Bankruptcy prediction model with risk factors using fuzzy logic approach / Teoh Yeong Kin ... [et al.]

Kin, Teoh Yeong and Ahmad Aizam, Akmal Haziq and Abu Hasan, Suzanawati and Ariffin, Anas Fathul and Mahat, Norpah (2021) Bankruptcy prediction model with risk factors using fuzzy logic approach / Teoh Yeong Kin ... [et al.]. Journal of Computing Research and Innovation (JCRINN), 6 (2): 11. pp. 102-110. ISSN 2600-8793

Abstract

Forecasting bankruptcy remains crucial, especially during this pandemic. Managers, financial institutions, and government agencies rely on the information regarding an impending bankruptcy threat to make decisions. This paper developed a straightforward bankruptcy prediction model using the fuzzy logic approach for individuals and companies to evaluate their performance and analyse the tendency of getting bankrupt. A sample of 250 respondents from banks and financial firms were tested using the qualitative risk factors, namely, industrial risk, management risk, financial flexibility, credibility, competitiveness, and operational risk. This study provides a comprehensive analysis using the Fuzzy Inference System (FIS) editor in the MATLAB software, where the model's accuracy is compared to the actual results. The results show an accuracy rate of 99.20%, indicating that this approach can determine the likelihood of bankruptcy. The fuzzy logic approach can improve prediction accuracy while also guiding decision-makers in detecting and preventing possible financial crises in their early phases.

Metadata

Item Type: Article
Creators:
Creators
Email / ID Num.
Kin, Teoh Yeong
ykteoh@uitm.edu.my
Ahmad Aizam, Akmal Haziq
UNSPECIFIED
Abu Hasan, Suzanawati
UNSPECIFIED
Ariffin, Anas Fathul
UNSPECIFIED
Mahat, Norpah
UNSPECIFIED
Subjects: H Social Sciences > HG Finance > Personal finance. Financial literacy
H Social Sciences > HG Finance > Credit. Debt. Loans
Q Science > QA Mathematics > Fuzzy logic
Divisions: Universiti Teknologi MARA, Perlis > Arau Campus
Journal or Publication Title: Journal of Computing Research and Innovation (JCRINN)
UiTM Journal Collections: UiTM Journal > Journal of Computing Research and Innovation (JCRINN)
ISSN: 2600-8793
Volume: 6
Number: 2
Page Range: pp. 102-110
Keywords: Baankruptcy prediction, fuzzy logic, risk factors, Fuzzy Inference System
Date: 2021
URI: https://ir.uitm.edu.my/id/eprint/60205
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