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

IF 7.6 1区 经济学 Q1 ECONOMICS Oeconomia Copernicana Pub Date : 2022-06-30 DOI:10.24136/oc.2022.013
Tomasz Korol, A. Fotiadis
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引用次数: 3

Abstract

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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在波兰和台湾实施人工智能预测个人破产风险
研究背景:2007年至2012年的全球金融危机、2019冠状病毒病(COVID-19)大流行以及当前的乌克兰战争大大增加了全球消费者破产的风险。由于利率上升、通货膨胀率、汇率波动和其他重大宏观经济因素,这三次危机都对家庭的财务状况产生了负面影响。当个人无法维持惯常的生活水平时,就会出现经济困难。这意味着无论财富或教育水平如何,任何人都可能在经济上变得脆弱。因此,对消费者破产风险的预测越来越受到科学界和公众的关注。文章目的:本研究提出人工智能解决方案,以解决个人破产现象日益重要和对可靠预测模型日益增长的需求。本文的目的是利用三种软计算技术,开发六个预测波兰和台湾个人破产的模型。方法:运用模糊集、遗传演算法及人工神经网路,建立波兰家庭及台湾消费者破产风险预测模型。这项研究依赖于四个样本。两个是学习样本(每个国家一个),两个是测试样本(每个国家一个)。两个测试样本分别包含500个破产家庭和500个非破产家庭,而每个学习样本分别包含100个破产自然人和100个有偿债能力的自然人。本研究提出了一种有效的破产风险预测方法,并提出了一种结合被评估消费者的新型比率。金融和人口特征。这种比率的使用也提高了所提模型的通用性,因为它们不以货币价值或严格以人口单位计价。这将仅限于在一个国家使用,但可以在世界其他区域广泛使用。
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来源期刊
CiteScore
13.70
自引率
5.90%
发文量
26
审稿时长
24 weeks
期刊介绍: The Oeconomia Copernicana is an academic quarterly journal aimed at academicians, economic policymakers, and students studying finance, accounting, management, and economics. It publishes academic articles on contemporary issues in economics, finance, banking, accounting, and management from various research perspectives. The journal's mission is to publish advanced theoretical and empirical research that contributes to the development of these disciplines and has practical relevance. The journal encourages the use of various research methods, including falsification of conventional understanding, theory building through inductive or qualitative research, first empirical testing of theories, meta-analysis with theoretical implications, constructive replication, and a combination of qualitative, quantitative, field, laboratory, and meta-analytic approaches. While the journal prioritizes comprehensive manuscripts that include methodological-based theoretical and empirical research with implications for policymaking, it also welcomes submissions focused solely on theory or methodology.
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