Machine Learning–based Prediction of SLA Violations in Fintech

A growing pressure in fintech pushes for continuous services as digital payment systems get more complex. Using rich transaction patterns and detailed data on how customers interact, this study estimates the chance of SLA violations in digital wallets with simple classification methods. Logistic Regression, Random Forest, K-Nearest Neighbors, and Deep Neural Networks are used to assess 7,000 customer accounts across 25 behavioral and performance variables. The results show that ensemble methods and deep learning perform best at classifying outcomes: Random Forest and Neural Networks achieve an accuracy of 98.79%, hence a perfect classification. By tying customer value metrics to performance measures, the research provides a clear decision framework that provides early warnings of possible service problems: transaction frequency, customer interactions, and support history.