Replication Data for: Alternative Datasets for Credit Scoring of Thin File Consumers

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Shukla, Deepa, 2024, "Replication Data for: Alternative Datasets for Credit Scoring of Thin File Consumers", https://doi.org/10.7910/DVN/TJ6RMQ, Harvard Dataverse, V1

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Description	Credit scoring is essential in financial services, allowing institutions to assess consumers' creditworthiness. Traditional credit scoring models heavily rely on extensive transaction history, which often poses a significant challenge for thin-file consumers—individuals with limited credit history. This comprehensive review aims to explore and evaluate various alternative datasets that can be utilised to improve credit scoring for thin-file consumers. By moving beyond traditional transaction profiles, alternative datasets such as social media data, web browsing behaviour, digital footprints, and telecom data offer new dimensions to assess consumer credit risk. Additionally, the review compares the effectiveness of various machine learning algorithms, including support vector machines, neural networks, decision trees, random forests, and hybrid models, in leveraging these datasets for credit scoring. The findings indicate that integrating multiple alternative data sources with advanced machine learning algorithms can significantly improve the accuracy and reliability of credit risk assessments. The comparative analysis of machine learning algorithms used in credit scoring highlights the strengths and limitations of different approaches. Support vector machines (SVM), neural networks, decision trees, random forests, and hybrid models have all shown varying degrees of success in utilising alternative datasets for credit scoring. Hybrid models combine multiple machine learning techniques and are particularly effective in leveraging diverse data sources to provide a robust credit risk assessment. This review underscores the potential of alternative datasets in revolutionizing credit scoring for thin-file consumers. By incorporating new data dimensions and advanced machine learning algorithms, researchers can improve their ability to assess credit risk accurately. Future researchers may continue to refine these models and explore new alternative datasets to enhance credit scoring models using machine learning algorithms. (2024-07-27)
Subject	Business and Management; Computer and Information Science
Keyword	Machine Learning Algorithms, Alternative Dataset, Credit Score, Behavioural Finance, Thin File
Related Publication	@misc{deepa_shukla_2024, title={Review of Alternative Datasets for Credit Scoring}, url={https://www.kaggle.com/dsv/9041453}, DOI={10.34740/KAGGLE/DSV/9041453}, publisher={Kaggle}, author={Deepa Shukla}, year={2024} }doi: 10.34740/KAGGLE/DSV/9041453
Notes	Dataset Overview - It contains information about various research papers related to alternative data sources for credit scoring. 1. Content of the Dataset: Authors and Year: Lists the authors of the papers and the year of publication. Title: Title of each research paper. Research Objectives: The goals or aims of the research described in each paper. Datasets Used: Types of datasets utilized in the research. Machine Learning Algorithms Employed: The algorithms used in the study. Key Findings: Major conclusions or results of the research. Citations: Links or references to the research papers. 2. Data Categories: The dataset includes a variety of alternative data categories used in credit scoring, such as social media data, web browsing behavior, digital footprints, telecom data, and hybrid data approaches. 3. Purpose of the Data: To categorize and assess the usage of different alternative datasets in credit scoring research for thin-file consumers. This data is used to analyze trends and prevalence of specific data sources in the context of credit scoring. 4. Visualization: The bar chart visualizes the distribution of these alternative data categories across the research papers. It helps to understand which data types are most frequently explored and their relevance in credit scoring models. 5. Analysis of Data: The dataset is organized to show the frequency of different categories of alternative data used in various studies. This analysis helps identify popular data sources and trends in research related to credit scoring. 6. Structure of Data: The data is structured into a table format with columns for serial number, authors and year, title, research objectives, datasets used, machine learning algorithms employed, key findings, and citations. 7. Potential Uses: This dataset can be used for literature reviews, identifying research gaps, and understanding the impact of various alternative datasets on credit scoring models. 8. Insights: The dataset provides insights into how different types of alternative data are used and evaluated in credit scoring research. It highlights the increasing interest in integrating diverse data sources to improve the accuracy and effectiveness of credit scoring models.
License/Data Use Agreement	CC0 1.0

	1 File
	Systematic Alternative Dataset Review.xlsx kaggle/input/review-of-alternative-datasets-for-credit-scoring/MS Excel Spreadsheet - 12.9 KB Published Jul 26, 2024 64 Downloads MD5: 9747d03475c95d6ef173f3414edb3e79 The findings indicate that social media data provides valuable insights into consumer behaviour and financial reliability, while web browsing patterns and digital footprints offer additional dimensions to assess creditworthiness. Telecom data, including call records and mobile payment history, has proven to be a reliable indicator of financial behaviour. Hybrid approaches, which combine these various data sources, provide a more comprehensive assessment of credit risk, addressing the limitations of relying on a single type of alternative dataset.	Access File File Access Public Download Options MS Excel Spreadsheet Download Metadata Data File Citation Download EndNote XML Download RIS Download BibTeX

Citation Metadata

Persistent Identifier	doi:10.7910/DVN/TJ6RMQ
Publication Date	2024-07-26
Title	Replication Data for: Alternative Datasets for Credit Scoring of Thin File Consumers
Subtitle	Non-Traditional Data Sources to Enhance Creditworthiness
Alternative Title	Innovative Data Approaches for Assessing Credit Risk in Limited Credit History Consumers
Alternative URL	https://www.kaggle.com/datasets/deepashukla/review-of-alternative-datasets-for-credit-scoring
Other Identifier	kaggle.com: /kaggle/input/review-of-alternative-datasets-for-credit-scoring
Author	Jaipur National University0000-0003-3016-1633
Point of Contact	Use email button above to contact. Gupta, Sunil (Jaipur National University)
Description	Credit scoring is essential in financial services, allowing institutions to assess consumers' creditworthiness. Traditional credit scoring models heavily rely on extensive transaction history, which often poses a significant challenge for thin-file consumers—individuals with limited credit history. This comprehensive review aims to explore and evaluate various alternative datasets that can be utilised to improve credit scoring for thin-file consumers. By moving beyond traditional transaction profiles, alternative datasets such as social media data, web browsing behaviour, digital footprints, and telecom data offer new dimensions to assess consumer credit risk. Additionally, the review compares the effectiveness of various machine learning algorithms, including support vector machines, neural networks, decision trees, random forests, and hybrid models, in leveraging these datasets for credit scoring. The findings indicate that integrating multiple alternative data sources with advanced machine learning algorithms can significantly improve the accuracy and reliability of credit risk assessments. The comparative analysis of machine learning algorithms used in credit scoring highlights the strengths and limitations of different approaches. Support vector machines (SVM), neural networks, decision trees, random forests, and hybrid models have all shown varying degrees of success in utilising alternative datasets for credit scoring. Hybrid models combine multiple machine learning techniques and are particularly effective in leveraging diverse data sources to provide a robust credit risk assessment. This review underscores the potential of alternative datasets in revolutionizing credit scoring for thin-file consumers. By incorporating new data dimensions and advanced machine learning algorithms, researchers can improve their ability to assess credit risk accurately. Future researchers may continue to refine these models and explore new alternative datasets to enhance credit scoring models using machine learning algorithms. (2024-07-27)
Subject	Business and Management; Computer and Information Science
Keyword	Machine Learning Algorithms (DBpedia) https://www.dbpedia.org Alternative Dataset (Investopedia) https://www.investopedia.com Credit Score (DBpedia) https://www.dbpedia.org Behavioural Finance (Wikipedia) https://www.wikipedia.org Thin File (Investopedia) https://www.investopedia.com
Topic Classification	Digital Credit Scoring (DBpedia) https://www.dbpedia.org
Related Publication	@misc{deepa_shukla_2024, title={Review of Alternative Datasets for Credit Scoring}, url={https://www.kaggle.com/dsv/9041453}, DOI={10.34740/KAGGLE/DSV/9041453}, publisher={Kaggle}, author={Deepa Shukla}, year={2024} } doi 10.34740/KAGGLE/DSV/9041453 https://www.kaggle.com/dsv/9041453 Smith, M., & Henderson, C. (2018). Beyond Thin Credit Files. Social Science Quarterly, 99, 24-42. doi 10.1111/SSQU.12389 https://doi.org/10.1111/SSQU.12389 Cheney, J. (2008). Alternative Data and its Use in Credit Scoring Thin- and No-File Consumers. Banking & Insurance. doi 10.2139/ssrn.1160283 https://doi.org/10.2139/ssrn.1160283 Rozo, B., Crook, J., & Andreeva, G. (2021). The Role of Web Browsing in Credit Risk Prediction. Econometrics: Econometric & Statistical Methods - Special Topics eJournal. doi 10.1016/j.dss.2022.113879 https://doi.org/10.1016/j.dss.2022.113879 Fu, G., Sun, M., & Xu, Q. (2020). An Alternative Credit Scoring System in China's Consumer Lending Market: A System Based on Digital Footprint Data. Decision-Making in Economics eJournal. doi 10.2139/ssrn.3638710 https://doi.org/10.2139/ssrn.3638710 Zhou, J., Wang, C., Ren, F., & Chen, G. (2021). Inferring multi-stage risk for online consumer credit services: An integrated scheme using data augmentation and model enhancement. Decis. Support Syst., 149, 113611. doi 10.1016/J.DSS.2021.113611 https://doi.org/10.1016/J.DSS.2021.113611 Djeundje, V., Crook, J., Calabrese, R., & Hamid, M. (2021). Enhancing credit scoring with alternative data. Expert Syst. Appl., 163, 113766. doi 10.1016/j.eswa.2020.113766 https://doi.org/10.1016/j.eswa.2020.113766 Sustersic, M., Mramor, D., & Zupan, J. (2007). Consumer Credit Scoring Models with Limited Data. Banking & Financial Institutions eJournal. doi 10.2139/ssrn.967384 https://doi.org/10.2139/ssrn.967384 Huang, C., Chen, M., & Wang, C. (2007). Credit scoring with a data mining approach based on support vector machines. Expert Syst. Appl., 33, 847-856. doi 10.1016/j.eswa.2006.07.007 https://doi.org/10.1016/j.eswa.2006.07.007 Jiang, J., Liao, L., Lu, X., Wang, Z., & Xiang, H. (2020). Deciphering Big Data in Consumer Credit Evaluation. International Political Economy: Investment & Finance eJournal. doi 10.2139/ssrn.3312163 https://doi.org/10.2139/ssrn.3312163 Dastile, X., Çelik, T., & Potsane, M. (2020). Statistical and machine learning models in credit scoring: A systematic literature survey. Appl. Soft Comput., 91, 106263. doi 10.1016/j.asoc.2020.106263 https://doi.org/10.1016/j.asoc.2020.106263 Bequé, A., & Lessmann, S. (2017). Extreme learning machines for credit scoring: An empirical evaluation. Expert Syst. Appl., 86, 42-53. doi 10.1016/j.eswa.2017.05.050 https://doi.org/10.1016/j.eswa.2017.05.050 Jiang, J., Liao, L., Lu, X., Wang, Z., & Xiang, H. (2021). Deciphering big data in consumer credit evaluation. Journal of Empirical Finance. doi 10.1016/J.JEMPFIN.2021.01.009 https://doi.org/10.1016/J.JEMPFIN.2021.01.009 He, H., Zhang, W., & Zhang, S. (2018). A novel ensemble method for credit scoring: Adaption of different imbalance ratios. Expert Syst. Appl., 98, 105-117. doi 10.1016/j.eswa.2018.01.012 https://doi.org/10.1016/j.eswa.2018.01.012 Munkhdalai, L., Munkhdalai, T., Namsrai, O., Lee, J., & Ryu, K. (2019). An Empirical Comparison of Machine-Learning Methods on Bank Client Credit Assessments. Sustainability. doi 10.3390/SU11030699 https://doi.org/10.3390/SU11030699 Aggarwal, N. (2018). Machine Learning, Big Data and the Regulation of Consumer Credit Markets: The Case of Algorithmic Credit Scoring. Discrimination. doi 10.2139/ssrn.3309244 https://doi.org/10.2139/ssrn.3309244 Zhu, B., Yang, W., Wang, H., & Yuan, Y. (2018). A hybrid deep learning model for consumer credit scoring. 2018 International Conference on Artificial Intelligence and Big Data (ICAIBD), 205-208. doi 10.1109/ICAIBD.2018.8396195 https://doi.org/10.1109/ICAIBD.2018.8396195 McCanless, M. (2023). Banking on alternative credit scores: Auditing the calculative infrastructure of U.S. consumer lending. Environment and Planning A: Economy and Space, 55, 2128 - 2146. doi 10.1177/0308518X231174026 https://doi.org/10.1177/0308518X231174026 Wiginton, J. (1980). A Note on the Comparison of Logit and Discriminant Models of Consumer Credit Behavior. Journal of Financial and Quantitative Analysis, 15, 757 - 770. doi 10.2307/2330408 https://doi.org/10.2307/2330408 Ala’raj, M., Abbod, M., & Majdalawieh, M. (2021). Modelling customers credit card behaviour using bidirectional LSTM neural networks. Journal of Big Data, 8, 1-27. doi 10.1186/s40537-021-00461-7 https://doi.org/10.1186/s40537-021-00461-7 Saberi, M., Mirtalaei, M., Hussain, F., Azadeh, A., Hussain, O., & Ashjari, B. (2013). A granular computing-based approach to credit scoring modeling. Neurocomputing, 122, 100-115. doi 10.1016/j.neucom.2013.05.020 https://doi.org/10.1016/j.neucom.2013.05.020 Wei, Y., Yildirim, P., Bulte, C., & Dellarocas, C. (2014). Credit Scoring with Social Network Data. Economics of Networks eJournal. doi 10.2139/ssrn.2475265 https://doi.org/10.2139/ssrn.2475265 Wang, C., Han, D., Liu, Q., & Luo, S. (2019). A Deep Learning Approach for Credit Scoring of Peer-to-Peer Lending Using Attention Mechanism LSTM. IEEE Access, 7, 2161-2168. doi 10.1109/ACCESS.2018.2887138 https://doi.org/10.1109/ACCESS.2018.2887138 West, D. (2000). Neural network credit scoring models. Comput. Oper. Res., 27, 1131-1152. doi 10.1016/S0305-0548(99)00149-5 https://doi.org/10.1016/S0305-0548(99)00149-5 Brown, I., & Mues, C. (2012). An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Expert Syst. Appl., 39, 3446-3453. doi 10.1016/j.eswa.2011.09.033 https://doi.org/10.1016/j.eswa.2011.09.033 Lee, T., & Chen, I. (2005). A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines. Expert Syst. Appl., 28, 743-752. doi 10.1016/j.eswa.2004.12.031 https://doi.org/10.1016/j.eswa.2004.12.031 Mahjoub, R., & Afsar, A. (2019). A hybrid model for customer credit scoring in stock brokerages using data mining approach. Int. J. Bus. Inf. Syst., 31, 195-214. doi https://doi.org/10.1504/IJBIS.2019.10022044 Arram, A., Ayob, M., Albadr, M., Sulaiman, A., & Albashish, D. (2023). Credit card score prediction using machine learning models: A new dataset. ArXiv, abs/2310.02956. 10.48550/arXiv.2310.02956 https://doi.org/10.48550/arXiv.2310.02956 Junior, L., Nardini, F., Renso, C., Trani, R., & Macêdo, J. (2020). A novel approach to define the local region of dynamic selection techniques in imbalanced credit scoring problems. Expert Syst. Appl., 152, 113351. doi 10.1016/j.eswa.2020.113351 https://doi.org/10.1016/j.eswa.2020.113351
Notes	Dataset Overview - It contains information about various research papers related to alternative data sources for credit scoring. 1. Content of the Dataset: Authors and Year: Lists the authors of the papers and the year of publication. Title: Title of each research paper. Research Objectives: The goals or aims of the research described in each paper. Datasets Used: Types of datasets utilized in the research. Machine Learning Algorithms Employed: The algorithms used in the study. Key Findings: Major conclusions or results of the research. Citations: Links or references to the research papers. 2. Data Categories: The dataset includes a variety of alternative data categories used in credit scoring, such as social media data, web browsing behavior, digital footprints, telecom data, and hybrid data approaches. 3. Purpose of the Data: To categorize and assess the usage of different alternative datasets in credit scoring research for thin-file consumers. This data is used to analyze trends and prevalence of specific data sources in the context of credit scoring. 4. Visualization: The bar chart visualizes the distribution of these alternative data categories across the research papers. It helps to understand which data types are most frequently explored and their relevance in credit scoring models. 5. Analysis of Data: The dataset is organized to show the frequency of different categories of alternative data used in various studies. This analysis helps identify popular data sources and trends in research related to credit scoring. 6. Structure of Data: The data is structured into a table format with columns for serial number, authors and year, title, research objectives, datasets used, machine learning algorithms employed, key findings, and citations. 7. Potential Uses: This dataset can be used for literature reviews, identifying research gaps, and understanding the impact of various alternative datasets on credit scoring models. 8. Insights: The dataset provides insights into how different types of alternative data are used and evaluated in credit scoring research. It highlights the increasing interest in integrating diverse data sources to improve the accuracy and effectiveness of credit scoring models.
Language	English
Producer	Sunil Gupta (Jaipur National Uniersity) (JNU) https://www.jnujaipur.ac.in
Production Date	2024-07-27
Production Location	INIDIA
Contributor	Researcher: Shukla, Deepa Supervisor: Gupta, Sunil
Depositor	Shukla, Deepa
Deposit Date	2024-07-27
Time Period	Start Date: 2024-07-26; End Date: 2024-07-27
Date of Collection	Start Date: 2024-02-18; End Date: 2024-07-20
Data Type	https://github.com/Deezpa/Datasets_Review
Software	Python, Version: 3
Related Material	https://github.com/Deezpa/Datasets_Review
Data Source	Research Papers

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