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

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/

Modeling Bankruptcy Prediction for Non-Financial Firms: The Case of Pakistan

This paper aims to identify the financial ratios that are most significant in bankruptcy prediction for the non-financial sector of Pakistan based on a sample of companies which became bankrupt over the 1996-2006 period. Twenty four financial ratios covering four important financial attributes namely profitability, liquidity, leverage, and turnover ratios) were examined for a five-year period prior bankruptcy. The discriminant analysis produced a parsimonious model of three variables viz. sales to total assets, EBIT to current liabilities, and cash flow ratio. Our estimates provide evidence that the firms having Z value below zero fall into the “bankrupt” whereas the firms with Z value above zero fall into the “non-bankrupt” category. The model achieved 76.9% prediction accuracy when it is applied to forecast bankruptcies on the underlying sample.Bankruptcy; Z-Score; Non-Financial Firms; Financial Ratios; Pakistan

Financial Ratios, Size, Industry and Interest Rate Issues in Company Failure: An Extended Multidimensional Scaling Analysis

Three-way multidimensional scaling methods are used to study the differences between UK failed and continuing companies from 1993 to 2001. The technique allows for visual representations of the results, so that qualitative information can be brought to bear when judging the health of a company. It is shown that it is important to take into account company size and area of activity. Results also suggest that the ratio structure of the companies varies between years in response to changes in the interest rates, suggesting that the frontier between failing and continuing firms moves in response to the economic cycle

A comparison of corporate distress prediction models in Brazil: hybrid neural networks, logit models and discriminant analysis

This paper looks at the ability of a relatively new technique, hybrid ANN's, to predict corporate distress in Brazil. These models are compared with traditional statistical techniques and conventional ANN models. The results suggest that hybrid neural networks outperform all other models in predicting firms in financial distress one year prior to the event. This suggests that for researchers, policymakers and others interested in early warning systems, hybrid networks may be a useful tool for predicting firm failure.hybrid neural networks, corporate failures

Improving bankruptcy prediction in micro-entities by using nonlinear effects and non-financial variables

The use of non-parametric methodologies, the introduction of non-financial variables, and the development of models geared towards the homogeneous characteristics of corporate sub-populations have recently experienced a surge of interest in the bankruptcy literature. However, no research on default prediction has yet focused on micro-entities (MEs), despite such firms’ importance in the global economy. This paper builds the first bankruptcy model especially designed for MEs by using a wide set of accounts from 1999 to 2008 and applying artificial neural networks (ANNs). Our findings show that ANNs outperform the traditional logistic regression (LR) models. In addition, we also report that, thanks to the introduction of non-financial predictors related to age, the delay in filing accounts, legal action by creditors to recover unpaid debts, and the ownership features of the company, the improvement with respect to the use of solely financial information is 3.6%, which is even higher than the improvement that involves the use of the best ANN (2.6%)

Toward the standardization of venture capital investment evaluation : decision criteria for rating investee business plans

This study examined the criteria used by venture capitalists to evaluate business plans in order to make investment decisions. A literature survey revealed two competing theories: &ldquo;espoused criteria&rdquo; where evaluation decisions are based on what venture capitalists say are the decisive factors; versus the use of &ldquo;known attributes&rdquo; that successful ventures actually possess. Brunswik&rsquo;s Lens Model from Social Judgment Theory guided an empirical investigation of several different evaluation methods based on information contained in 129 business plans submitted for venture capital over a 3 year period. Data evaluation culminated in the comparison of the percentage of correct decisions (&ldquo;hit-rate&rdquo;) for each method. We found that decisions based on the known attributes of successful ventures have significantly better hit-rates than decisions made using espoused criteria. Discussion centred on the goal of achieving consistency in the conduct of venture analysis. Process standardization can aid in the achievement of consistency. Future research will both deepen and broaden insights.<br /

Predicting Romanian Financial Distressed Companies

The study consisted in collecting financial information for a group of distressed and non-distressed Romanian listed companies during the period 2006–2008, in order to create early warning signals for financial distressed companies using the following methodologies: the Logistic and the Hazard model, the CHAID decision tree model and the Artificial Neural Network model (ANN). For each company a set of 14 financial ratios, that reflect the company’s profitability, solvency, asset utilization, growth ability and size, were calculated and then used in the study. A Principal Component Analysis was also used to reduce the dimensionality of the data space and to allow seeing that the 2 types of companies do form 2 distinct groups suggesting that the ratios used are useful enough to predict financial distress. The following 4 data sets were separately analyzed: first-year data to predict distress one year ahead, second-year data for a 2 year-ahead prediction, third-year data for a 3 year-ahead prediction, as well as cumulative three-year data to predict distress 1 year ahead by letting the ratios vary in time. For each data set, several prediction models were created using CHAID, the Logit and Hazard models as well as the ANN and the hybrid-ANN. The results are consistent with the theory and also to previous studies and the out-of-sample forecast accuracy of the estimated models of 73%-100% indicates that the proposed early warning models for the Romanian listed companies are quite efficient.early warning signals, CHAID, ANN