Legal and Regulatory Challenges to MSME Insurance Inclusion: The Role of AI-Driven Predictive Solutions

Abstract

This research builds predictive analytics models with the help of Artificial Intelligence (AI) support to improve insurance claims management for Micro, Small, and Medium Enterprises (MSMEs). Quantitative research was conducted on the basis of data gathered from 385 MSMEs in different industries. Methods utilized are Negative Binomial Regression for the analysis of claim frequency, Random Forest Regressor to predict claim settlement time, Naive Bayes Classifier to predict fraud risk, and Multiple Linear Regression for willingness to use AI tools analysis. Even a decision-support model using Random Forest and Gradient Boosting was formulated to minimize processing time and detect fraud. Major findings indicate that while MSME profile variables are relatively poor statistical predictors of claim frequency, claim amount and turnover are good predictors of settlement duration. AI awareness affects intention to adopt strongly. While modestly predictive, the ensemble model provides useful insights to insurers keen on automating claims and fraud detection. The research showcases the AI transformative potential in MSME insurance business processes.