An Explainable AI model to predict financial exclusion in Kenya

Abstract

Financial exclusion remains a significant barrier to economic development and social equity, particularly in emerging economies. This study employed an explainable machine learning framework to predict financial exclusion in Kenya using nationally representative survey data from 2016 and 2021. The research followed the Knowledge Discovery in Databases (KDD) process, incorporating robust feature engineering techniques to derive behavioral and demographic indicators from raw survey data. Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting were evaluated under two experimental scenarios: baseline training and synthetic minority oversampling (Synthetic Minority Oversampling Technique (SMOTE)) to address class imbalance. A temporal validation strategy was implemented by training models on the 2016 dataset and testing on 2021 data to assess generalizability over time. Feature selection using Random Forest importance and SelectFromModel was applied to reduce dimensionality, while model interpretability was achieved through SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) explainability techniques. The optimized Decision Tree model achieved the highest F1-Score (Harmonic mean of precision and recall) (F1) score of 0.926, followed closely by a soft-voting ensemble F1 of 0.906. Behavioral indicators particularly financial engagement, product category diversity, and digital finance adoption emerged as stronger predictors than demographic variables. The resulting framework not only predicted exclusion risk with high accuracy but also provided transparent, interpretable insights for policy design. The findings offered actionable recommendations to government agencies, Non-Governmental Organization (NGO)s, and financial institutions aiming to improve inclusive finance strategies in Kenya and similar socio-economic contexts.