Towards a Theory of the Credit-Risk Balance Sheet

This article designs what it calls a Credit-Risk Balance Sheet (the risk being that of default by customers), a tool which, in principle, can contribute to revealing, controlling and managing the bad debt risk arising from a companys commercial credit, whose amount can represent a significant proportion of both its current and total assets. To construct it, we start from the duality observed in any credit transaction of this nature, whose basic identity can be summed up as Credit = Risk. Credit is granted by a company to its customer, and can be ranked by quality (we suggest the credit scoring system) and risk can either be assumed (interiorised) by the company itself or transferred to third parties (exteriorised). What provides the approach that leads to us being able to talk with confidence of a real Credit-Risk Balance Sheet with its methodological robustness is that the dual vision of the credit transaction is not, as we demonstrate, merely a classificatory duality (a double risk-credit classification of reality) but rather a true causal relationship, that is, a risk-credit causal duality. Once said Credit-Risk Balance Sheet (which bears a certain structural similarity with the classic net asset balance sheet) has been built, and its methodological coherence demonstrated, its properties static and dynamic are studied. Analysis of the temporal evolution of the Credit-Risk Balance Sheet and of its applications will be the object of subsequent works.credit-risk balance sheet, bad debts, risk, insolvency, commercial credit, credit, credit information, business risk, credit risk, credit management

A CREDIT SCORING MODEL FOR INSTITUTIONS OF MICROFINANCE UNDER THE BASEL II NORMATIVE

The growth of microcredit worldwide along with international rules on capital requirements (Basel II) are increasing the competition between microfinance institutions (MFIs) and banks for this business segment. The bank system traditionally has relied on adequate credit scoring models to analyze the risk of payment failures, but this has not been the case in supervised MFIs. The objective of this research is to design a credit scoring model for any institution subjected to supervision and specialized in microcredit as the Development Agency for Small and Micro Enterprise (Entidad de Desarrollo de la Pequeña y Micro Empresa - Edpyme) of the financial system in Peru. The results of this research includes a methodology and the steps needed to design the model, and the assessment and validation process that can be applied in the business area, in particular, to establish an interest rate policy with customers. Eventually, the paper also explains how the model can be used to develop credit risk management under the Basel II IRB approaches.Microcredit; institutions of microfinance; Basel II; credit scoring; Logit; IRB

Consequences of Information Asymmetry on Corporate Risk Management

This paper will demonstrate the impact information asymmetry has on risk management. There is a noticeable impact within the context of consumer credit risk. If a firm is able to recognize this, they can make improved credit decisions that will reduce the consequences. The theoretical impact will be presented while depicting areas of risk management that are susceptible to information asymmetry. We find a direct impact on the development of scoring models, credit policies, and origination volume. These results hold for banks with portfolios consisting of consumer credit products and small business loans. Once known, banks can better tailor their credit policies and underwriting guidelines to reduce the impact. This will provide the blueprints for empirical research into the fiscal consequences, particularly concerning loss provisioning and the charge-off of consumer loans

Credit scoring: comparison of non‐parametric techniques against logistic regression

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceOver the past decades, financial institutions have been giving increased importance to credit risk management as a critical tool to control their profitability. More than ever, it became crucial for these institutions to be able to well discriminate between good and bad clients for only accepting the credit applications that are not likely to default. To calculate the probability of default of a particular client, most financial institutions have credit scoring models based on parametric techniques. Logistic regression is the current industry standard technique in credit scoring models, and it is one of the techniques under study in this dissertation. Although it is regarded as a robust and intuitive technique, it is still not free from several critics towards the model assumptions it takes that can compromise its predictions. This dissertation intends to evaluate the gains in performance resulting from using more modern non-parametric techniques instead of logistic regression, performing a model comparison over four different real-life credit datasets. Specifically, the techniques compared against logistic regression in this study consist of two single classifiers (decision tree and SVM with RBF kernel) and two ensemble methods (random forest and stacking with cross-validation). The literature review demonstrates that heterogeneous ensemble approaches have a weaker presence in credit scoring studies and, because of that, stacking with cross-validation was considered in this study. The results demonstrate that logistic regression outperforms the decision tree classifier, has similar performance in relation to SVM and slightly underperforms both ensemble approaches in similar extents

A credit scoring model for institutions of microfinance under the Basil II normative

El crecimiento de los microcréditos a nivel mundial, junto con la normativa internacional sobre requerimientos de capital (Basilea II), están impulsando a las instituciones de microfinanzas (IMFs) a una mayor competencia con las entidades bancarias por este segmento de negocio. La banca tradicionalmente ha contado con adecuados modelos de credit scoring para analizar el riesgo de incumplimiento, pero esto no ha sido así en las IMFs supervisadas. El objetivo de esta investigación es diseñar un modelo de credit scoring para una institución sometida a supervisión y especializada en microcréditos, como es la Entidad de Desarrollo de la Pequeña y Micro Empresa (Edpyme) del sistema financiero del Perú. El resultado de la investigación muestra la metodología y fases necesarias para diseñar el modelo, así como el proceso de valoración y validación para que pueda ser aplicado en el área de negocio, especialmente para establecer la política de tasas de interés con clientes. Por último, también se muestra cómo puede utilizarse el modelo para desarrollar una gestión del riesgo de crédito en el marco de los métodos IRB de Basilea II.------------------------------------------------------------The growth of microcredit worldwide along with international rules on capital requirements (Basel II) are increasing the competition between microfinance institutions (MFIs) and banks for this business segment. The bank system traditionally has relied on adequate credit scoring models to analyze the risk of payment failures, but this has not been the case in supervised MFIs. The objective of this research is to design a credit scoring model for any institution subjected to supervision and specialized in microcredit as the Development Agency for Small and Micro Enterprise (Entidad de Desarrollo de la Pequeña y Micro Empresa - Edpyme) of the financial system in Peru. The results of this research includes a methodology and the steps needed to design the model, and the assessment and validation process that can be applied in the business area, in particular, to establish an interest rate policy with customers. Eventually, the paper also explains how the model can be used to develop credit risk management under the Basel II IRB approaches.Publicad

Credit risk analysis using artificial intelligence : evidence from a leading South African banking institution

Credit risk analysis is an important topic in financial risk management. Financial institutions (e.g. commercial banks) that grant consumers credit need reliable models that can accurately detect and predict defaults. This research investigates the ability of artificial neural networks as a decision support system that can automatically detect and predict “bad” credit risks based on customers demographic, biographic and behavioural characteristics. The study focuses specifically on the learning vector quantization neural network algorithm. This thesis contains a short overview of credit scoring models, an introduction to artificial neural networks and their applications and presents the performance evaluation results of a credit risk detection model based on learning vector quantization networks.Graduate School of Business LeadershipM.B.L

Credit Risk Management in Finance : a Review of Various Approaches

Classification of customers of banks and financial institutions is an important task in today's business world. Reducing the number of loans granted to companies of questionable credibility can positively influence banks' performance. The appropriate measurement of potential bankruptcy or probability of default is another step in credit risk management. Among the most commonly used methods, we can enumerate discriminant analysis models, scoring methods, decision trees, logit and probit regression, neural networks, probability of default models, standard models, reduced models, etc. This paper investigates the use of various methods used in the initial step of credit risk management and corresponding decision process. Their potential advantages and drawbacks from the point of view of the principles for the management of credit risk are presented. A comparison of their usability and accuracy is also made. (original abstract

Zero Textbook Cost Syllabus for FIN 4093 (Corporate Credit Risk)

The course will provide students with an overview of key concepts in corporate credit risk through the lens of a commercial banking risk analyst. Students will be assigned a company to follow throughout the semester and will be required to use the tools of the course to build their own credit rating analysis in a term paper due at the end of the semester. Topics including country risk, industry risk, market risk, business risk (financial and management), and structure risk will be explored through lectures, industry publications, and access to industry analysis and tools. Upon completion of this course, students will have an understanding of the main components of corporate credit risk scoring, industry terminology, and the capability to develop their own credit rating view on a company

Prédiction du risque de crédit : étude comparative des techniques de Scoring

Good control and management of credit risk has become the main concern of financial institutions, which are constantly developing models for analyzing, assessing and predicting this risk, particularly with the prudential standards required by central banks. Credit risk assessment and prediction methods are represented in the form of scoring models which aim to predict the potential vulnerability of a business using financial information and computable. The objective of our work is to study the different techniques of credit scoring, their interest as a powerful tool allowing to predict the solvency of the borrowers.Une bonne maîtrise et gestion du risque de crédit est devenue la principale préoccupation des établissements financiers qui ne cessent de développer des modèles d’analyse, d’évaluation et de prédiction de ce risque, notamment avec les normes prudentielles exigées par les banques centrales. Les méthodes d’évaluation et de prédiction du risque de crédit sont représentées sous la forme de modèles de scoring qui ont pour but la prédiction de la défaillance d’une entreprise grâce à des informations financières et comptables. L’objectif de notre travail est d'analyserles différentes techniques de crédit scoring en tant qu’un outil puissant permettant de prévoir la solvabilité des emprunteurs