A Credit Scoring Model for SMEs Based on Social Media Data

Putra, Septian Gilang Permana; Joshi, Bikash; Redi, Judith; Bozzon, Alessandro

doi:10.1007/978-3-030-50578-3_9

Cited by 6 publications

(4 citation statements)

References 24 publications

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“…Previous studies have verified the significance of social network data; however, research on behavioral data is limited (Chen and Chen, 2022; Niu et al. , 2019; Putra et al. , 2020).…”

Section: Discussion Implications and Limitationsmentioning

confidence: 98%

“…First, it contributes to the non-financial factor (soft information) related to corporate credit by distinguishing between specific types of social activities: questions, posts and comments. Previous studies have verified the significance of social network data; however, research on behavioral data is limited (Chen and Chen, 2022;Niu et al, 2019;Putra et al, 2020). Although digital platforms and online communities enabled corporate social activities and social behaviors have been widely explored (Zhang et al, 2020b;Park and Kim, 2023;Li and Chen, 2022), most existing research does not verify the relationships between corporate social activities (i.e.…”

Section: Theoretical Implicationsmentioning

confidence: 99%

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How social activities affect corporate credit behavior? – the mediating role of network centrality

Chen

2023

IMDS

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PurposeSocial information is crucial to credit ratings and can improve the accuracy of the traditional credit assessment model. Drawing on the resource-based view (RBV) and social capital theory (SCT), this research explores the relationships between corporate social activities, network centrality and corporate credit behavior.Design/methodology/approachThe authors used social network analysis (SNA) and regression analysis to analyze the data collected from 14,544 enterprises on the Alibaba platform.FindingsThe results indicate that among the four types of social activities, the number of corporate questions and posts shows a positive relationship with credit behavior; while the number of corporate comments has negative relationship with credit behavior. Further, degree and betweenness centralities mediate the relationship between the number of corporate questions, posts and comments with credit behavior.Originality/valueThis study contributes to the literature on non-financial factors (soft information) by exploring the social behavioral factors related to corporate credit. In addition, this study offers a new theoretical lens and reasonable explanations for investigating the relationship between corporate social activities, network centrality and credit behavior from the perspective of the resource-based view, while most studies are predictive and methodological. Moreover, this study provides new insights for platforms to evaluate enterprise credit and for managers to improve credit behavior.

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“…Previous studies have verified the significance of social network data; however, research on behavioral data is limited (Chen and Chen, 2022; Niu et al. , 2019; Putra et al. , 2020).…”

Section: Discussion Implications and Limitationsmentioning

confidence: 98%

Section: Theoretical Implicationsmentioning

confidence: 99%

How social activities affect corporate credit behavior? – the mediating role of network centrality

Chen

2023

IMDS

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“…Explore Target Variable: Investigate the distribution of the target variable "Credit_Score" by printing the value counts (Kuppili et al, 2020). Evaluate Model Performance: Optionally, Evaluate the model using the testing data and metrics like accuracy, precision, recall, and F1-score (Putra et al, 2020).…”

Section: Check For Missing Valuesmentioning

confidence: 99%

Financial Oracle: Unlocking Credit Scores

Srinivas,

Bharathi

2024

JoITML

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This paper provides individuals with a reliable tool for predicting their credit scores based on various financial attributes. Leveraging ML models, specifically a Random Forest classifier, our system analyzes features such as yearly income, salary, bank accounts, credit card usage, loan history, and payment behavior. Users can input their financial information, and the system generates a predicted credit score. This predictive tool empowers individuals to gain insights into their creditworthiness, enabling better financial planning and decision-making. By offering a user-friendly interface and accurate predictions, our project seeks to assist users in understanding the factors influencing their credit scores and ultimately improving their financial well-being. With the increasing importance of credit scores in financial transactions and lending decisions, our tool serves as a valuable resource for individuals seeking to manage their finances more effectively.

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“…However, the research lines to increase the performance of these models are the same as in application scoring. Putra et al (2020) investigated the value of social network data to predict bankruptcy, and Letizia and Lillo (2019) included a corporate payments network to assess an internal rating. Our previous research (Muñoz-Cancino et al, 2021) shows that social network data generate a much more significant performance enhancement in application scoring than behavioral scoring, considering the same population and features.…”

Section: Introductionmentioning

confidence: 99%

On the dynamics of credit history and social interaction features, and their impact on creditworthiness assessment performance

Muñoz-Cancino¹,

Bravo²,

Ríos³

et al. 2022

Preprint

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For more than a half-century, credit risk management has used credit scoring models in each of its well-defined stages to manage credit risk. Application scoring is used to decide whether to grant a credit or not, while behavioral scoring is used mainly for portfolio management and to take preventive actions in case of default signals. In both cases, network data has recently been shown to be valuable to increase the predictive power of these models, especially when the borrower's historical data is scarce or not available. This study aims to understand the creditworthiness assessment performance dynamics and how it is influenced by the credit history, repayment behavior, and social network features. To accomplish this, we introduced a machine learning classification framework to analyze 97.000 individuals and companies from the moment they obtained their first loan to 12 months afterward. Our novel and massive dataset allow us to characterize each borrower according to their credit behavior, and social and economic relationships. Our research shows that borrowers' history increases performance at a decreasing rate during the first six months and then stabilizes. The most notable effect on perfomance of social networks features occurs at loan application; in personal scoring, this effect prevails a few months, while in business scoring adds value throughout the study period. These findings are of great value to improve credit risk management and optimize the use of traditional information and alternative data sources.

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A Credit Scoring Model for SMEs Based on Social Media Data

Cited by 6 publications

References 24 publications

How social activities affect corporate credit behavior? – the mediating role of network centrality

How social activities affect corporate credit behavior? – the mediating role of network centrality

Financial Oracle: Unlocking Credit Scores

On the dynamics of credit history and social interaction features, and their impact on creditworthiness assessment performance

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