IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v9y2021i11p192-d669198.html
   My bibliography  Save this article

Machine Learning (ML) Technologies for Digital Credit Scoring in Rural Finance: A Literature Review

Author

Listed:
  • Anil Kumar

    (Doctor of Business Administration (DBA), SP Jain School of Global Management, Mumbai 400070, Maharashtra, India)

  • Suneel Sharma

    (SkyzHotech Solutions Pvt. Ltd., Bengaluru 560102, India)

  • Mehregan Mahdavi

    (Sydney International School of Technology and Commerce (SISTC), Sydney, NSW 2000, Australia)

Abstract

Rural credit is one of the most critical inputs for farm production across the globe. Despite so many advances in digitalization in emerging and developing economies, still a large part of society like small farm holders, rural youth, and women farmers are untouched by the mainstream of banking transactions. Machine learning-based technology is giving a new hope to these individuals. However, it is the banking or non-banking institutions that decide how they will adopt this advanced technology, to have reduced human biases in loan decision making. Therefore, the scope of this study is to highlight the various AI-ML- based methods for credit scoring and their gaps currently in practice by banking or non-banking institutions. For this study, systematic literature review methods have been applied; existing research articles have been empirically reviewed with an attempt to identify and compare the best fit AI-ML-based model adopted by various financial institutions worldwide. The main purpose of this study is to present the various ML algorithms highlighted by earlier researchers that could be fit for a credit assessment of rural borrowers, particularly those who have no or inadequate loan history. However, it would be interesting to recognize further how the financial institutions could be able to blend the traditional and digital methods successfully without any ethical challenges.

Suggested Citation

  • Anil Kumar & Suneel Sharma & Mehregan Mahdavi, 2021. "Machine Learning (ML) Technologies for Digital Credit Scoring in Rural Finance: A Literature Review," Risks, MDPI, vol. 9(11), pages 1-15, October.
    • Handle: RePEc:gam:jrisks:v:9:y:2021:i:11:p:192-:d:669198
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/9/11/192/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-9091/9/11/192/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Onder Ozgur & Erdal Tanas Karagol & Fatih Cemil Ozbugday, 2021. "Machine learning approach to drivers of bank lending: evidence from an emerging economy," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-29, December.
    2. Saba Moradi & Farimah Mokhatab Rafiei, 2019. "A dynamic credit risk assessment model with data mining techniques: evidence from Iranian banks," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-27, December.
    3. Maisa Cardoso Aniceto & Flavio Barboza & Herbert Kimura, 2020. "Machine learning predictivity applied to consumer creditworthiness," Future Business Journal, Springer, vol. 6(1), pages 1-14, December.
    4. Shorouq Fathi Eletter & Saad Ghaleb Yaseen & Ghaleb Awad Elrefae, 2010. "Neuro-Based Artificial Intelligence Model for Loan Decisions," American Journal of Economics and Business Administration, Science Publications, vol. 2(1), pages 27-34, March.
    5. José Francisco Martínez Sánchez & Gilberto Pérez Lechuga, 2016. "Assessment of a credit scoring system for popular bank savings and credit," Contaduría y Administración, Accounting and Management, vol. 61(2), pages 391-417, Abril-Jun.
    6. Zeng, Yiwu & Jia, Fu & Wan, Li & Guo, Hongdong, 2017. "E-commerce in agri-food sector: a systematic literature review," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 20(4), February.
    7. José Américo Pereira Antunes, 2021. "To supervise or to self-supervise: a machine learning based comparison on credit supervision," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-21, December.
    8. Apostolos Ampountolas & Titus Nyarko Nde & Paresh Date & Corina Constantinescu, 2021. "A Machine Learning Approach for Micro-Credit Scoring," Risks, MDPI, vol. 9(3), pages 1-20, March.
    9. Lkhagvadorj Munkhdalai & Tsendsuren Munkhdalai & Oyun-Erdene Namsrai & Jong Yun Lee & Keun Ho Ryu, 2019. "An Empirical Comparison of Machine-Learning Methods on Bank Client Credit Assessments," Sustainability, MDPI, vol. 11(3), pages 1-23, January.
    10. Martin Leo & Suneel Sharma & K. Maddulety, 2019. "Machine Learning in Banking Risk Management: A Literature Review," Risks, MDPI, vol. 7(1), pages 1-22, March.
    11. Ta Nhat Linh & Hoang Thanh Long & Le Van Chi & Le Thanh Tam & Philippe Lebailly, 2019. "Access to Rural Credit Markets in Developing Countries, the Case of Vietnam: A Literature Review," Sustainability, MDPI, vol. 11(5), pages 1-18, March.
    12. Lean Yu & Xinxie Li & Ling Tang & Zongyi Zhang & Gang Kou, 2015. "Social credit: a comprehensive literature review," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 1(1), pages 1-18, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yangyang Zheng & Jianhong Lou & Linfeng Mei & Yushuang Lin, 2023. "Research on Digital Credit Behavior of Farmers’ Cooperatives—A Grounded Theory Analysis Based on the “6C” Family Model," Agriculture, MDPI, vol. 13(8), pages 1-19, August.
    2. Jiali Zhou & Xiangbo Fan & Chenggang Li & Guofei Shang, 2022. "Factors Influencing the Coupling of the Development of Rural Urbanization and Rural Finance: Evidence from Rural China," Land, MDPI, vol. 11(6), pages 1-21, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pejman Peykani & Mostafa Sargolzaei & Mohammad Hashem Botshekan & Camelia Oprean-Stan & Amir Takaloo, 2023. "Optimization of Asset and Liability Management of Banks with Minimum Possible Changes," Mathematics, MDPI, vol. 11(12), pages 1-24, June.
    2. Dmytro Kovalenko & Olga Afanasieva & Nani Zabuta & Tetiana Boiko & Rosen Rosenov Baltov, 2021. "Model of Assessing the Overdue Debts in a Commercial Bank Using Neuro-Fuzzy Technologies," JRFM, MDPI, vol. 14(5), pages 1-20, May.
    3. Pedro Guerra & Mauro Castelli & Nadine Côrte-Real, 2022. "Approaching European Supervisory Risk Assessment with SupTech: A Proposal of an Early Warning System," Risks, MDPI, vol. 10(4), pages 1-23, March.
    4. Paritosh Navinchandra Jha & Marco Cucculelli, 2021. "A New Model Averaging Approach in Predicting Credit Risk Default," Risks, MDPI, vol. 9(6), pages 1-15, June.
    5. Kuang-Hua Hu & Ming-Fu Hsu & Fu-Hsiang Chen & Mu-Ziyun Liu, 2021. "Identifying the key factors of subsidiary supervision and management using an innovative hybrid architecture in a big data environment," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-27, December.
    6. John R. J. Thompson & Longlong Feng & R. Mark Reesor & Chuck Grace, 2021. "Know Your Clients’ Behaviours: A Cluster Analysis of Financial Transactions," JRFM, MDPI, vol. 14(2), pages 1-29, January.
    7. Xin Xu & Feng Xiong & Zhe An, 2023. "Using Machine Learning to Predict Corporate Fraud: Evidence Based on the GONE Framework," Journal of Business Ethics, Springer, vol. 186(1), pages 137-158, August.
    8. Şirin Özlem & Omer Faruk Tan, 2022. "Predicting cash holdings using supervised machine learning algorithms," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-19, December.
    9. Filippi, Maryline & Chapdaniel, Alain, 2020. "Sustainable demand-supply chain: an innovative approach for improving sustainability in agrifood chains," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 24(2), March.
    10. Ivan Tikshaev & Roman Kulshin & Gennadii Volokitin & Pavel Senchenko & Anatoly Sidorov, 2022. "The Possibilities of Using Scoring to Determine the Relevance of Software Development Tenders," Mathematics, MDPI, vol. 10(24), pages 1-13, December.
    11. Wachenheim, Cheryl & Fan, Linfeng & Zheng, Shi, 2021. "Adoption of unmanned aerial vehicles for pesticide application: Role of social network, resource endowment, and perceptions," Technology in Society, Elsevier, vol. 64(C).
    12. Keerthana Sivamayil & Elakkiya Rajasekar & Belqasem Aljafari & Srete Nikolovski & Subramaniyaswamy Vairavasundaram & Indragandhi Vairavasundaram, 2023. "A Systematic Study on Reinforcement Learning Based Applications," Energies, MDPI, vol. 16(3), pages 1-23, February.
    13. Bauer, Kevin & Nofer, Michael & Abdel-Karim, Benjamin M. & Hinz, Oliver, 2022. "The effects of discontinuing machine learning decision support," SAFE Working Paper Series 370, Leibniz Institute for Financial Research SAFE.
    14. Gaofeng Guan & Dong Liu & Jiayang Zhai, 2022. "Factors Influencing Consumer Satisfaction of Fresh Produce E-Commerce in the Background of COVID-19—A Hybrid Approach Based on LDA-SEM-XGBoost," Sustainability, MDPI, vol. 14(24), pages 1-19, December.
    15. Gonzalo Villa‐Cox & Francesco Cavazza & Cristian Jordan & Mijail Arias‐Hidalgo & Paúl Herrera & Ramon Espinel & Davide Viaggi & Stijn Speelman, 2021. "Understanding constraints on private irrigation adoption decisions under uncertainty in data constrained settings: A novel empirical approach tested on Ecuadorian Cocoa cultivations," Agricultural Economics, International Association of Agricultural Economists, vol. 52(6), pages 985-999, November.
    16. Dario Schulz & Jan Börner, 2023. "Innovation context and technology traits explain heterogeneity across studies of agricultural technology adoption: A meta‐analysis," Journal of Agricultural Economics, Wiley Blackwell, vol. 74(2), pages 570-590, June.
    17. Maro Vlachopoulou & Christos Ziakis & Kostas Vergidis & Michael Madas, 2021. "Analyzing AgriFood-Tech e-Business Models," Sustainability, MDPI, vol. 13(10), pages 1-17, May.
    18. Vikranth Lokeshwar Dhandapani & Shashi Jain, 2024. "Neural Networks for Portfolio-Level Risk Management: Portfolio Compression, Static Hedging, Counterparty Credit Risk Exposures and Impact on Capital Requirement," Papers 2402.17941, arXiv.org.
    19. Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    20. Yufei Xia & Xinyi Guo & Yinguo Li & Lingyun He & Xueyuan Chen, 2022. "Deep learning meets decision trees: An application of a heterogeneous deep forest approach in credit scoring for online consumer lending," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1669-1690, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jrisks:v:9:y:2021:i:11:p:192-:d:669198. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.