No description available.

Thesis (M.B.A.)--Boston University

Thesis (Ph. D.)--Michigan State University. Dept. of Accounting and Information Systems, 2008. / Title from PDF t.p. (viewed on Mar. 26, 2009). Includes bibliographical references (p. 62-71). Also issued in print.

M.Comm. (Financial Management) / If the creditworthiness of applicants for overdraft facilities is assessed inaccurately, future defaults on repayment commitments may threaten the cash flow and profitability of the bank issuing the credit. The main purpose of this study is to assess how reliable the inputs used by one large South African bank to determine the creditworthiness of individual clients applying for overdraft facilities on personal current accounts are in predicting the future behaviour of the client. The findings of this study should inform the bank in question (and other credit providers) of the extent to which the original application-based score (OABS), is an accurate predictor of a client's future behaviour after the account is opened. It will also help them to identify which of those variables are the best predictors, and so reduce the risk of default. A quantitative research methodology is employed, using secondary data on individual applications processed at one large South African banking institution between July 2006 and July 2009. The data was used to establish whether any (and which) of the input variables captured by the bank when the client originally applies for credit, are strongly associated with the behavioural risk indicator (BRI) assigned to them after the first six months (BRI 1) and twelve months (BRI 2) after being granted a loan, respectively. The findings of this study revealed that non-financial characteristics (biographical and demographic information) are not considered in the OABS; while finance-related characteristics (segment, income bands, overdraft taken up category and overdraft taken up as a percentage of gross income categories) are. The study found that there is also an association between the OABS and the behavioural scores (BRI 1 and BRI 2) allocated thereafter. In particular, the number of days the client went into excess during the initial six and subsequent six months is strongly associated with the BRI 1 and BRI 2 scores. This finding implies that, despite the utilisation of non-financial measures to determine creditworthiness scores, a client’s current behaviour is still the best predictor of future behaviour. The study concluded that, despite its low predictive power, the OABS is, however, associated with, and to a certain extent predicts, the future behaviour of clients.

In order to evaluate the creditworthiness of the company not only outstanding financial and economic knowledge will be needed but also inter-personal skills, common sense and good judgment of human nature. As the issue of finding employees with all the above mentioned qualification will occur for commercial banks the main proposition will be either hiring an expert in psychology or providing systematic psychological training for the credit experts. The idea is not currently used in the vast majority of banks and the appropriate training would be of a high value for credit specialists for their self-development. The quality of the credit specialists will trigger higher standards of credit granting. All these efforts will be become a milestone for a more reliable banking system and trustworthy financial transactions.

Submitted in fulfilment of the requirements of the Masters of Technology degree in Cost and Management Accounting, Durban University of Technology, Durban, South Africa, 2015. / The National Credit Act No. 34 of 2005 (NCA) was introduced by the South African government mainly to bring about accessibility to credit markets, protect consumers from malpractices and market abuses by credit providers and reduce consumer over indebtedness. As a result, credit providers are compelled to apply stringent rules and regulations when assessing a credit consumer’s affordability prior to granting home loans. This study aims at investigating the impact of the NCA on the affordability of home loans within the Pinetown metropolitan area. The literature review presents an overall view of affordability of home loans in developed and emerging countries and also provides an in-depth explanation of factors that affect affordability of home loans in South Africa. The predominant factors, amongst others that hinder the housing market, are: an increase in house prices; elevated interest rates and household debt which include inflation, transportation cost and low wage increase. A mixed methods approach was utilized for the research, analyzing both quantitative and qualitative data. Respondents (home loan borrowers) completed a questionnaire by indicating if assessments were conducted in terms of their affordability prior to the approval of their home loan and their view on the impact that the NCA had on their home loan. The data suggested strongly that credit providers had conducted proper credit and affordability assessments prior to granting home loans and that most home loan borrowers’ level of debt had remained stable since acquiring their home loans as they continue to meet their debts. An overall analysis revealed that the application of the NCA had a positive impact on the affordability of home loans in the research area and that compliance with NCA, when granting credit, resulted in the reduction of reckless lending, a decline in the level of consumer indebtedness and a reduction in payment default.

A Research Report Submitted to the Faculty of Engineering and the Built Environment in partial fulfilment of the Requirements for the degree of Master of Science in Engineering / The research centred on improving the current software systems development life cycle (SDLC) of the credit scoring process at a financial institution based on systems engineering principles. The research sought ways to improve the current software SDLC in terms of cost, schedule and performance. This paper proposes an improved software SDLC that conforms to the principles of systems engineering. As decisioning has been automated in financial institutions, various processes are developed according to a software SDLC in order to ensure accuracy and validity thereof. This research can be applied to various processes within financial institutions where software development is conducted, verified and tested. A comparative analysis between the current software SDLC and a recommended SDLC was performed. Areas within the current SDLC that did not comply with systems engineering principles were identified. These inefficiencies were found during unit testing, functional testing and regression testing. An SDLC is proposed that conforms to systems engineering principles and is expected to reduce the current SDLC schedule by 20 per cent. Proposed changes include the sequence of processes within the SDLC, increasing test coverage by extracting data from the production environment, filtering and sampling data from the production environment, automating functional testing using mathematical algorithms, and creating a test pack for regression testing which adequately covers the software change. / MT2016

A presente dissertação trata da estipulação de limite de crédito para empresas clientes, de modo automático, com o uso de técnicas de Inteligência Computacional, especificamente redes neurais artificiais (RNA). Na análise de crédito as duas situações mais críticas são a liberação do crédito, de acordo com o perfil do cliente, e a manutenção deste limite ao longo do tempo de acordo com o histórico do cliente. O objeto desta dissertação visa a automação da estipulação do limite de crédito, implementando uma RNA que possa aprender com situações já ocorridas com outros clientes de perfil parecido e que seja capaz de tomar decisões baseando-se na política de crédito apreendida com um Analista de Crédito. O objetivo é tornar o sistema de crédito mais seguro para o credor, pois uma análise correta de crédito de um cliente reduz consideravelmente os índices de inadimplência e mantém as vendas num patamar ótimo. Para essa análise, utilizouse a linguagem de programação VB.Net para o sistema de cadastro e se utilizou do MatLab para treinamento das RNAs. A dissertação apresenta um estudo de caso, onde mostra a forma de aplicação deste software para a análise de crédito. Os resultados obtidos aplicando-se as técnicas de RNAs foram satisfatórias indicando um caminho eficiente para a determinação do limite de crédito. / This research deals with the credit limit stipulation for corporate clients, automatically, with the use of Computational Intelligence techniques, specifically artificial neural networks (ANN). In the analysis of credit, the two most critical situations are release of credit, according to the customer profile, and maintain the credit according to the customer history. The object of this work aims at automating the stipulated credit limit at the time of initial registration of the customer. The main focus of this work is to make an ANN can provide the credit limit, learning from situations that have occurred with other clients of similar profile and is able to make decisions based on the credit policy seized with a Credit Analyst. The goal is to make the system more secure credit to the lender, for a correct analysis of the creditworthiness of a customer drops considerably default rates and maintains a sales plateau great. For this analysis, we used the VB.Net programming language for the registration system of MatLab and was used for training ANNs. The paper presents a case study, which shows how to apply this software to credit analysis. The results obtained applying the techniques ANNs were satisfactory showing an efficient way to determine the credit limit.

Thesis (M.S.)--State University of New York at Binghamton, Systems Science and Industrial Engineering Department, 2006. / Includes bibliographical references.

A presente tese tem como objetivo propor um modelo de previsão para estimar o lucro médio esperado na concessão de crédito para pessoas físicas em empresas comerciais, obtendo assim uma medida monetária para dar suporte à tomada de decisão. O modelo proposto foi desenvolvido em três grandes etapas: 1) pré-processamento; 2) modelos de classificação; e 3) modelo de previsão do risco monetário. A primeira etapa inclui três passos: (i) delimitação da população, (ii) seleção da amostra, e (iii) análise preliminar. Na segunda etapa mais dois passos são necessários: (i) construção dos modelos, e (ii) qualidade dos modelos. Por fim, a última etapa trata das definições para construção do modelo de previsão do risco monetário propriamente dito, que utilizou os seguintes métodos: (i) ensemble, (ii) hybrid, e (iii) regressão linear múltipla. A exequibilidade do modelo proposto foi testada em dados reais de concessão de crédito. São avaliados os resultados de utilização do modelo de previsão, de forma a verificar o potencial aumento nos ganhos a partir da concessão do crédito, comparando quatro cenários: (i) sem utilizar nenhum modelo de previsão de risco de crédito; (ii) utilizando o modelo de classificação obtido com a regressão logística; (iii) utilizando o modelo de classificação obtido com a rede neural; e (iv) utilizando o modelo proposto para previsão do risco monetário. O modelo construído demonstrou resultados promissores na previsão do lucro médio esperado, apresentando um aumento estimado de 94,97% em comparação com o cenário sem uso de modelo de previsão, e um aumento de 26,08% quando comparado com o cenário de uso do modelo de classificação obtido com regressão logística. Uma análise de sensibilidade dos resultados com variações na margem de lucro por transação também foi realizada, evidenciando sua robustez. Nesse sentido, o modelo proposto se mostra efetivo como ferramenta de apoio para gestão no processo de decisão de concessão de crédito. / This thesis aims to propose a forecasting model to estimate the expected average profit in lending to individuals in commercial companies, thus obtaining a monetary measure to support decision making. The proposed model was developed in three major stages: 1) preprocessing, 2) classification models, and 3) model to forecast the currency risk. The first stage includes three steps: (i) delimitation of the population, (ii) sample selection, and (iii) preliminary analysis. In the second stage two more steps are necessary: (i) construction of models, and (ii) quality of the models. Finally, the last stage is regarding to the definitions for the construction of model prediction of the currency risk itself, which used the following methods: (i) ensemble, (ii) hybrid, and (iii) multiple linear regressions. The feasibility of the proposed model was tested on real data of grant credit. Results are evaluated using the prediction model in order to verify the potential increase in profits from the grant credit, comparing four scenarios: (i) without using any prevision model of credit risk, (ii) using the classification model obtained by logistic regression, (iii) using the classification model obtained with the neural network, and (iv) using the model to forecast the currency risk. The constructed model showed promising results in predicting the expected average profits, with an estimated increase of 94.97% compared to the scenario without the use of forecasting model, and an increase of 26.08% compared with the scenario of the classification model obtained by logistic regression. A sensitivity analysis of the results with variations in the profit margin per transaction was also performed, demonstrating its robustness. Accordingly, the proposed model proved effective as a support tool for management in the decision to grant credit.