Can a mobile credit-scoring model provide better accessibility to South African citizens requiring micro-lending

Hendricks, Matthew Kenneth; Budree, Adheesh

doi:10.1504/ijef.2019.10020399

Cited by 2 publications

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“…The conventional technique of credit scoring relied on statistical analysis and the common judgment method. However, the novel techniques are centered on utilizing social media data, mobile data, and psychometrics (Hendricks & Budree, 2019). Classical credit reporting, which is primarily focused on financial data, may only provide such good assessment of SMEs that are restricted in financial data but flourishing in non-financial data, like big data from business, government, social media, and networks (Yadi et al, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Credit Scoring Active Telegram Channels Offering Stock Signals

Sohrabi

Jafarabad

Orfi

2022

IJFIFSA

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The impact of personal judgment on the assessment of an individual's financial situation has been drastically reduced through the development of credit scoring. The systems are capable of deciding based on an applicant's total score which is a combination of several factors and indicators. Over the past few decades, credit scoring has been considered an essential tool for evaluation in various institutions and has also been able to transform the industry as aThe methodology used is "CRISP-DM," which consists of six steps. The main variables include social and financial variables that are examined for six months. In the research, we seek to identify, analyze and categorize active telegram channels in stock signals using the data mining model and the RFM method. The k-means algorithm is selected for this category. Then, in each cluster, the importance of social variables and the performance of the channels are extracted by the EXTRATREECLASSIFIER algorithm, and channel performance is measured by considering the changes in the total index.

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Section: Literature Reviewmentioning

confidence: 99%

Credit Scoring Active Telegram Channels Offering Stock Signals

Sohrabi

Jafarabad

Orfi

2022

IJFIFSA

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“…The data regarding the interest of individual in social networking sites have been implemented to predict the credit worthiness of borrowers (Cnudde et al , 2019). Hendricks and Budree (2019) proposed a conceptual model with variables like social media, mobile credit and psychometric to predict the default behaviour of borrowers. Similarly, Agarwal et al (2020) built a credit scoring model by using certain digital information of individuals and stated that such data has better predictive power than the traditional data sets used by banks.…”

Section: Theoretical Background and Hypothesis Developmentmentioning

confidence: 99%

Credit scoring of small and medium enterprises: a behavioural approach

Goel

Rastogi

2021

JEEE

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Purpose This study aims to formulate a behavioural credit scoring models for Indian small and medium enterprises (SME) entrepreneurs using certain behavioural and psychological constructs. Two separate models are built which can predict the credit default and wilful default of the borrowers, respectively. This research was undertaken to understand whether certain psychological and behavioural factors can significantly predict the borrowers’ credit and wilful default. Design/methodology/approach A questionnaire survey was undertaken by SME entrepreneurs of two Indian states, i.e. Uttar Pradesh and Maharashtra. The questionnaire had two dependent variables: wilful default and credit default and nine independent variables. The questionnaire reliability and validity were ensured through confirmatory factor analysis (CFA) and further a model was built using logistic regression. Findings The results of this study have shown that certain behavioural and psychological traits of the borrowers can significantly predict borrowers’ default. These variables can be used to predict the overall creditworthiness of SME borrowers. Practical implications The findings of this research indicate that using behavioural and psychological constructs, lending institutions can easily evaluate the credit worthiness of those borrowers, who do not have any financial and credit history. This will enhance the capability of financial institutions to evaluate opaque SME borrowers. Originality/value There are very few numbers of studies which have considered predicting the credit default using certain psychological variables, but with respect to Asian market, and especially India, there does not exist a single significant study which has tried to fulfil such research gap. Also, this is the first study that has explored whether certain psychological factors can predict the wilful default of the borrowers. This is one of the most significant contributions of this research.

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Can a mobile credit-scoring model provide better accessibility to South African citizens requiring micro-lending

Cited by 2 publications

References 0 publications

Credit Scoring Active Telegram Channels Offering Stock Signals

Credit Scoring Active Telegram Channels Offering Stock Signals

Credit scoring of small and medium enterprises: a behavioural approach

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