A risk mitigation tool for merchant selection

Organisations or individuals that lend money (banks and micro lenders) or that sell goods on credit (retailers) are classified as credit providers. The debtor enters into a contractual agreement with a credit provider, or creditor, with the obligation to repay the loan amount, fees and interest according to a predetermined schedule. The contractual agreement, also known as a credit agreement, is as a general rule very complex. Legislation protecting debtors in various ways is an international phenomenon. In South Africa, the National Credit Act, Act 34 of 2005 (NCA) was enacted in 2005. The NCA changed the playing field for credit providers participating in the South African consumer credit market to a great extent. Consumer lending is the sleeping giant of the financial sector. The key to successfully unlock this enormous market is the credit provider's ability to accurately assess the creditworthiness of a potential customer during the customer acquisition phase. The creditworthiness of the customer is related to the risk of default, i.e. a debtor's non-payment of debt in terms of the credit agreement. The risk of default is also known as credit risk. Real People Investment Holdings (Pty) Ltd (RPIH) classifies credit risk as the single largest risk the Group is exposed to. They recognise that the intelligent and responsible management of credit risk makes it the Group's largest profit driver. Credit risk scorecards are excellent decision aids during the customer acquisition phase. The characteristics and behaviour of merchants submitting credit applications to RPIH for assessment have a definite impact on the credit risk of the Group. The merchant plays a pivotal role in the debtor-creditor-supplier business model. The merchant influences the customer's sales experience and subsequent level of satisfaction with the transaction. A satisfied customer constitutes a lower level of credit risk for the creditor, in this case RPIH. The research is conducted with a positivistic paradigm. The cross-sectional study approach is used. The merchant is the unit of analysis. A sample of 77 merchants is selected from the population of 244 merchants who submitted credit applications to RPIH during the observation period. Questionnaires are used as the data collection method in this research project. The predictive ability of fourteen merchant related characteristics are demonstrated through this empirical study

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

Forecasting creditworthiness in retail banking: a comparison of cascade correlation neural networks, CART and logistic regression scoring models

The preoccupation with modelling credit scoring systems including their relevance to forecasting and decision making in the financial sector has been with developed countries whilst developing countries have been largely neglected. The focus of our investigation is the Cameroonian commercial banking sector with implications for fellow members of the Banque des Etats de L’Afrique Centrale (BEAC) family which apply the same system. We investigate their currently used approaches to assessing personal loans and we construct appropriate scoring models. Three statistical modelling scoring techniques are applied, namely Logistic Regression (LR), Classification and Regression Tree (CART) and Cascade Correlation Neural Network (CCNN). To compare various scoring models’ performances we use Average Correct Classification (ACC) rates, error rates, ROC curve and GINI coefficient as evaluation criteria. The results demonstrate that a reduction in terms of forecasting power from 15.69% default cases under the current system, to 3.34% based on the best scoring model, namely CART can be achieved. The predictive capabilities of all three models are rated as at least very good using GINI coefficient; and rated excellent using the ROC curve for both CART and CCNN. It should be emphasised that in terms of prediction rate, CCNN is superior to the other techniques investigated in this paper. Also, a sensitivity analysis of the variables identifies borrower’s account functioning, previous occupation, guarantees, car ownership, and loan purpose as key variables in the forecasting and decision making process which are at the heart of overall credit policy

Gender Bias Claims in Farm Service Agency’s Lending Decisions

This study analyzes the courts’ denial of women farmers’ motion for class-action certification of their lawsuits alleging gender discrimination in Farm Service Agency (FSA) lending decisions. The plaintiffs’ claim of “commonality†of circumstances in women farmers’ dealings with FSA is tested using a four-year sampling of Georgia FSA loan applications. The econometric framework has been developed after accounting for the separability of loan approval and amount decisions, as well as endogeneity issues through instrumental variable estimation. This study’s results do not produce overwhelming evidence of gender bias in FSA loan approval decisions and in favor of the “commonality†argument among Georgia FSA farm loan applicants.class-action suit, credit risk, creditworthiness, gender discrimination, Heckman selection, instrumental variable probit, Labor and Human Capital,

Estimating an SME investment gap and the contribution of financing frictions. ESRI WP589, March 2018

In this paper, we use firm-level survey data to explore the determinants of SME investment activity and the extent to which observed investment is in line with that suggested by economic fundamentals. In contrast to previous literature which has focused on whether investment gaps exist at a more aggregate level, we find evidence that for SMEs actual investment is below what would be expected given how companies are currently performing. The estimated magnitude of this investment gap is economically meaningful at just over 30 per cent in 2016. We explore the extent to which the gap is explained by financial market challenges such as access to finance, interest rates, and the availability of collateral. Financing frictions are found to account for a moderate share of the overall investment gap (between 10 per cent and 20 per cent of the gap)