352 research outputs found
Decision Support Systems for Risk Assessment in Credit Operations Against Collateral
With the global economic crisis, which reached its peak in the second half of 2008, and
before a market shaken by economic instability, financial institutions have taken steps to protect
the banks’ default risks, which had an impact directly in the form of analysis in credit institutions
to individuals and to corporate entities. To mitigate the risk of banks in credit operations, most
banks use a graded scale of customer risk, which determines the provision that banks must
do according to the default risk levels in each credit transaction. The credit analysis involves
the ability to make a credit decision inside a scenario of uncertainty and constant changes and
incomplete transformations. This ability depends on the capacity to logically analyze situations,
often complex and reach a clear conclusion, practical and practicable to implement.
Credit Scoring models are used to predict the probability of a customer proposing to
credit to become in default at any given time, based on his personal and financial information
that may influence the ability of the client to pay the debt. This estimated probability, called the
score, is an estimate of the risk of default of a customer in a given period. This increased concern
has been in no small part caused by the weaknesses of existing risk management techniques
that have been revealed by the recent financial crisis and the growing demand for consumer
credit.The constant change affects several banking sections because it prevents the ability to
investigate the data that is produced and stored in computers that are too often dependent on
manual techniques.
Among the many alternatives used in the world to balance this risk, the provision of
guarantees stands out of guarantees in the formalization of credit agreements. In theory, the
collateral does not ensure the credit return, as it is not computed as payment of the obligation
within the project. There is also the fact that it will only be successful if triggered, which involves
the legal area of the banking institution. The truth is, collateral is a mitigating element
of credit risk. Collaterals are divided into two types, an individual guarantee (sponsor) and the
asset guarantee (fiduciary). Both aim to increase security in credit operations, as an payment
alternative to the holder of credit provided to the lender, if possible, unable to meet its obligations
on time. For the creditor, it generates liquidity security from the receiving operation. The
measurement of credit recoverability is a system that evaluates the efficiency of the collateral
invested return mechanism.
In an attempt to identify the sufficiency of collateral in credit operations, this thesis
presents an assessment of smart classifiers that uses contextual information to assess whether
collaterals provide for the recovery of credit granted in the decision-making process before
the credit transaction become insolvent. The results observed when compared with other approaches
in the literature and the comparative analysis of the most relevant artificial intelligence
solutions, considering the classifiers that use guarantees as a parameter to calculate the
risk contribute to the advance of the state of the art advance, increasing the commitment to
the financial institutions.Com a crise econômica global, que atingiu seu auge no segundo semestre de 2008, e diante
de um mercado abalado pela instabilidade econômica, as instituições financeiras tomaram
medidas para proteger os riscos de inadimplência dos bancos, medidas que impactavam diretamente
na forma de análise nas instituições de crédito para pessoas físicas e jurídicas. Para
mitigar o risco dos bancos nas operações de crédito, a maioria destas instituições utiliza uma
escala graduada de risco do cliente, que determina a provisão que os bancos devem fazer de
acordo com os níveis de risco padrão em cada transação de crédito. A análise de crédito envolve
a capacidade de tomar uma decisão de crédito dentro de um cenário de incerteza e mudanças
constantes e transformações incompletas. Essa aptidão depende da capacidade de analisar situações
lógicas, geralmente complexas e de chegar a uma conclusão clara, prática e praticável
de implementar.
Os modelos de Credit Score são usados para prever a probabilidade de um cliente
propor crédito e tornar-se inadimplente a qualquer momento, com base em suas informações
pessoais e financeiras que podem influenciar a capacidade do cliente de pagar a dívida. Essa
probabilidade estimada, denominada pontuação, é uma estimativa do risco de inadimplência de
um cliente em um determinado período. A mudança constante afeta várias seções bancárias,
pois impede a capacidade de investigar os dados que são produzidos e armazenados em computadores
que frequentemente dependem de técnicas manuais.
Entre as inúmeras alternativas utilizadas no mundo para equilibrar esse risco, destacase
o aporte de garantias na formalização dos contratos de crédito. Em tese, a garantia não
“garante” o retorno do crédito, já que não é computada como pagamento da obrigação dentro do
projeto. Tem-se ainda, o fato de que esta só terá algum êxito se acionada, o que envolve a área
jurídica da instituição bancária. A verdade é que, a garantia é um elemento mitigador do risco
de crédito. As garantias são divididas em dois tipos, uma garantia individual (patrocinadora) e
a garantia do ativo (fiduciário). Ambos visam aumentar a segurança nas operações de crédito,
como uma alternativa de pagamento ao titular do crédito fornecido ao credor, se possível, não
puder cumprir suas obrigações no prazo. Para o credor, gera segurança de liquidez a partir da
operação de recebimento. A mensuração da recuperabilidade do crédito é uma sistemática que
avalia a eficiência do mecanismo de retorno do capital investido em garantias.
Para tentar identificar a suficiência das garantias nas operações de crédito, esta tese
apresenta uma avaliação dos classificadores inteligentes que utiliza informações contextuais
para avaliar se as garantias permitem prever a recuperação de crédito concedido no processo de
tomada de decisão antes que a operação de crédito entre em default. Os resultados observados
quando comparados com outras abordagens existentes na literatura e a análise comparativa das
soluções de inteligência artificial mais relevantes, mostram que os classificadores que usam
garantias como parâmetro para calcular o risco contribuem para o avanço do estado da arte,
aumentando o comprometimento com as instituições financeiras
Internal Model of Commercial Bank as an Instrument for Measuring Credit Risk of the Borrower in Relation to Financial Performance (Credit Scoring and Bankruptcy Models)
Commercial banks generally use different methods and procedures for managing credit risk. The internal rating method in which the client has an important position in the process of granting credit provides a comprehensive assessment of client creditworthiness. The aim of this article is to analyze selected theoretical, methodological and practical aspects of internal rating models of commercial banks within the context of models that measures financial performance and to make a comparison of results of real - rating models which are used in the Czech Republic and Slovakia. The results of the chosen credit scoring and bankruptcy methods on selected companies from segments of small and medium-sized companies are presented
An academic review: applications of data mining techniques in finance industry
With the development of Internet techniques, data volumes are doubling every two years, faster than predicted by Moore’s Law. Big Data Analytics becomes particularly important for enterprise business. Modern computational technologies will provide effective tools to help understand hugely accumulated data and leverage this information to get insights into the finance industry. In order to get actionable insights into the business, data has become most valuable asset of financial organisations, as there are no physical products in finance industry to manufacture. This is where data mining techniques come to their rescue by allowing access to the right information at the right time. These techniques are used by the finance industry in various areas such as fraud detection, intelligent forecasting, credit rating, loan management, customer profiling, money laundering, marketing and prediction of price movements to name a few. This work aims to survey the research on data mining techniques applied to the finance industry from 2010 to 2015.The review finds that Stock prediction and Credit rating have received most attention of researchers, compared to Loan prediction, Money Laundering and Time Series prediction. Due to the dynamics, uncertainty and variety of data, nonlinear mapping techniques have been deeply studied than linear techniques. Also it has been proved that hybrid methods are more accurate in prediction, closely followed by Neural Network technique. This survey could provide a clue of applications of data mining techniques for finance industry, and a summary of methodologies for researchers in this area. Especially, it could provide a good vision of Data Mining Techniques in computational finance for beginners who want to work in the field of computational finance
How Secure Are Good Loans: Validating Loan-Granting Decisions And Predicting Default Rates On Consumer Loans
The failure or success of the banking industry depends largely on the industrys ability to properly evaluate credit risk. In the consumer-lending context, the banks goal is to maximize income by issuing as many good loans to consumers as possible while avoiding losses associated with bad loans. Mistakes could severely affect profits because the losses associated with one bad loan may undermine the income earned on many good loans. Therefore banks carefully evaluate the financial status of each customer as well as their credit worthiness and weigh them against the banks internal loan-granting policies. Recognizing that even a small improvement in credit scoring accuracy translates into significant future savings, the banking industry and the scientific community have been employing various machine learning and traditional statistical techniques to improve credit risk prediction accuracy.This paper examines historical data from consumer loans issued by a financial institution to individuals that the financial institution deemed to be qualified customers. The data consists of the financial attributes of each customer and includes a mixture of loans that the customers paid off and defaulted upon. The paper uses three different data mining techniques (decision trees, neural networks, logit regression) and the ensemble model, which combines the three techniques, to predict whether a particular customer defaulted or paid off his/her loan. The paper then compares the effectiveness of each technique and analyzes the risk of default inherent in each loan and group of loans. The data mining classification techniques and analysis can enable banks to more precisely classify consumers into various credit risk groups. Knowing what risk group a consumer falls into would allow a bank to fine tune its lending policies by recognizing high risk groups of consumers to whom loans should not be issued, and identifying safer loans that should be issued, on terms commensurate with the risk of default
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The Classification Performance of Multiple Methods and Datasets: Cases from the Loan Credit Scoring Domain
Decisions to extend credit to potential customers are complex, risky and even potentially catastrophic for the credit granting institution and the broader economy as underscored by credit failures in the late 2000s. Thus, the ability to accurately assess the likelihood of default is an important issue. In this paper the authors contrast the classification accuracy of multiple computational intelligence methods using five datasets obtained from five different decision contexts in the real world. The methods considered are: logistic regression (LR), neural network (NN), radial basis function neural network (RBFNN), support vector machine (SVM), k-nearest neighbor (kNN), and decision tree (DT). The datasets have various characteristics with respect to the number of cases, the number and type of attributes, the extent of missing values as well as different ratios for bad loans/good loans. Using areas under ROC charts as well as the classification accuracy rates for overall, bad loans, and good loans the performances of six methods across five datasets and the five datasets across the methods are examined to find if there are significant differences between the methods and datasets. Our results reveal some interesting findings which may be useful to practitioners. Even though no method consistently outperformed any other method using the above metrics on all datasets, this study provides some guidelines as to the most appropriate methods suitable for each specific data set. In addition, the study finds that customer financial attributes are much more relevant than the personal, social, or employment attributes for predictive accuracy
An exploratory study of behavioural finance insights in the Small, Medium and Micro-Enterprise creditworthiness assessment process
Financial institutions are often reluctant to lend to smaller entrepreneurs due to perceived information asymmetry and lack of available collateral. At the nascent and new entrepreneurial levels, it is generally more difficult for loan applicants to provide the information required to secure the necessary funds. Inadequate financial information coupled with uninformative credit histories heighten the information opacity thus diminishing the entrepreneur's prospects of securing loan funding. Viable entrepreneurial projects may therefore remain unfunded largely due to uncertainty rather than riskiness. This study therefore highlights the creditworthiness assessment process and seeks to address the information opacity problem by looking to alternative sources of entrepreneurial information that may aid the loan officer
Risks Management Application in Helping the Poor Through Microfinancing
Poverty alleviation in Buea, Cameroon, has been a problem of concern for decades. The study is vital because managers who control the funds given to the government of Cameroon to help reduce poverty are politicians and do not equitably distribute the funds to all on the pretext that the default rate is high. The purpose of this study was to find better ways to make additional capital available to the microbusiness owners of Buea to open or improve businesses. This qualitative case study design was consistent with the aim of understanding the importance of risk management within the microfinance industry and the risks involved in getting loans and paying them back. The key research question concerned how the microbusiness owners of Buea can obtain additional capital to open new businesses or improve existing businesses. The conceptual framework for this study was Rostow\u27s theory of modernization. Twenty purposively sampled loan officers, bank managers, government officials, and microbusiness owners in Buea were interviewed. Six participants from the population also participated in a focus group. Study findings suggest it is possible for microbusiness owners in Buea to get microloans and start or improve businesses with the use of land titles as collateral or family members as cosigners. The government of Cameroon could improve the financial stability of microbusinesses by facilitating the issuance of land titles or certificates, which are acceptable forms of collateral. This study may contribute to positive social change by improving the financial stability of microbusinesses in Cameroon, and possibly in other socially similar countries
Towards demand-driven financial services in Northern Vietnam: a participatory analysis of customer preferences
Analyzing secondary and primary data, this paper suggests a shift in national development policies from solely promoting rural credit to supporting savings activities. The household data are econometrically analyzed applying the Conjoint Analysis (CA). The CA gave valuable insights into how to improve outreach of formal financial institutes (FFIs) by adapting the credit products to client preferences and revealed an unattended demand for savings instruments. Due the enormous credit outreach of the FFIs in Vietnam, it would be more efficient to launch a credit consolidation policy and to implement a reliable and sustainable deposit collection system at the village level. However, in national policymaking a paradigm change must take place and the capability of rural households to save needs to be recognized by policy-makers
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