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

Anil Kumar,Suneel Sharma,Mehregan Mahdavi

doi:10.3390/risks9110192

Anil Kumar, Suneel Sharma + Show 1 more

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https://doi.org/10.3390/risks9110192

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Journal: Risks	Publication Date: Oct 31, 2021
Citations: 19	License type: CC BY 4.0

Affiliation: University of Technology Sydney

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.

Highlights

Credit penetration among the farming community is gaining attention because of the development of rural markets, recognizing it as a growth engine for an emerging economy like India
It is recommended that XGBoost, being an integrated machine learning (ML) method based on the gradient boosting decision tree (GBDT), is highly useful for the study on having a dichotomy problem
We have identified four parameters that define the strength of the ML-based model for credit scoring

Summary

Introduction

Credit penetration among the farming community is gaining attention because of the development of rural markets, recognizing it as a growth engine for an emerging economy like India. Banking accessibility for the poorer households, smallholder farmers, and individual women farmers is still by far a concern and needs to be addressed. These are a few defined groups of people who are probably still unserved or underserved by the existing banking or financial institutions. The requirement of digital channels for credit scoring to know the creditworthiness of the rural borrowers is extremely crucial. Most of the financial institutions developed their credit scoring approach based on the customer’s historical data, previous borrowing habits, and so forth. In the absence of any historical data for bank finance, these kinds of customers in particular or any new banking customers have to face all the difficulties to get credit from any of the formal banking institutions. For them artificial intelligence (AI) and machine learning (ML)-based technologies may provide a big help to assess their credit score as it provides a comprehensive profile of the borrower’s current level of income, his or her employment opportunities, and their potential ability to repay their intended loan

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