INNOVATIVE MACHINE LEARNING APPROACHES TO FOSTER FINANCIAL INCLUSION IN MICROFINANCE

This study examines the application of machine learning algorithms to enhance financial inclusion in microfinance, focusing on credit scoring, risk and fraud detection, and customer segmentation. We performed feature engineering and employed models such as Logistic Regression, Decision Trees, Random Forests, Gradient Boosting Machines (XGBoost and LightGBM), Support Vector Machines (SVM), Autoencoders, Isolation Forests, and K-means Clustering. LightGBM achieved the highest accuracy (89.6%) and AUC (0.92) in credit scoring, while Random Forests demonstrated strong performance in both loan approval (86.7% accuracy) and fraud detection (87.6% accuracy, AUC of 0.88). SVM also performed competitively, and unsupervised methods like Autoencoders and Isolation Forests showed potential for anomaly detection but required further refinement.K-means Clustering excelled in customer segmentation with a silhouette score of 0.72, enabling tailored services based on client demographics. Our findings highlight the significant impact of machine learning on improving credit scoring accuracy, reducing fraud risks, and enhancing customer service delivery in microfinance, thereby promoting financial inclusion for underserved populations. Ethical considerations and model interpretability are crucial, particularly for smaller institutions. This study advocates for the broader adoption of machine learning in the microfinance sector.

本研究探讨机器学习算法在提升小额信贷（microfinance）领域金融普惠（financial inclusion）中的应用，重点聚焦于信用评分、风险与欺诈检测以及客户细分三大核心方向。我们实施了特征工程工作，并采用了逻辑回归（Logistic Regression）、决策树（Decision Trees）、随机森林（Random Forests）、梯度提升机（Gradient Boosting Machines，涵盖XGBoost与LightGBM）、支持向量机（Support Vector Machines，简称SVM）、自编码器（Autoencoders）、孤立森林（Isolation Forests）以及K-means聚类（K-means Clustering）等多种机器学习模型。其中LightGBM在信用评分任务中取得了最高的准确率（89.6%）与受试者工作特征曲线下面积（Area Under Curve，简称AUC）值0.92；随机森林则在贷款审批（准确率86.7%）与欺诈检测（准确率87.6%，AUC值0.88）两项任务中均表现优异。支持向量机的表现同样具备竞争力，而自编码器、孤立森林等无监督学习方法在异常检测领域展现出应用潜力，但仍需进一步优化。K-means聚类在客户细分任务中表现出色，轮廓系数（silhouette score）达0.72，可基于客户人口统计特征提供定制化服务。本研究结果表明，机器学习可显著提升小额信贷领域的信用评分准确性、降低欺诈风险并优化客户服务交付，从而为受金融排斥的服务不足群体（underserved populations）推广金融普惠服务。此外，伦理考量与模型可解释性至关重要，尤其对于小型金融机构而言。本研究倡导在小额信贷行业更广泛地应用机器学习技术。