Hybrid model using logit and nonparametric methods for predicting micro-entity failure

Following the calls from literature on bankruptcy, a parsimonious hybrid bankruptcy model is developed in this paper by combining parametric and non-parametric approaches.To this end, the variables with the highest predictive power to detect bankruptcy are selected using logistic regression (LR). Subsequently, alternative non-parametric methods (Multilayer Perceptron, Rough Set, and Classification-Regression Trees) are applied, in turn, to firms classified as either “bankrupt” or “not bankrupt”. Our findings show that hybrid models, particularly those combining LR and Multilayer Perceptron, offer better accuracy performance and interpretability and converge faster than each method implemented in isolation. Moreover, the authors demonstrate that the introduction of non-financial and macroeconomic variables complement financial ratios for bankruptcy prediction

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%)

Finding rules for audit opinions prediction through data mining methods

Nowadays data mining, which is used in various accounting and financial applications, has received a great deal of attention. One of these applications is predicting and identifying the audit opinion type. The objective of research is to help auditors identify audit opinions by using a support vector machine from data mining methods. The system receives the data from financial reports and identifies the type of audit opinions. This approach combine support vector machine with a decision tree that can understand and interpret the obtained results. In this paper, a novel approach for rule extraction from support vector machine and decision tree is presented and its application is shown in the prediction of audit opinions. The research result is 30 rules that predict the audit opinions

Finding rules for audit opinions prediction through data mining methods

Nowadays data mining, which is used in various accounting and financial applications, has received a great deal of attention. One of these applications is predicting and identifying the audit opinion type. The objective of research is to help auditors identify audit opinions by using a support vector machine from data mining methods. The system receives the data from financial reports and identifies the type of audit opinions. This approach combine support vector machine with a decision tree that can understand and interpret the obtained results. In this paper, a novel approach for rule extraction from support vector machine and decision tree is presented and its application is shown in the prediction of audit opinions. The research result is 30 rules that predict the audit opinions

Genetic Algorithm-based Feature Selection for Auditing Decisions

When examining a firm’s financial statements, independent auditors seek to render opinions on their fairness, accuracy, presence of fraud, and going concern, among others. This research focuses on the going concern, and the ability to predict when the going concern is flagged based on an array of accounting measures. It seeks to determine a parsimonious set of measures that can accurately predict when the going concern is raised, when using a linear kernel support vector machine for prediction. A genetic algorithm is employed to effectively reduce the set of measures without compromising accuracy of prediction. Using data from audits of public firms, a parsimonious model is created utilizing only 8 measures from a set of 35 available measures. The model exhibits 98.6% accuracy, and outperforms several other machine learning techniques

A comparative analysis of decision trees vis-a-vis other computational data mining techniques in automotive insurance fraud detection

The development and application of computational data mining techniques in financial fraud detection and business failure prediction has become a popular cross-disciplinary research area in recent times involving financial economists, forensic accountants and computational modellers. Some of the computational techniques popularly used in the context of - financial fraud detection and business failure prediction can also be effectively applied in the detection of fraudulent insurance claims and therefore, can be of immense practical value to the insurance industry. We provide a comparative analysis of prediction performance of a battery of data mining techniques using real-life automotive insurance fraud data. While the data we have used in our paper is US-based, the computational techniques we have tested can be adapted and generally applied to detect similar insurance frauds in other countries as well where an organized automotive insurance industry exists

A survey of outlier detection methodologies

Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review