Abstract: A central server receives API calls requesting services. The central server identifies whether the API calls are associated with a merchant. A distribution is constructed based on the API calls. The central server further executes a pre-defined rule to identify a set of the API calls belonging to a maximum percentile in the distribution and a set of the API calls belonging to a minimum percentile in the distribution before estimating a set of the anomalous data points with one or more goodness of fit functions against the maximum percentile and the minimum percentile. A GUI receives a critical value from a user. In response to receiving the critical value, the central server generates probabilities of the set of the anomalous data points before displaying a set of the anomalous data points in response to the probabilities being less than the critical value.

Abstract: A central server receives API calls requesting services. The central server identifies whether the API calls are associated with a merchant. A distribution is constructed based on the API calls. The central server further executes a pre-defined rule to identify a set of the API calls belonging to a maximum percentile in the distribution and a set of the API calls belonging to a minimum percentile in the distribution before estimating a set of the anomalous data points with one or more goodness of fit functions against the maximum percentile and the minimum percentile. A GUI receives a critical value from a user. In response to receiving the critical value, the central server generates probabilities of the set of the anomalous data points before displaying a set of the anomalous data points in response to the probabilities being less than the critical value.

Abstract: A central server receives API calls requesting services. The central server identifies whether the API calls are associated with a merchant. A distribution is constructed based on the API calls. The central server further executes a pre-defined rule to identify a set of the API calls belonging to a maximum percentile in the distribution and a set of the API calls belonging to a minimum percentile in the distribution before estimating a set of the anomalous data points with one or more goodness of fit functions against the maximum percentile and the minimum percentile. A GUI receives a critical value from a user. In response to receiving the critical value, the central server generates probabilities of the set of the anomalous data points before displaying a set of the anomalous data points in response to the probabilities being less than the critical value.

Abstract: A central server receives API calls requesting services. The central server identifies whether the API calls are associated with a merchant. A distribution is constructed based on the API calls. The central server further executes a pre-defined rule to identify a set of the API calls belonging to a maximum percentile in the distribution and a set of the API calls belonging to a minimum percentile in the distribution before estimating a set of the anomalous data points with one or more goodness of fit functions against the maximum percentile and the minimum percentile. A GUI receives a critical value from a user. In response to receiving the critical value, the central server generates probabilities of the set of the anomalous data points before displaying a set of the anomalous data points in response to the probabilities being less than the critical value.