...GIỚI THIỆU MỘT SỐ MÔ HÌNH XẾP HẠNG TÍN DỤNG KHÁCH HÀNG GIẢI PHÁP GIẢM THIỂU NỢ XẤU TS. Đào Minh Phúc Phó Hiệu trưởng Trường bồi dưỡng Cán bộ Ngân hàng Ngân hàng Nhà nước Việt nam 1. Sự cần thiết thực hiện xếp hạng tín dụng nội bộ: Năm 2012, một năm đầy những khó khăn thử thách đối với nền kinh tế nói chung và đối với hệ thống Ngân hàng nói riêng với 3 nhiệm vụ hết sức nặng nề đó là cơ cấu lại các doanh nghiệp, hoạt động đầu tư và cơ cấu lại hệ thống ngân hàng. Cơ cấu lại hệ thống ngân hàng là một công việc hết sức khó khăn, phức tạp – đây không chỉ là nhiệm vụ của toàn ngành ngân hàng mà trong đó còn là nhiệm vụ của các doanh nghiệp và toàn xã hội. Để cơ cấu lại hệ thống ngân hàng đạt kết quả, công việc đầu tiên phải giải quyết tốt và phải giải quyết trước là vấn đề nợ xấu ở các TCTD và NHTM. Cho đến nay, nợ xấu của toàn ngành lên tới trên 200 ngàn tỷ ĐVN chiếm trên 8% tổng dư nợ của các NHTM và TCTD. Tỷ lệ nợ xấu cao như hiện nay là hệ quả tất yếu của nhiều năm chất lượng tín dụng yếu kém ở các NHTM mà một trong những nguyên nhân chính là quản trị rủi ro tín dụng của NHTM yếu kém. Do vậy, nếu chúng ta chỉ quan tâm đến xử lý nợ xấu mà không thực hiện ngay và đồng thời các giải pháp để nâng cao chất lượng tín dụng thì sau vài năm tiếp theo nợ xấu còn nhiều hơn với những tác hại lớn hơn bây giờ và không bao giờ giải quyết được triệt để. . Mặt khác, để phát triển và tăng trưởng kinh tế, lẽ đương nhiên phải thúc đẩy và kích thích đầu tư và như vậy phải tăng trưởng tín dụng. Tuy...
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...A good credit score can save consumers money with lower interest rates because lenders use it to determine credit risk. Lenders use different tools to determine risk; the most widely used tool is the FICO Credit Scoring system. Maintaining a satisfactory credit score is important because lenders consider this an important tool for determining credit risk. According to the Fair Credit Reporting Act, the definition of a credit score is “a numerical value or a categorization derived from a statisticaltool or modeling system used by a person who makes or arranges a loanto predict the likelihood of certain credit behaviors, including default (and the numerical value or the categorization derived from such analysis may also be referred to as a “risk predictor” or “risk score”); and does not include any mortgage score or rating of an automated underwriting system that considers one or more factors in addition to credit information, including the loan to value ratio, the amount of down payment, or the financial assets of a consumer; or any other elements of the underwriting process or underwriting decision” (FCRA §609(f) (2)). Although there are different types of credit scoring models, the most widely used is the FICO scoring system, created by Fair, Isaac, and Company. The factors that make up this score are as follows: payment history, new credit, amounts owed, length of credit history, and types of credit used. The length of time used for creating the score is the...
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...About Ratings & Segments on IRB Approach João Pires da Cruz1 Introduction The Basel Committee on Banking Supervision, on the process of definition of the New Capital Accord, establishes a stepwise framework for regulatory capital allocation for credit risk, starting on what is designated as Standard Approach, in which banks must allocate capital according to regulatory rules, and finishing on what is designated as the Advanced IRB Approach, in which banks must allocate capital based on their own risk evaluation and on the committee guidelines for that evaluation. The committee defines several guidelines for the IRB Approach depending on the type of credit exposure but, technically, we can group the several lines of attach into two ways of deal with the credit portfolio, the rating approach, for the major exposures like banks, sovereigns and corporate; and the segmentation approach for retail and small business exposures. The most accepted credit risk frameworks are rating based models since, historically, the aim of the models was the bond market, the market of debt securities issued by stable corporations, banks and states. In this market, the assumption that a debt security is less risky than other debt security become the essence of the market, since debt issuers need to disclose information to lower the price of the debt security, affected by a risk premium over the interest rate. And the disclosed information includes rating agencies evaluations of financial figures...
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...Reject inference applied to large data sets Customer Inserts His/Her Name Customer Inserts Grade Course Customer Inserts Tutor’s Name Writer Inserts Date Here (Day, Month, Year) Reject inference applied to large data sets Introduction One of the most common use of reject inference technique is negotiation and application scoring. When prospective customers approaches a bank for a loan, it is important to evaluate their credit worthiness or rather if they are likely to default on the loan. Therefore, appropriate models are usually applied, which are pegged upon the bank’s previous performance, and on discovering the fundamental characteristics that could be useful in establishing the prospects of new customers. Apparently,...
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...Course Project Part II: Bertram Proprietary Credit Scoring Model Jordan Spence Student ID: D01326483 In partial fulfillment of the requirements for MATH 533 – Applied Managerial Statistics Keller Graduate School of Management Dr. Gerard L. Kiely June 24, 2015 PROJECT PART II: Bertram Proprietary Credit Scoring Model The preliminary analysis carried out in Part I of our project has shown that the data is consistent and reliable, with no missing values. The next step is to construct preliminary and final models. A. Create indicator (dummy) variables for the qualitative variables Own/Rent and Location using Minitab. First, label columns for the new indicator variables: Own, Rent, Urban, Suburban, Rural. Pull down the Calc menu in Minitab and select “Make Indicator Variables”. In the box labeled “Indicator Variables for … “ put the variable for which indicators are desired. Minitab will automatically code the values of Own/Rent and create two new variables named “Own” and “Rent”. Repeat this process for the variable Location. See screenshot below for new indicator variables: [pic] B. Develop a preliminary model and display its output in your paper. Describe its statistical characteristics and state your conclusions. Identify which variables you will keep and those you will drop. Be sure to explain why you made your choices. Be specific. ...
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...kredito rizikos vertinimo įmonėje credit domain (SMEs and larger businesses), that can guide lenders when choosing kredito domenas (MVĮ ir didesnėmis įmonėmis), kurios gali padėti skolintojams renkantis appropriate data and tools. atitinkami duomenys ir įrankiai. Traditionally, lenders relied upon judgmental assessments of Tradiciškai, skolintojai, remtis subjektyvių nuomonių vertinimais the five Cs (capacity, capital, character, collateral, and conditions), but modern 5 Ca (galia, kapitalas, charakteris, įkaitas, ir sąlygos), bet modernus technology has allowed them to amass and capitalise on data. technologija leido jiems kaupti ir pasinaudoti duomenų. Besides judgment, lenders Be nuovoką, skolintojų can also apply scoring, reduced-form, and structural models—with the choice being taip pat gali taikyti įvertinimas balais, sumažinta forma, ir struktūrinio modelių pasirinkimas yra dependent upon the size and nature of the firms being assessed. priklauso nuo vertinamos įmonių dydį ir pobūdį. For the largest Didžiausias companies with traded securities, reduced-form and structural models can be used to bendrovėms, turinčioms vertybinių popierių apyvartą, sumažinti forma ir struktūrinių modelių gali būti naudojamas interpret their prices and price movements. interpretuoti savo kainas ir kainų svyravimus. In contrast, credit scoring is used mostly in Tuo tarpu kredito vertinimo, naudojamas daugiausia data-rich small-business credit environments, but can add value elsewhere...
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...business agility, and contains some interesting lessons for SOA implementation. The case study was written by Alcedo Coenen. Alcedo has built his experience in IT since 1987, although he originally graduated in musicology in 1986. He has been working as programmer, information analyst, and since 1997 as (information) architect for ING and other companies in the Netherlands. Within ING Alcedo has been working on multi-channel architecture, a global SOA for ING Europe, a credit card system and on knowledge systems. Recently he has established a working group on the Business Rules Approach, producing articles and presentations for several architecture conferences and meetings. Open Group SOA Case Study http://www.opengroup.org Open Group SOA Case Study SOA Agility in Practice Service orientation within one application1 Alcedo Coenen alcedo.coenen@gmail.com ING Card2 built an application for its customer base that enables it to link to new websites, implement new product features, and maintain credit scoring rules, easily and quickly. It achieved this by applying three construction principles, one of which was service orientation. This article describes the...
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...≈√ Guidelines on Credit Risk Management Rating Models a n d Va l i d a t i o n These guidelines were prepared by the Oesterreichische Nationalbank (OeNB) in cooperation with the Financial Market Authority (FMA) Published by: Oesterreichische Nationalbank (OeNB) Otto Wagner Platz 3, 1090 Vienna, Austria Austrian Financial Market Authority (FMA) Praterstrasse 23, 1020 Vienna, Austria Produced by: Oesterreichische Nationalbank Editor in chief: Gunther Thonabauer, Secretariat of the Governing Board and Public Relations (OeNB) ‹ Barbara Nosslinger, Staff Department for Executive Board Affairs and Public Relations (FMA) ‹ Editorial processing: Doris Datschetzky, Yi-Der Kuo, Alexander Tscherteu, (all OeNB) Thomas Hudetz, Ursula Hauser-Rethaller (all FMA) Design: Peter Buchegger, Secretariat of the Governing Board and Public Relations (OeNB) Typesetting, printing, and production: OeNB Printing Office Published and produced at: Otto Wagner Platz 3, 1090 Vienna, Austria Inquiries: Oesterreichische Nationalbank Secretariat of the Governing Board and Public Relations Otto Wagner Platz 3, 1090 Vienna, Austria Postal address: PO Box 61, 1011 Vienna, Austria Phone: (+43-1) 40 420-6666 Fax: (+43-1) 404 20-6696 Orders: Oesterreichische Nationalbank Documentation Management and Communication Systems Otto Wagner Platz 3, 1090 Vienna, Austria Postal address: PO Box 61, 1011 Vienna, Austria Phone: (+43-1) 404 20-2345 Fax: (+43-1) 404 20-2398 Internet: ...
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...Psicothema 2006. Vol. 18, supl., pp. 34-41 www.psicothema.com ISSN 0214 - 9915 CODEN PSOTEG Copyright © 2006 Psicothema Measuring emotional intelligence with the Mayer-Salovery-Caruso Emotional Intelligence Test (MSCEIT) Marc A. Brackett and Peter Salovey Yale University This manuscript examines the measurement instrument developed from the ability model of EI (Mayer and Salovey, 1997), the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT; Mayer, Salovey and Caruso, 2002). The four subtests, scoring methods, psychometric properties, reliability, and factor structure of the MSCEIT are discussed, with a special focus on the discriminant, convergent, predictive, and incremental validity of the test. The authors review associations between MSCEIT scores and important outcomes such as academic performance, cognitive processes, psychological wellbeing, depression, anxiety, prosocial and maladaptive behavior, and leadership and organizational behavior. Findings regarding the low correlations between MSCEIT scores and self-report measures of EI also are presented. In the conclusion the authors’ provide potential directions for future research on emotional intelligence. La medida de la inteligencia emocional con el Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT). Este artículo examina el instrumento de medida desarrollado desde el modelo de habilidad de IE (Mayer y Salovey, 1997), el Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT; Mayer, Salovey y...
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...In this week’s lecture, we looked at the credit risk management. Credit risk is defined as the potential for loss due to a counterparty failing to meet its financial obligations in accordance with agreed terms. The sources of credit risk for the bank are summarized as direct lending, traditional off-balance sheet business, investment and capital market operations, etc. For the commercial banks the major risk is the credit risk which accounted by 50%-60% of total risk, far more compared to market risk and operational risk. Thus the credit risk management is quite important. The managed way is measured on the nature of counterparty (retail credit, corporate credit and business banking credit) they are measured differently from each other. EG:The retail business has lots of transactions but relatively small amounts related to individuals, which adopt a proprietary scoring model. It is considered the factors as appropriate weights, historical data on defaulted and good loans, information provided by the credit applicant etc. Then, the SMEs credit model used to predict the probability of default of bankruptcy. The Z-score is derived from 5 financial ratios using 8 inputs from the financial statements; a low Z-score, the higher risks of bankruptcy. Third, for the large companies, the credit rating is provided involve detailed analysis. The rating agency demonstrates the S&P’s investment grade ratings as AAA,AA,A; BBB,BB.B…from strongest capacity to meet financial obligation...
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...Credit risk Credit risk is a fast changing discipline at the leading edge of risk management practice. The recent credit crisis brought into focus the need for effective risk management control and highlighted many of the deficiencies of the banks’ approach to measuring credit risk. This has resulted in many financial institutions reviewing their existing approach to the management of credit risk from a process, organisational and systems perspective. At the same time, many institutions are also continuing to develop more sophisticated methods of risk management, such as measuring and hedging Credit Valuation Adjustments (CVA) and modelling economic capital and incremental risk Definitions of Credit risk: ❖ Credit risk is the risk of loss due to a debtor's non-payment of a loan or other line of credit (either the principal or interest (coupon) or both). ❖ Is the risk that another party to an investment transaction will not fulfill its obligations. Credit risk can be associated with the issuer of ❖ The likelihood that an individual will pay his or her credit obligations as agreed. Borrowers who are more likely to pay as agreed pose less risk to creditors and lenders. ❖ Risk of loss that may arise on outstanding contracts should a counter party default on its obligations. ❖ The risk that a counter party to a transaction will fail to perform according to the terms and conditions of the contract, thus causing the holder of the claim to suffer a loss. ...
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...Credit Risk Management Ken Brown Peter Moles CR-A2-engb 1/2012 (1044) This course text is part of the learning content for this Edinburgh Business School course. In addition to this printed course text, you should also have access to the course website in this subject, which will provide you with more learning content, the Profiler software and past examination questions and answers. The content of this course text is updated from time to time, and all changes are reflected in the version of the text that appears on the accompanying website at http://coursewebsites.ebsglobal.net/. Most updates are minor, and examination questions will avoid any new or significantly altered material for two years following publication of the relevant material on the website. You can check the version of the course text via the version release number to be found on the front page of the text, and compare this to the version number of the latest PDF version of the text on the website. If you are studying this course as part of a tutored programme, you should contact your Centre for further information on any changes. Full terms and conditions that apply to students on any of the Edinburgh Business School courses are available on the website www.ebsglobal.net, and should have been notified to you either by Edinburgh Business School or by the centre or regional partner through whom you purchased your course. If this is not the case, please contact Edinburgh Business School at the address below:...
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...insufficient for institutions to efficiently lend to activities in the agricultural sector. Information on borrowers’ credit histories is rarely available, resulting in information asymmetries that make accurate credit risk assessment difficult. In addition, while agricultural client’s major assets are production and land, it is often difficult for banks to use these as collateral, and particularly difficult to foreclose on land in case of default. Compounding this lack of traditional collateral is the presence of a high degree of covariate risk, in particular market price risk and weather risk. Banks lending to agricultural clients know that agricultural and rural revenues easily drop below break-even levels due to extreme weather events and price falls, which result in defaults and higher loan loss provisions, thereby making lending to agribusiness unprofitable. The second major constraint in agricultural lending, high transaction and supervisory costs, is due to the particular risk, nature, and characteristics of the rural sector. In all financial markets, there is a trade-of between minimizing loan default and supervisory costs, but the nature of agricultural lending, especially through microfinance institutions, makes transaction costs and supervision costs disproportionately high relative to its urban counterpart. The small size of seasonal agricultural credit results in high due diligence costs per loan. The large geographical spread of customers, coupled with poor transportation...
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...ĮMONIŲ KREDITO RIZIKOS VERTINIMO MODELIŲ ANALIZĖ Neringa Semėnaitė, Solveiga Jagminaitė Vadovė: Lekt. Dr. Laura Ivaškevičiūtė Kauno technologijos universitetas, Socialinių mokslų fakultetas SM 5/2 gr. Įvadas Bankai skolindami neturi visos reikiamos informacijos apie įmones, todėl susiduria su vis didesne kredito rizika, o tai sąlygoja nuolatinį poreikį tobulinti savo rizikos valdymo sistemą, kurti naujus metodus, kurie padėtų efektyviai vystyti veiklą. Problema: parinkus netinkamus kredito rizikos vertinimo modelius ir metodus iškyla neadekvataus kredito rizikos vertinimo grėsmė, kuri yra viena iš šiandieninės finansinės krizės priežasčių. Tikslas – išanalizuoti įmonės kredito rizikos vertinimo modelius. Metodika: mokslinės literatūros apžvalga. Kredito rizikos samprata Norint išaiškinti kredito rizikos sąvokos reikšmę, pirmiausia reikia apibrėžti kas yra kreditas. Išanalizavus keleto autorių, t.y. Martinkaus B., Buškevičiūtės E., Bartkaus E., Žaltauskienės N., pateikiamas kredito sąvokas, galima teigti, kad kreditas – komercinis pasitikėjimas, kurį kreditorius (skolintojas) išreiškia kredito gavėjui (skolininkui) tiesiogiai (skolindamas pinigus) arba netiesiogiai parduodamas prekes ir paslaugas skolon. Anot S. Tarailos, paskolos išdavimas bendrais bruožais taip pat gali būti apibrėžiamas kaip vertės suteikimo kitam asmeniui arba subjektui procesas prisiimant su tuo susijusią riziką. Rizika susijusi su galimybe, kad kitas asmuo arba subjektas gali nesugebėti ateityje...
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...1.0 Introduction Business analytics (BA) is the practice of iterative, methodical exploration of an organization’s data with emphasis on statistical analysis. It describes the skills, technologies, practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. Business analytics is used by companies committed to data-driven decision making. It focuses on developing new insights and understanding of business performance based on data and statistical methods. BA is used to gain insights that inform business decisions and can be used to automate and optimize business processes. Business analytics makes extensive use of statistical analysis, including explanatory and predictive modeling, and fact-based management to drive decision making. It is therefore closely related to management science. Analytics may be used as input for human decisions or may drive fully automated decisions. Data-driven companies treat their data as a corporate asset and leverage it for competitive advantage. Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business and an organizational commitment to data-driven decision making. Once the business goal of the analysis is determined, an analysis methodology is selected and data is acquired to support the analysis. Data acquisition often involves extraction from one or more business systems, cleansing, and integration...
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