> **Title:** Bankruptcy Prediction Model with Risk Factors using Fuzzy Logic Approach > > The authors greatly appreciate the constructive comments and suggestions made by the reviewers in order to improve the manuscript. The followings are the responses to the comments and suggestions.

Aspects that need to be corrected	Aspects that have been added/corrected
1) Literature Review Literature is updated with the recent findings but not adequate. The latest paper is in 2021. If possible, are there any other papers that are discuss regarding the dynamic bankruptcy prediction models?	Corrections: We thank you for that suggestion. We have cited numerous journal papers to show the dynamic of bankruptcy prediction models. Added sentences (Pg. 2, 2^nd paragraph, line 5-15): Statistical models and artificial intelligence models are two major types of models use to forecast bankruptcy. In the 1960s, Beaver (1966) and Altman (1968) initiated studies on predicting bankruptcy. Since the works of Beaver and Altman, various statistical techniques have been used in numerous research studies (Alaka et al., 2018; Balcaen & Ooghe, 2006; Giannopoulos & Sigbjørnsen, 2019; Jackson & Wood, 2013). According to Balcaen and Ooghe (2006), multivariate discriminant analysis and logistic regression models are two widely used statistical methods. Although statistical methods are widely employed in bankruptcy prediction, they have numerous flaws in statistical assumptions such as linearity, normality, and independence among variables that have been documented in multiple publications (Jayasekera, 2018; Tian & Yu, 2017). As a result, intelligent bankruptcy prediction techniques, including neural networks, genetic algorithms, vector support machines, and fuzzy logic, have gained prominence (Dong et al., 2018; García et al., 2019; Hosaka, 2019).

1) Literature Review

Literature is updated with the recent findings but not adequate. The latest paper is in 2021. If possible, are there any other papers that are discuss regarding the dynamic bankruptcy prediction models?

Corrections:

We thank you for that suggestion. We have cited numerous journal papers to show the dynamic of bankruptcy prediction models.

Added sentences

(Pg. 2, 2^nd paragraph, line 5-15):

Statistical models and artificial intelligence models are two major types of models use to forecast bankruptcy. In the 1960s, Beaver (1966) and Altman (1968) initiated studies on predicting bankruptcy. Since the works of Beaver and Altman, various statistical techniques have been used in numerous research studies (Alaka et al., 2018; Balcaen & Ooghe, 2006; Giannopoulos & Sigbjørnsen, 2019; Jackson & Wood, 2013). According to Balcaen and Ooghe (2006), multivariate discriminant analysis and logistic regression models are two widely used statistical methods. Although statistical methods are widely employed in bankruptcy prediction, they have numerous flaws in statistical assumptions such as linearity, normality, and independence among variables that have been documented in multiple publications (Jayasekera, 2018; Tian & Yu, 2017). As a result, intelligent bankruptcy prediction techniques, including neural networks, genetic algorithms, vector support machines, and fuzzy logic, have gained prominence (Dong et al., 2018; García et al., 2019; Hosaka, 2019).

Aspects that need to be corrected	Aspects that have been added/ corrected
2) Introduction The problem and background of this study is clearly stated but the reasons for conducting this research/study is not clearly stated. No gap highlighted. If possible, highlight the gap and contribution of this paper and previous literature. Or how your work differs from published work?	Corrections: We thank you for the comment. The reasons for conducting this study is added to Pg. 2, 3^rd paragraph, line 1-3. We thank you for the comment. The gap and contribution of this paper are added to Pg. 2, 3^rd paragraph, line 1-3.
3) Methodology In my opinion, to include one figure that give an idea on how the basic configuration of Fuzzy Inference System. Then, the “IF-THEN” set decision rule (on page 3) used in this study, if possible, to present it in Table form.	Corrections: We thank you for that suggestion. A figure of basic configuration of Fuzzy Inference System is added on Figure 1, Pg. 3. We appreciate your suggestion. The “IF-THEN” set decision rule (on page 3) was changed to table form.
4) Results Results are developed by merging the input and output variables and investigating their relationship and extracted from 250 respondents, where it produced 25 rules in figures and table forms. The findings are well explained. However, it is better to provide the reader sufficient information on how the data were produced.	We thank you for that suggestion. Information on how the data were produced is added Pg. 5, line 3-5 under RESULT AND DISCUSSION. 3-5
5) Discussion Discussion is appropriate. If possible, indicate how the results relate to expectations and to the literature previously cited and show that how your research has moved the body of scientific knowledge forward.	We thank you for the comment. Sentences have been added to indicate how the results relate to expectations and previously cited literature to show this research has moved the body of scientific knowledge forward.

2) Introduction

The problem and background of this study is clearly stated but the reasons for conducting this research/study is not clearly stated.

No gap highlighted. If possible, highlight the gap and contribution of this paper and previous literature. Or how your work differs from published work?

Corrections:

We thank you for the comment. The reasons for conducting this study is added to Pg. 2, 3^rd paragraph, line 1-3.

We thank you for the comment. The gap and contribution of this paper are added to Pg. 2, 3^rd paragraph, line 1-3.

3) Methodology

In my opinion, to include one figure that give an idea on how the basic configuration of Fuzzy Inference System.

Then, the “IF-THEN” set decision rule (on page 3) used in this study, if possible, to present it in Table form.

Corrections:

We thank you for that suggestion. A figure of basic configuration of Fuzzy Inference System is added on Figure 1, Pg. 3.

We appreciate your suggestion. The “IF-THEN” set decision rule (on page 3) was changed to table form.

4) Results

Results are developed by merging the input and output variables and investigating their relationship and extracted from 250 respondents, where it produced 25 rules in figures and table forms. The findings are well explained. However, it is better to provide the reader sufficient information on how the data were produced.

We thank you for that suggestion. Information on how the data were produced is added Pg. 5, line 3-5 under RESULT AND DISCUSSION.

3-5

5) Discussion

Discussion is appropriate. If possible, indicate how the results relate to expectations and to the literature previously cited and show that how your research has moved the body of scientific knowledge forward.

We thank you for the comment. Sentences have been added to indicate how the results relate to expectations and previously cited literature to show this research has moved the body of scientific knowledge forward.

We hoped our explanation answers your uncertainty. If the information given is not sufficient, we are always happy to provide other feedbacks. Thank you for giving us this chance.