Modeling SLA Breach Probability for Critical Fintech Data

SLA requirements are one of the mainstays of fintech contracts. The entire premise depends upon data being accurate, always available, and processed with speed, impacting customer satisfaction and regulatory rules. This research investigates the use of predictive models to estimate the probability of SLA breaches in digital wallet transactions using machine learning. It considers customer behavior and transaction details. The dataset consists of 7,000 customer records with 20 features and was tested with four different classification methods: Logistic Regression, Random Forest, KNearest Neighbors, and Neural Networks. Random Forest produced an accuracy of 100%, while Neural Networks reached 98.79%, well beyond conventional statistical methods. Linking the metrics from customer lifetime value with the performance of SLA to establish a quantitative risk assessment framework and testing predictive models across several fintech settings, important predictors of SLA breach risk include how often the transactions occur, how engaged customers are, and patterns in support interactions; thus, helping to plan resources and improving service quality.