Finding Network Anomalies Faster with AI
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.
The mission at H2O.ai is to democratize AI for all so that more people across industries can use the power of AI to solve business and social challenges. The financial services industry is a key focus for the company with strong partnerships with leading brands like Wells Fargo, Citigroup, Capital One, PayPal, Discover, Dun & Bradstreet and Equifax helping to drive significant product innovation. H2O Driverless AI is an award-winning platform for automatic machine learning that empowers data science teams to scale by dramatically increasing the speed to develop highly accurate predictive models. Driverless AI includes innovative features of particular interest to financial services including machine learning interpretability (MLI), reason codes for individual predictions, and automatic time series modeling.