研究和創(chuàng)造財政比率綜合經(jīng)驗主義。Beaver使用財政比率，開發(fā)了在未通過的和非倒閉的企業(yè)之間使用單變量的

57、分析技術的顯示最大區(qū)別。單變量的方法被改進了和以后延伸到由Altman建立的多維分布分析。Altma建立的單變量的模型，能證明一些有判別作用可變量。因此，他同時考慮了幾個可變量，并使用多重判別分析（MDA）。他辯稱，MDA的優(yōu)點是考慮了對有關事務所對整個剖面的共同特點，這項研究還旨在預測未來失敗的基礎上的財務比率。但在另一方面，一項單變量的研究，認為測量只被使用為小組一次一個任務。在選擇可變物至于使用在有識別力的作用之內(nèi)，Altman

58、審查了各種各樣的供選擇的作用、相互關系在相關的可變物之間，有預測性的準確性各種各樣的外形和他自己的評斷的統(tǒng)計意義。他認為，在破產(chǎn)的2 年之前，破產(chǎn)預言模型是破產(chǎn)的一位準確預測員，并且模型的準確性極大地減少當訂貨交貨的時間的增加。竟管對MDA 的普遍用途，Altman交代對判別分析有以下弱點：</p><p>　　在隨后的歲月里，許多研究者試圖提高成功的MDA預測企業(yè)倒閉。其中Eisenbeis、Peel和Falb

59、o等。這些工作也牽涉土耳其的公司：Unal、Ganamukkala和Karan。</p><p>　　在八十年代，研究利用遞歸分割算法（RPA）基于二元分類樹理被Frydman等人應用到了這個問題，還有Srinivasan和Kim。在80年代和90年代，使用幾個數(shù)學編程技術，豐富了文獻的基本目標和方法，這些人逃避的假設和限制，以往的技術，以提高分類的準確性。</p><p>　　在90 年

60、代初期，決策支持系統(tǒng)（DSS）與多準決策法（MCDM），在財務分類問題上得到廣泛使用。Zopounidis、Mareschal、Brans、Zopounidis、Diakoulaki、Siskos、Zopounidis和Doumpos，這些人都是致力于DSS和MCDM的研究，同時神經(jīng)網(wǎng)絡方法也被運用在了企業(yè)破產(chǎn)問題上。</p><p>　　在1990年代末期， 數(shù)據(jù)包絡分析（DEA）是Troutt引入到分析信用評

61、分上的。相對于目前大致已知的MDA方法，對企業(yè)倒閉的預測（其中需要額外的先驗信息，即好/壞分類），DEA方法只需要事后信息，即觀察組的投入和產(chǎn)出，計算信用分數(shù)。因此，它開拓了新的信用評分。</p><p>　　DEA是家喻戶曉的一個非參數(shù)方法，是Charnes用在一種數(shù)學的編程技術開發(fā)的，DEA的皈依多重輸入與輸出的措施納入單位自由的單一性能指標。生產(chǎn)力最大化是嵌入其原始分數(shù)表述的一種體現(xiàn)，有能力處理了多條設定D

62、EA的優(yōu)勢比其他分析工具。在概念上，DEA方法運用到投入和產(chǎn)出，以確定相對的“最佳做法”?；谶@些最佳觀測，對環(huán)保的投入型DEA制定所產(chǎn)生的業(yè)績指數(shù)值（公信力評分），如果E小于1時，24.7%的被認為是“低效率”，是相對于有效前沿所得最佳做法；如果E等于1，24.7%位于有效值。</p><p>　　雖然在八十年代初期，隨著時間的推移，對DEA在金融文獻的介紹和應用研究是為了獲取更廣泛的承認。</p>