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Bankruptcy is a financial failure of a business or an organization. Different kinds of bankruptcy prediction techniques are proposed to predict it. But, they are restricted as techniques in predicting the bankruptcy and not addressing the associated activities like acquiring the suitable data and delivering the results to the user after processing it. This situation demands to look for a comprehensive solution for predicting bankruptcy with intelligence. The paper aims to discuss these issues.

To model Business Intelligence (BI) solution for BP the concept of reference model is used. A Reference Model for Business Intelligence to Predict Bankruptcy (RMBIPB) is designed by applying unit operations as hierarchical structure with abstract components. The layers of RMBIPB are constructed from the hierarchical structure of the model and the components, which are part of the reference model. In this model, each layer is designed based on the functional requirements of the Business Intelligence System (BIS).

This reference model exhibits the non functional software qualities intended for the appropriate unit operations. It has flexible design in which techniques are selected with minimal effort to conduct the bankruptcy prediction. The same reference model for another domain can be implemented with different kinds of techniques for bankruptcy prediction.

This model is designed using unit operations and the software qualities exhibited by RMBIPB are limited by unit operations. The data set which is applied in RMBIPB is limited to Indian banks.

A comprehensive bankruptcy prediction model using BI with customized reporting.

The purpose of this paper is to determine whether the original and the revised versions of the existing prediction models are the best tools for assessing the going concern assumption of a firm in the creditor-oriented regime.

The analysis begins from estimating the classification accuracy of the original versions of the bankruptcy, going concern and liquidation prediction models. At the second step, the revised versions of the aforesaid existing prediction models are developed. At the third step, the accounting-based going concern prediction model is proposed by using multiple discriminant analysis for the creditor-oriented regime. The sample contains the financial ratios of manufacturing firms for the period 1997–2014.

The finding indicates that the five discriminatory variables, which belong to “income statement” and “statement of financial position,” of the proposed model are not only useful for evaluating the going concern assumption of a firm, but also give aid for evaluating the financial fraud risk of a firm as compared to the original and revised versions of the prediction models that are developed for the debtor-oriented regime.

The external validity of the proposed prediction model can be tested on the large data sets of the countries where the liquidation provisions are a part of their local corporate law.

The proposed accounting prediction model will be helpful for the internal and external auditors in order to determine the going concern assumption at planning, performing and evaluation stages.

The proposed accounting-based going concern prediction model is based on liquidated firms.

This study aims to develop an assessment methodology using a Bayesian network (BN) to predict the failure probability of oil tanker shipping firms.

This paper proposes a bankruptcy prediction model by applying the hybrid of logistic regression and Bayesian probabilistic networks.

The proposed model shows its potential of contributing to a powerful tool to predict financial bankruptcy of shipping operators, and provides important insights to the maritime community as to what performance measures should be taken to ensure the shipping companies’ financial soundness under dynamic environments.

The model and its associated variables can be expanded to include more factors for an in-depth analysis in future when the detailed information at firm level becomes available.

The results of this study can be implemented to oil tanker shipping firms as a prediction tool for bankruptcy rate.

Incorporating quantitative statistical measurement, the application of BN in financial risk management provides advantages to develop a powerful early warning system in shipping, which has unique characteristics such as capital intensive and mobile assets, possibly leading to catastrophic consequences.

The Mexican housing industry was hindered by a shrinking market and tighter financial conditions related to the Great Recession. Moreover, in 2013, a major change in public policy further modified this industry’s environment. Mexico’s new urban development policy supported inner-city new housing, in contrast to the previous policy that incentivized sprawling. Three out of eight publicly traded housing companies filed for bankruptcy protection in 2013-2014, arguably because of the effects of the Great Recession and the new housing policy. The purpose of this study is to identify firm-level factors that caused some firms to file for bankruptcy protection.

Three approaches were used to analyze the housing industry in Mexico from 2006 to 2015. First, a policy analysis focused on the new housing policy and its consequences for housebuilding companies. Second, a financial analysis of the two economic shocks was performed in search for the transmission mechanisms in the companies’ financial metrics. Third, a retrospective analysis using the Fisher’s exact test was used to identify variables statistically associated with companies filing for bankruptcy protection.

There are two features significantly associated with bankruptcy protection: increasing market share while being vertically integrated, as a response to the Great Recession, and the relative magnitude of the loss on firms’ inventory value due to the new public policy. Neither Altman’s Z-score values nor firm size or degree of integration are significantly related to bankruptcy.

The small sample size presented a challenge, as most statistical methodologies require large samples; however, this was overcome by using the Fisher’s exact test.

The main contribution of this paper is the statistical identification of the possible causes for bankruptcy protection in Mexico amongst homebuilding firms in 2013 and 2014, which have not previously been reported in the literature.

The paper's aim is to test the usefulness of ratio analysis to predict bankruptcy in a period of stability of an emerging economy, such as the case of Argentina in the 1990s.

Financial profiles of 22 bankrupt and healthy companies are examined and a model is built using the multiple discriminant analysis technique, thus providing comparability with previous studies.

The set of models tested in this paper show that the financial data of Argentine companies in the 1990s do have information content, but the model to use depends on the preferences of the decision maker. Comparing models it is observed a common use of solvency ratios in terms of total assets and profitability ratios in terms of sales.

Data availability constitutes the primary limitation of this and similar studies, here is reflected in the sample size: 11 healthy and 11 bankrupt.

The model can be used to assist investors, creditors, and regulators in Argentina and other emerging economies to predict business failure. The Z ′‐score model of Altman can be used for public companies in emerging economies because it pays attention to solvency indicators, but in rapid changing environment, profitability ratios should also be considered.

The incremental information content of profitability and solvency in predicting bankruptcy is examined and a simple and reliable failure prediction model for large Argentinean firms is developed. Also this paper offers a classification method that is publicly available to all investors and creditors interested in Argentinean companies.

An appropriate use of neural computing techniques is to apply them to corporate bankruptcy prediction, where conventional solutions can be hard to obtain. Having said that, choosing an appropriate Artificial Neural Network topology (ANN) for predicting corporate bankruptcy would remain a daunting prospect. The context of the problem is that there are no fixed rules in determining the ANN structure or its parameter values, a large number of ANN topologies may have to be constructed with different structures and parameters before determining an acceptable model. The trial‐and‐error process can be tedious, and the experience of the ANN user in constructing the topologies is invaluable in the search for a good model. Yet, a permanent solution does not exist. This paper identifies a non trivial novel approach for implementing artificial neural networks for the prediction of corporate bankruptcy by applying inter‐connected neural networks. The proposed approach is to produce a neural network architecture that captures the underlying characteristics of the problem domain. The research primarily employed financial data sets from the London Stock Exchange and Jordans financial database of major public and private British companies. Early results indicate that an ANN appears to outperform the traditional approach in forecasting corporate bankruptcy.

In the past decade, the development of the global economy, the change in organizational structures and the maturing of new technologies have led to considerable changes in business structures. Emergency situations, such as the recent COVID-19 pandemic, have led many companies to declare bankruptcy. In this context, the present study aims to analyze strategic opinions of company executives in a declaration of bankruptcy.

To this end, an innovative approach is applied to strategic management and business. First, the authors conducted 14 interviews with executives, and the interview data were transcribed. Second, using textual analysis and data mining techniques, the transcripts were analyzed to understand the importance of indicators identified as relevant in companies in a declaration of bankruptcy.

This resulted in identification of 10 relevant indicators perceived by executives to avoid or anticipate a state of bankruptcy, including innovation, business adaptability, room for improvement in production processes, time to react to situations of alarm, layoffs, support from public institutions, suppliers, international and national regulations, impact on the industry, credits and debts.

The paper concludes with a discussion of important theoretical and practical implications of these findings for the industry. Also, strategic management decision-making strategies are presented as a result of the innovative textual analysis approach used.

The purpose of this paper is to investigate to what extent the increased insolvency filings by migrants since the enactment of the consumer insolvency law in 1999 is associated with moral hazard. It describes the profile of migrant debtors and highlights the areas of moral hazard. This study aims to propose changes to the consumer bankruptcy system.

Empirical evidence for this work consists of primary data from 435 individuals mainly with immigration background, who were declared bankrupt by district courts (Amtsgericht). Both qualitative and quantitative research types were used. Interviews helped to attain an in-depth understanding of the way in which any misconduct may take place. Quantitative data were gathered to understand the debt profile of migrant debtors, types of liabilities and creditors’ reactions to write-off requests.

The paper provides empirical insights about the way misconduct is pursued and suggests that neither party, i.e. the debtors through debt counsellors and creditors/factoring companies or their representatives, is entirely free of unethical practice. Hence, the paper stresses the need to establish public agencies, which provide joint mediation services for private debtors and their creditors alike.

Data collected for the purpose of this study may not be comprehensive because given the sensitivity of the area of study that is misconduct – including breaking the law – not all machinations may have been revealed and described in this work. Therefore, further research needs to be conducted in this field.

The paper has implications for policymakers. Consumer bankruptcy system is relatively new and needs to be amended to allow debtors and creditors to negotiate write-offs not by sending countless letters through their respective representatives, which is also carried out over a long period of time, but to try to come to terms in one agency, which is responsible for both sides.

The findings in this paper may provide some valuable insights, which could also give impulses to debates on problems that may come with immigration.

To the best of the author’s knowledge, no research exists that analyzes the topic at hand with such extensive data and using both methods of research at the same time.

In finance the proposition that market prices in company securities are efficient in respect of public information has received substantial confirmation over the years (Fama, 1970, 1976). Since efficiency means that there are no systematic trading rules which generate abnormal returns and since the public information concerned consists to a large extent of published accounting data, it is somewhat surprising to find that a stream of empirical work (e.g. Altman, 1971) using the same data claims to provide predictions of bankruptcy or quasi‐bankruptcy which are of considerable accuracy and usefulness.

This paper aims to examine potential causes of bankruptcy as they relate to hurricane damage. Investigate whether hurricanes result in personal bankruptcy filings due to real property damages. Strengthen existing descriptive results by using fully modified ordinary least squares (FMOLS).

Lagged FMOLS model is used with data from states that suffered hurricane damage between 2000 through 2020. FMOLS controls for various financial distresses that can cause bankruptcy filings.

Bankruptcy is usually filed for within one year of a hurricane. Changes in house prices and hurricane severity were significant indicators of bankruptcy filings. However, the divorce rate, commonly thought of as a primary reason for bankruptcy, is insignificant.

Data was available on a state level for the independent variables. Hurricane damage needed to be financially significant enough for inland flooding to be measurable and influential.

Establishes that financial distress comes from several sources, not just home damage. Financial distress is highly correlated with whether a home was insured. Divorce does not cause bankruptcy filings.

Federal flood insurance programs should be reexamined. Having a broader all-risk homeowner policy could reduce the number of households that file for bankruptcy after a hurricane.

Existing research uses descriptive statistics and obtains mixed findings regarding the association between hurricane damage and bankruptcy filings. The FMOLS approach provides clarity about this association.