Consumer Bankruptcy Prediction Using Balanced and Imbalanced Data

Brygała, Magdalena

doi:10.3390/risks10020024

Cited by 14 publications

(14 citation statements)

References 55 publications

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“…Unfortunately, the authors of the C&RT model did not present the type I and II errors (E1, E2). Looking at the study by Brygała (2022) it is clear that although the imbalanced sample gives better results, in practice the model is completely unable to predict the risk for bankrupt consumers. The logit model estimated for American consumers is characterized by a very high type I error (99.71%).…”

Section: Resultsmentioning

confidence: 99%

Implementing artificial intelligence in forecasting the risk of personal bankruptcies in Poland and Taiwan

Korol

Fotiadis

2022

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Research background: The global financial crisis from 2007 to 2012, the COVID-19 pandemic, and the current war in Ukraine have dramatically increased the risk of consumer bankruptcies worldwide. All three crises negatively impact the financial situation of households due to increased interest rates, inflation rates, volatile exchange rates, and other significant macroeconomic factors. Financial difficulties may arise when the private person is unable to maintain a habitual standard of living. This means that anyone can become financially vulnerable regardless of wealth or education level. Therefore, forecasting consumer bankruptcy risk has received increasing scientific and public attention. Purpose of the article: This study proposes artificial intelligence solutions to address the increased importance of the personal bankruptcy phenomenon and the growing need for reliable forecasting models. The objective of this paper is to develop six models for forecasting personal bankruptcies in Poland and Taiwan with the use of three soft-computing techniques. Methods: Six models were developed to forecast the risk of insolvency: three for Polish households and three for Taiwanese consumers, using fuzzy sets, genetic algorithms, and artificial neural networks. This research relied on four samples. Two were learning samples (one for each country), and two were testing samples, also one for each country separately. Both testing samples contain 500 bankrupt and 500 nonbankrupt households, while each learning sample consists of 100 insolvent and 100 solvent natural persons. Findings & value added: This study presents a solution for effective bankruptcy risk forecasting by implementing both highly effective and usable methods and proposes a new type of ratios that combine the evaluated consumers? financial and demographic characteristics. The usage of such ratios also improves the versatility of the presented models, as they are not denominated in monetary value or strictly in demographic units. This would be limited to use in only one country but can be widely used in other regions of the world.

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Section: Resultsmentioning

confidence: 99%

Implementing artificial intelligence in forecasting the risk of personal bankruptcies in Poland and Taiwan

Korol

Fotiadis

2022

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“…Ciampi et al, 2021;Mirza et al, 2023), proposing some innovative approaches to improve the predictive ability of models. Moreover, the changes in the macroeconomic and microeconomic environment require modifications and searching for more precise methods of financial health prediction (Brygala, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

Bankruptcy Prediction Model Development and its Implications on Financial Performance in Slovakia

Gajdosikova

Valášková

2023

Economics and Culture

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Research purpose. Financial distress being a global phenomenon makes it impact firms in all sectors of the economy and predicting corporate bankruptcy has become a crucial issue in economics. At the beginning of the last century, the first studies aimed to predict corporate bankruptcy were published. In Slovakia, however, several prediction models were developed with a significant delay. The main aim of this paper is to develop a model for predicting bankruptcy based on the financial information of 3,783 Slovak enterprises operating in the manufacturing and construction sectors in 2020 and 2021. Design / Methodology / Approach. A prediction model that uses the appropriate financial indicators as predictors may be developed using multiple discriminant analysis. Multiple discriminant analysis is currently used in prediction model development. In this case, financial health is assessed using several variables that are weighted in order to maximise the difference between the average value calculated in the group of prosperous and non-prosperous firms. When developing a bankruptcy prediction model based on multiple discriminant analysis, it is crucial to determine the independent variables used as primary financial health predictors. Findings. Due to the discriminant analysis results, the corporate debt level of the monitored firms may be regarded as appropriate. Despite the fact that the model identified 215 firms in financial distress due to an insufficient debt level, 3,568 out of 3,783 Slovak enterprises operating in the manufacturing and construction sectors did not have any problems with financing their debts. The self-financing ratio was identified in the developed model as the variable with the highest accuracy. Based on the results, the developed model has an overall discriminant ability of 93% since bankruptcy prediction models require strong discriminating abilities to be used in practice. Originality / Value / Practical implications. The principal contribution of the paper is its application of the latest available data, which could help in more accurate financial stability predictions for firms during the current difficult period. Additionally, this is a ground-breaking research study in Slovakia that models the financial health of enterprises in the post-pandemic period.

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“…This paper presents an original solution tailored to a narrowly specialized environment, with potential applicability in other countries and time periods, encouraging broader comparisons and deeper investigations into the problem. As noted by Brygała (2022), the efficiency of logistic regression with unbalanced data is lower than with balanced data. Given the low proportion of bankrupt samples, developing accurate prediction models remains a challenge (Garcia 2022), highlighting the difficulty of obtaining high quality and sufficient data.…”

Section: Introductionmentioning

confidence: 97%

“…In financial decision making, the ability to predict bankruptcy or anticipate challenges in meeting financial obligations holds paramount importance (Brygała 2022). The consequences of financial failure have a significant impact on creditors.…”

Section: Introductionmentioning

confidence: 99%

Is Artificial Intelligence Really More Accurate in Predicting Bankruptcy?

Letkovský,

Jenčová,

Vašaničová

2024

IJFS

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Predicting bankruptcy within selected industries is crucial because of the potential ripple effects and unique characteristics of those industries. It serves as a risk management tool, guiding various stakeholders in making decisions. While artificial intelligence (AI) has shown high success rates in classification tasks, it remains uncertain whether its use significantly enhances the potential for early warning of impending problems. The following question arises: will classical methods eventually replace the effectiveness of these advanced techniques? This paper sheds light on the fact that even classical methods continue to achieve results that are not far behind, highlighting their enduring importance in financial analysis. This paper aims to develop bankruptcy prediction models for the chemical industry in Slovakia and to compare their effectiveness. Predictions are generated using the classical logistic regression (LR) method as well as AI techniques, artificial neural networks (ANNs), support vector machines (SVMs), and decision trees (DTs). The analysis aims to determine which of the employed methods is the most efficient. The research sample consists of circa 600 enterprises operating in the Slovak chemical industry. The selection of eleven financial indicators used for bankruptcy prediction was grounded in prior research and existing literature. The results show that all of the explored methods yielded highly similar outcomes. Therefore, determining the clear superiority of any single method is a difficult task. This might be partially due to the potentially reduced quality of the input data. In addition to classical statistical methods employed in econometrics, there is an ongoing development of AI-based models and their hybrid forms. The following question arises: to what extent can these newer approaches enhance accuracy and effectiveness?

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Consumer Bankruptcy Prediction Using Balanced and Imbalanced Data

Cited by 14 publications

References 55 publications

Implementing artificial intelligence in forecasting the risk of personal bankruptcies in Poland and Taiwan

Implementing artificial intelligence in forecasting the risk of personal bankruptcies in Poland and Taiwan

Bankruptcy Prediction Model Development and its Implications on Financial Performance in Slovakia

Is Artificial Intelligence Really More Accurate in Predicting Bankruptcy?

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