Mobile applications are critical to many businesses today. For credit card and banking companies, for example, mobile applications represent a significant channel of interaction where customer can review transactions, pay bills and resolve support issues. When application services are not available, customers use more expensive call centers for support. With payment applications, an outage means lost transactions, revenues and increased customer churn.
AI systems have been proven successful at detecting anomalies in transaction volume data. This time series process looks at expected data volumes based on historical patterns. Upper and lower boundaries are also predicted based on volume variation. This system is then used to compare real-time transaction value to expected volume. This real-time system allows network administrators to be notified when transactions start to spike above or fall below these boundaries so they can take action before an outage in service.