Machine Learning-Based Predictive Analytics for Early Risk Detection Across Healthcare, Financial Services, And Retail Markets

Abstract

Organizations across healthcare, financial services, and retail markets generate extensive data but often struggle to transform it into timely, reliable, and actionable risk intelligence. This study examined how machine learning-based predictive analytics supports early risk detection and determined the technological and organizational factors influencing its effectiveness. A quantitative, cross-sectional, case-based research design was adopted, covering organizational cases from healthcare, financial services, and retail enterprises. Using purposive sampling, data were collected through a structured five-point Likert-scale questionnaire from 286 professionals, including 99 healthcare, 95 financial services, and 92 retail respondents, representing an 86.67% valid response rate. The principal variables were machine learning capability, predictive analytics adoption, data quality, organizational readiness, decision-support effectiveness, and early risk detection effectiveness. Data were analyzed using descriptive statistics, Cronbach’s alpha, Pearson correlation, multiple regression, and one-way ANOVA. The overall instrument demonstrated high reliability with a Cronbach’s alpha of 0.91. Data quality recorded the highest mean score at 4.12, followed by early risk detection effectiveness at 4.08, machine learning capability at 4.05, decision-support effectiveness at 4.01, predictive analytics adoption at 3.98, and organizational readiness at 3.89. All predictors were positively correlated with early risk detection, with data quality producing the strongest relationship, r = 0.68, p < 0.01. The regression model was statistically significant, F = 92.84, p < 0.001, and explained 62.4% of the variance in early risk detection effectiveness. Data quality was the strongest predictor, β = 0.29, followed by decision-support effectiveness, β = 0.25, machine learning capability, β = 0.21, predictive analytics adoption, β = 0.18, and organizational readiness, β = 0.14. Financial services achieved the highest readiness index at 4.09, compared with healthcare at 4.02 and retail at 3.94, while sectoral differences were significant, F (2, 283) = 5.74, p = 0.004. The findings imply that enterprises should combine reliable data, capable models, skilled personnel, supportive governance, and actionable decision systems to strengthen preventive risk management across sectors.