TY  - BOOK
T1  - Implementing artificial intelligence in forecasting the risk of personal bankruptcies in Poland and Taiwan
A1  - Korol, Tomasz
A1  - Fotiadis, Anestis K.
Y1  - 2022///
KW  - Regression modelling
KW  - Taiwan
KW  - artificial neural networks
KW  - consumer bankruptcy
KW  - fuzzy logic
KW  - genetic algorithms
KW  - the financial crisis of households
JF  - Oeconomia Copernicana
VL  - 13
IS  - 2
SP  - 407
EP  - 438
SN  - 0000000188067
DO  - 10.24136/oc.2022.013
L1  - file:///E:/jurnal/2023/j of economics and business/paper/Korol 2022 Implementing_artificial_intell.pdf
N2  - 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.
ER  -