Estimating credit and profit scoring of a Brazilian credit union with logistic regression and machine-learning techniques

Purpose – Although credit unions are nonprofit organizations, their objectives depend on the efficient management of their resources and credit risk aligned with the principles of the cooperative doctrine. This paper aims to propose the combined use of credit scoring and profit scoring to increase the effectiveness of the loan-granting process in credit unions. Design/methodology/approach – This sample is composed by the data of personal loans transactions of a Brazilian credit union. Findings – The analysis reveals that the use of statistical methods improves significantly the predictability of default when compared to the use of subjective techniques and the superiority of the random forests model in estimating credit scoring and profit scoring when compared to logit and ordinary least squares method (OLS) regression. The study also illustrates how both analyses can be used jointly for more effective decision-making. Originality/value – Replacing subjective analysis with objective credit analysis using deterministic models will benefit Brazilian credit unions. The credit decision will be based on the input variables and on clear criteria, turning the decision-making process impartial. The joint use of credit scoring and profit scoring allows granting credit for the clients with the highest potential to pay debt obligation and, at the same time, to certify that the transaction profitability meets the goals of the organization: to be sustainable and to provide loans and investment opportunities at attractive rates to members

Consumer finance: challenges for operational research

Consumer finance has become one of the most important areas of banking, both because of the amount of money being lent and the impact of such credit on global economy and the realisation that the credit crunch of 2008 was partly due to incorrect modelling of the risks in such lending. This paper reviews the development of credit scoring—the way of assessing risk in consumer finance—and what is meant by a credit score. It then outlines 10 challenges for Operational Research to support modelling in consumer finance. Some of these involve developing more robust risk assessment systems, whereas others are to expand the use of such modelling to deal with the current objectives of lenders and the new decisions they have to make in consumer finance. <br/

Operations research in consumer finance: challenges for operational research

Consumer finance has become one of the most important areas of banking both because of the amount of money being lent and the impact of such credit on the global economy and the realisation that the credit crunch of 2008 was partly due to incorrect modelling of the risks in such lending. This paper reviews the development of credit scoring,-the way of assessing risk in consumer finance- and what is meant by a credit score. It then outlines ten challenges for Operational Research to support modelling in consumer finance. Some of these are to developing more robust risk assessment systems while others are to expand the use of such modelling to deal with the current objectives of lenders and the new decisions they have to make in consumer financ

AGRICULTURAL LOAN REVIEW AND RISK RATING FOR COMMUNITY BANKS

Agricultural Finance,

Credit bureaus between risk-management, creditworthiness assessment and prudential supervision

"This text may be downloaded for personal research purposes only. Any additional reproduction for other purposes, whether in hard copy or electronically, requires the consent of the author. If cited or quoted, reference should be made to the full name of the author, the title, the working paper or other series, the year, and the publisher."This paper discusses the role and operations of consumer Credit Bureaus in the European Union in the context of the economic theories, policies and law within which they work. Across Europe there is no common practice of sharing the credit data of consumers which can be used for several purposes. Mostly, they are used by the lending industry as a practice of creditworthiness assessment or as a risk-management tool to underwrite borrowing decisions or price risk. However, the type, breath, and depth of information differ greatly from country to country. In some Member States, consumer data are part of a broader information centralisation system for the prudential supervision of banks and the financial system as a whole. Despite EU rules on credit to consumers for the creation of the internal market, the underlying consumer data infrastructure remains fragmented at national level, failing to achieve univocal, common, or defined policy objectives under a harmonised legal framework. Likewise, the establishment of the Banking Union and the prudential supervision of the Euro area demand standardisation and convergence of the data used to measure debt levels, arrears, and delinquencies. The many functions and usages of credit data suggest that the policy goals to be achieved should inform the legal and institutional framework of Credit Bureaus, as well as the design and use of the databases. This is also because fundamental rights and consumer protection concerns arise from the sharing of credit data and their expanding use

Determining the Probability of Default of Agricultural Loans in a French Bank

Recently, financial institutions have developed improved internal risk rating systems and emphasized the probability of default and loss given default. Also they have been affected by globalization and it became important to understand the way foreign banks operate. The probability of default is studied for 756 loans from a French bank: CIC- Banque SNVB. A binomial logit regression is used to estimate a model of the probability of default of an agribusiness loan. The results show that leverage, profitability and liquidity at loan origination are good indicators of the probability of default. The loan length is another good indicator of the probability of default. Also it is more accurate to develop a model for each type of collateral (activity).Agricultural Finance,

A Panel Data Analysis of the Repayment Capacity of Farmers

Using a balanced panel of 264 unique Illinois farmers from 2000 to 2004, this study identifies the most pertinent factors that explain the repayment capacity of farmers. After correcting for endogeneity bias caused by farmer-specific effects, one year lagged debt-to-asset ratio and soil productivity are both found to be significantly correlated with the coverage ratio at the 5% significance level using random effects. The finding is significant because it can enhance agricultural lenders ability to assess creditworthiness, screen borrowers, manage loan loss reserves, and price loans, thereby decreasing lenders costs associated with defaulted loans and ultimately reducing the costs borne by the government and taxpayers.panel data, random effects, coverage ratio, financial efficiency, solvency, liquidity, repayment capacity, profitability, creditworthiness, Agricultural Finance,

Credit Scoring Models in Illinois by Farm Type: Hog, Dairy, Beef and Grain

Employing a logit model and farm-level data for Illinois from 1995 to 2004, this study explores the importance of farm-type differences in the development of credit scoring models. Apart from the conclusion that regional credit scoring models specific to each farm type are needed, the following are identified as the most pertinent factors for explaining creditworthiness: previous years working capital to gross farm return, the debt-to-asset ratio, and return on farm assets. Furthermore, beef farms have a larger marginal effect compared to grain farms on the probability of the farmer being highly creditworthy. Hog farms differ from grain farms in how the following financial characteristics affect farmer creditworthiness: solvency, profitability, and financial efficiency. These separate credit scoring models result in increased expected profit for the lender, better capital management, less bankruptcy, and less burden on the government and tax payers.creditworthiness, credit scoring, cut-off point, farm type, FBFM, Agricultural Finance,

Factors affecting the level of farm indebtedness: the role of farming attitudes

Working paperUsing a nationally representative survey of farm operators in Ireland, this paper aims to provide a framework for better understanding the characteristics that influence the degree of indebtedness on farm businesses. This paper derives explanatory variables (based on a factor analysis of respondents mean ratings of 13 multiple value items) representing 3 different farming attitudes. An ordered logit model is then formulated to examine the effect of farming attitudes as well as personal characteristics and farm structural variables on the degree of indebtedness. Personal characteristics of the farmer such as age and education as well as farm structural variables such as farm size and farm system were all found to have a statistically significant impact. The presence of decoupled farm payments was also found to affect the degree of indebtedness. The study identified two distinct farming attitudes which were found to have important but opposite effects. These were attitudes strongly orientated to business related objectives which was positively associated with having farming debts and secondly positive attitudes relating to the benefits of farm relative to non-farm work which was negatively associated with the degree of indebtedness. Past research has focused on the effect of socio-demographic characteristics and farm structural variables in examining differences in farm indebtedness. This study extends this literature by specifically examining the role of farming attitudes. Obtaining a deeper understanding of the factors that affect the level of farming debt will be important as the degree of indebtedness has been found to affect farmers’ management decisions. Furthermore, outside of explaining farm credit use, farming attitudes and motivations may have an important impact on farmers’ behaviour in relation to a variety of farm activities