Optimizing insurance time for claim resolution through machine learning

Abstract

As insurance plays a key role in financial protection, the ability to predict claim resolution time has become essential for improving operational efficiency and customer satisfaction. This study applied machine learning and explainable AI techniques to estimate the time taken to resolve insurance claims using a dataset from the Association of Kenya Insurers (AKI), comprising 13,000 records with 33 features. After preprocessing and feature engineering, several regression models were trained and evaluated, with XGBoost emerging as the best-performing model—achieving an R2 of 51.8%, outperforming Random Forest (45.7%) and Gradient Boosting (46.6%), and recording the lowest RMSE and MAE values. SHAP analysis highlighted key predictors, including PolicyUpgradesLastYear, PolicyStartYear, and PolicyDurationMonths. The final model was deployed via a Streamlit interface, offering a practical and interpretable solution for real-time prediction of claim resolution time, with potential to enhance decision-making and service delivery in the insurance sector.