International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 03 | Mar 2025
p-ISSN: 2395-0072
www.irjet.net
ENHANCING CREDIT RISK PREDICTION AND INCLUSION THROUGH MACHINE LEARNING IN MICROFINANCE: A REVIEW Namala Deekshitha ¹, Kareena Tunk ², Rathnavath Vyshnavi ³, Arjuman Subhani ⁴ 1Stanley College of engineering and technology for women, India 2Stanley College of engineering and technology for women, India
³Stanley College of engineering and technology for women, India ⁴Asst. Professor, Dept. of AI&DS and CME Engineering, Stanley College of engineering and technology for women, India -------------------------------------------------------------------------***----------------------------------------------------------------------
Abstract - By giving underprivileged groups access to credit and other financial products, microfinance institutions (MFIs) are
essential in the provision of financial services. Microfinance has changed as a result of the incorporation of machine learning (ML), deep learning (DL) which has improved financial decision-making, loan default prediction, and credit risk assessment. With an emphasis on enhancing credit scoring models, streamlining loan approval procedures, and reducing financial risks, this study investigates the use of diverse machine learning approaches in microfinance. MFIs can improve portfolio management, lower default rates, and advance financial inclusion by utilising predictive algorithms like decision trees, random forests, and neural networks. The study highlights upcoming innovation opportunities and gives a summary of recent developments in machine learning within the microfinance industry. Key Words: Microfinance Institutions (MFIs), Machine learning (ML), Deep learning (DL), Financial decision-making, Loan default prediction, Credit risk assessment, Decision trees, Random forests.
1.INTRODUCTION Microfinance institutions (MFIs) have been essential in helping small businesses and individuals without access to traditional banking systems by offering financial services. MFIs support financial inclusion, especially in underserved areas, by providing credit, savings, and insurance products. But historically, MFIs' growth and sustainability have been hampered by their particular set of problems, which include high default rates, operational inefficiencies, and a lack of information about borrowers. Machine learning (ML) has become a potent tool in recent years to tackle these issues. MFIs can better assess credit risk, anticipate loan defaults, process vast volumes of data efficiently, and make better decisions regarding loan approvals thanks to machine learning techniques. Machine learning can greatly improve the speed and accuracy of financial assessments in microfinance by utilising predictive models like decision trees, random forests, and neural networks. This will lower default rates and improve financial performance. This review of the literature looks at how machine learning methods are used in the microfinance industry. It highlights important studies that have used machine learning (ML) to predict loan performance, manage risk, and score credit. Along with highlighting trends, obstacles, and areas for further study, the review provides insights into how machine learning can keep revolutionising the microfinance sector. This paper attempts to give a thorough grasp of the relationship between microfinance and machine learning by synthesising recent research, demonstrating the potential of these technologies to enhance sustainability and financial inclusion. This review is organized as follows: Section 2, gives the overview of microfinance and machine learning. Section 3, provides the list of prominent research work that was done and different techniques that are employed. Section 4, describes some of the most prominent future research lines. Lastly, conclusions are provided in the Section 5.
2. OVERVIEW OF MICROFINANCE AND MACHINE LEARNING A subset of financial services known as microfinance provides small loans to low-income individuals who might not otherwise be able to obtain or qualify for traditional financing [1][2]. Economic development, financial inclusion, and poverty reduction have all been shown to be possible with microfinance [3][4] [5]. Many microfinance institutions (MFIs) have faced sustainability challenges despite their demonstrated potential, mainly as a result of rising loan default rates [6] [7]
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