How gender and emotions bias the credit decision-making in banking firms

Bacha, Sami; Azouzi, Mohamed Ali

doi:10.1016/j.jbef.2019.03.004

Cited by 27 publications

(20 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is related to the choice of gender who will lead the company [4]; [6]. In Tunisian banks, gender has an emotional bias on the credit risk of Tunisian bank management [7]. Gender equality created at the institutional level in Spain where the head of the company is a woman implies a positive influence on the financial performance of a company [8].…”

Section: 8the Effect Of Gender Diversity On Firm Riskmentioning

confidence: 99%

Gender Diversity Effect on Tax Avoidance and Firm Risk

Sofianty

Murwaningsari

Mulyani

2022

TSSJ

View full text Add to dashboard Cite

The purpose of this study was to examine the effect of gender diversity on firm risk with tax avoidance as a mediating variable in manufacturing companies listed on the Indonesia Stock Exchange (IDX). This study used SPSS version 20.0 to process the data. The sample of this research is 51 manufacturing companies listed on the IDX using multiple regression panel data. This study uses financial statement data for the 2015 – 2019 period. The findings of this study are (1) there is a negative effect of gender diversity on tax avoidance; (2) there is a negative effect of gender diversity on firm risk; (3) there is a positive effect of tax avoidance on firm risk; (4) Gender diversity has an influence on firm risk through tax avoidance. The limitations of this study are as follows: the research sample is only in manufacturing companies listed on the Indonesia Stock Exchange with a limited number of samples because during the observation period there are companies that are losing, suspending, and delisting. Therefore, this research suggests that (1) Further research can expand the scope of the research sample or compare it with companies in other industrial sectors. (2) Further research can increase the number of other variables, such as Corporate Social Responsibility by using the Blau-Index measurement (1975) so that the measurement can be more detail and constructive. (3) Further research can use other samples in Asean countries by comparing the success rate of anti-corruption disclosure in ASEAN countries. The practical implications include the following: (1) the role of gender diversity in the company is very necessary for implementing Good Corporate Governance (GCG) thus a healthy company will be created so that the company's risk does not occur in the future. (2) the role of the government is needed in making policies so that companies do not do tax evasion. The originality of the research includes this study, which is the first to analyze gender diversity on firm risk through tax avoidance.

show abstract

Section: 8the Effect Of Gender Diversity On Firm Riskmentioning

confidence: 99%

Gender Diversity Effect on Tax Avoidance and Firm Risk

Sofianty

Murwaningsari

Mulyani

2022

TSSJ

View full text Add to dashboard Cite

show abstract

“…Cash flow analysis and projections, as well as an understanding of the basic financial situation, are essential for monitoring the agreements that are often included in loan documentation (Eniola and Entebang, 2017). Financial knowledge is also important for micro-enterprises that are tempted to lend against personal assets, such as their home or property, as collateral, without fully understanding the risks involved (Bacha and Azouzi, 2019).…”

Section: Financial Literacy For Msmesmentioning

confidence: 99%

Unlock Financial Knowledge in Managers Through Games

Reuter

Dias²,

Sousa³

et al. 2022

ECGBL

View full text Add to dashboard Cite

Financial literacy for managers and owners of Micro, Small and Medium Enterprises (MSMEs) is defined as the combination of awareness, knowledge, skills, attitudes, and behaviours that a potential entrepreneur or business owner should have to make effective financial decisions to start a business, run a business, and ensure its sustainability and growth (OECD, 2020). To ensure this combination of financial knowledge and skills, different educational perspectives may emerge as the solution. The use of gamification and digital game-based learning (DGBL) are educational methodologies largely used in formal and informal environments, for different target audiences and different fields of knowledge, including finance. However, it is largely used by children and younger people in the same contexts described above. We find a gap that justifies further research on the formal use of DGBL in the adult population, namely for the development of financial literacy. For the use of gamification in this context to be effective is essential to identify which knowledge, skills and attitudes are required for MSMEs managers. The OECD (2020) questionnaire is an example of an instrument used to measure adult financial knowledge namely in managers, owners and future entrepreneurs of MSMEs. The financial literacy content for managers includes questions on basic concepts of asset management, inflation, investments, cash flows and risk analysis. However, at the top of our knowledge, we find little research to support how can we actively develop this awareness and knowledge through games. In this study, through exploratory analysis of government documents and literature, we sought to understand the three scales for measuring financial literacy, namely, financial knowledge, financial attitudes, and financial behaviour, which are the main contents that should be included in the pre-game’s assessment and on the learning objectives. The second concern is how to include these scales in the game to positively impact the behaviour, attitudes and knowledge of the managers of MSMEs. Thus, the contribution of the study is to create through the metrics used to measure financial literacy and the game mechanisms used a proposal for a model for designing digital games for financial literacy. Policymakers, private associations connected to MSMEs, and other players may take, for sure, lessons to be learned to prepare better managers to guarantee stable and sustainable enterprise support.

show abstract

“…Regarding gender bias, female individuals are having lower gender bias. It is worth noticing that findings in Bacha and Azouzi (2019) are based on Tunisian banks, and thus cannot be generalized.…”

Section: Gender Bias In Bankingmentioning

confidence: 99%

“…In addition, because this type of behavior is both socially and economically unacceptable, it is measured if female loan officers are having overconfidence bias. The results are presented in Bacha and Azouzi (2019). It is found that females are more conservative and risk‐averse than males.…”

Section: Related Workmentioning

confidence: 99%

A fair classifier chain for multi‐label bank marketing strategy classification

Radovanović

Petrović

Delibašić

et al. 2021

Int Trans Operational Res

View full text Add to dashboard Cite

Recently, the usage of machine learning algorithms is subject to discussion from a legal and ethical point of view. Unwanted discrimination regarding gender or race of a prediction model can lead to legal consequences. Therefore, during predictive model learning, one needs to be aware of possible bias and adjust the model to be fair. However, in bank marketing applications, one customer can receive multiple offers instead of just one. Because of their correlation between, a multi-label classification approach is the most suitable one. This paper proposes a fair classifier chain machine learning model for multi-label classification. Our algorithm solves the multi-label classification problem in an efficient manner, and it is suitable for real-life application employment. The proposed approach allows for controlling fairness constraints during the process of machine learning. It is based on the logistic regression model, thus enabling high efficiency and understandability. We apply our model to a real-life model from bank marketing campaign response prediction. The obtained results are promising. More specifically, our model achieves high fairness measures having an increase from 7% to 17%. However, fairness has a price of a decrease in predictive performance, up to 9% of AUC. To the best of our knowledge, this is the first algorithm that introduce fairness constraints in multi-label classification problems.

show abstract

How gender and emotions bias the credit decision-making in banking firms

Cited by 27 publications

References 45 publications

Gender Diversity Effect on Tax Avoidance and Firm Risk

Gender Diversity Effect on Tax Avoidance and Firm Risk

Unlock Financial Knowledge in Managers Through Games

A fair classifier chain for multi‐label bank marketing strategy classification

Contact Info

Product

Resources

About