A logistic regression model for consumer default risk

Silva, Eliana Costa e; Lopes, Isabel Cristina; Correia, Aldina; Faria, Susana

doi:10.1080/02664763.2020.1759030

Cited by 32 publications

(24 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We used logistic regression to test our hypotheses and estimate the influence of external and internal R&D, external knowledge and website on technological innovation. Logistic regression was used due to the dummy variables used in the study (Costa e Silva et al, 2020). The results (β=0 .8464884, p=0.000) support hypothesis 2 that, there is positive and statistically significant impact of external knowledge and on technological innovation of firms.…”

Section: Data Analysis and Resultsmentioning

confidence: 71%

Managing Knowledge Towards Firm Performance: The Moderation Role of the Business Environment

Arthur¹

2022

ECKM

View full text Add to dashboard Cite

The prominence of the knowledge economy technological innovation and economic development globally cannot be questioned. However, not all firms especially from Central and Eastern European to tap into the benefits coupled with new knowledge for improvement in firm innovation performance. Mostly, this is due to environmental factors confronting the firms. This study focused on the impact of External Knowledge (EK), R&D expenditure and ICT usage on firm innovation performance with the moderation role of informal competition. We used Resource Dependency Theory and Contingency theory as the theoretical background. A cross sectional data from the World Bank Enterprise Survey (WBES) 2019 on 3105 firms from CEE regions namely Czech Republic, Hungary, Slovakia, and Poland was analysed using logistic regression. These countries were selected because firms face elevated level of informal competition in the business environment. The study confirmed the hypotheses that, internal R&D, external knowledge and website availability and usage have positive and significant direct effect on technological innovation. Our study contributes to the literature of R&D and firm performance by highlighting the moderating effect of business environment (informal competition).

show abstract

Section: Data Analysis and Resultsmentioning

confidence: 71%

Managing Knowledge Towards Firm Performance: The Moderation Role of the Business Environment

Arthur¹

2022

ECKM

View full text Add to dashboard Cite

show abstract

“…They helped humans to make faster and more accurate decisions. With the increase of available information and computation power, these statistical models have been gradually substituted with Machine Learning (ML) models [2,3]. In this respect, vast literature focused on exploiting machine learning models in the financial credit market [4,5].…”

Section: Related Workmentioning

confidence: 99%

A General Architecture for a Trustworthy Creditworthiness-Assessment Platform in the Financial Domain

Cornacchia¹,

Anelli²,

Narducci³

et al. 2023

AETiC

View full text Add to dashboard Cite

The financial domain is making huge advancements thanks to the exploitation of artificial intelligence. As an example, the credit-worthiness-assessment task is now strongly based on Machine Learning algorithms that make decisions independently from humans. Several studies showed remarkable improvement in reliability, customer care, and return on investment. Nonetheless, many users remain sceptical since they perceive the whole as only partially transparent. The trust in the system decision, the guarantee of fairness in the decision-making process, the explanation of the reasons behind the decision are just some of the open challenges for this task. Moreover, from the financial institution's perspective, another compelling problem is credit-repayment monitoring. Even here, traditional models (e.g., credit scorecards) and machine learning models can help the financial institution in identifying, at an early stage, customers that will fall into default on payments. The monitoring task is critical for the debt-repayment success of identifying bad debtors or simply users who are momentarily in difficulty. The financial institution can thus prevent possible defaults and, if possible, meet the debtor's needs. In this work, the authors propose an architecture for a Creditworthiness-Assessment duty that can meet the transparency needs of the customers while monitoring the credit-repayment risk. This preliminary study carried out an experimental evaluation of the component devoted to the credit-score computation and monitoring credit repayments. The study shows that the authors’ architecture can be an effective tool to improve current Credit-scoring systems. Combining a static and a subsequent dynamic approach can correct mistakes made in the first phase and foil possible false positives for good creditors.

show abstract

“…Logistic regression (LR) can predict the credit risk of the small and medium-sized enterprises for financial institutions (Zhu et al, 2016) and consumer default risk (Costa e Silva et al, 2020). Naive Bayes (NB) is a classification method based on Bayes theorem and independent assumption of feature conditions (Chen et al, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

An Enhanced TSA-MLP Model for Identifying Credit Default Problems

et al. 2022

View full text Add to dashboard Cite

Credit default has always been one of the critical factors in the development of personal credit business. By establishing a default identification model, default can be avoided effectively. There are some existing methods to identify credit default. However, these methods have some problems: Problem (1): It is different to deal the non-linear data, Problem (2): The local stagnation results in the high error rate, and Problem (3): The premature convergence leads to the low classification rate. In this paper, the sinhTSA-MLP default risk identification model is proposed to solve these problems. In this model, the proposed sinhTSA method can effectively avoid the problems of falling into local optimum and premature convergence. And the benchmark test results demonstrate sinhTSA is superior to other methods. According to the two experiments, the classification rate reaches 77.35% and 96.48%. Therefore, the sinhTSA-MLP default identification model has some particular advantages in identifying credit problems The feasibility of the sinhTSA-MLP default identification model has been proved through helping to manage credit default more consciously.

show abstract

A logistic regression model for consumer default risk

Cited by 32 publications

References 11 publications

Managing Knowledge Towards Firm Performance: The Moderation Role of the Business Environment

Managing Knowledge Towards Firm Performance: The Moderation Role of the Business Environment

A General Architecture for a Trustworthy Creditworthiness-Assessment Platform in the Financial Domain

An Enhanced TSA-MLP Model for Identifying Credit Default Problems

Contact Info

Product

Resources

About