In Business Reporter, David Drai, a founder of AI analytics startup Anodot, explains how machine learning (ML) is transforming the way financial service companies monitor their business and how this technology is poised to change the banking industry.
Teams tasked with monitoring data typically rely on dashboards and static thresholds – manually set for each KPI – to identify anomalies across the business. While this may work well enough for hardware-related KPIs, static thresholds prove far too fixed to accommodate the dynamic behavioral patterns of business metrics. An unusual slowing of a rise in revenue may not trigger an alert because it falls within fixed thresholds.
Monitoring customer experience is particularly challenging. Without automation, banks struggle to monitor their large customer bases and millions of daily usage events.
ML-driven monitoring can track millions of business metrics autonomously and in real time. Algorithms continuously perform granular, rapid analysis of every individual customer account, to extract valuable insights into their behaviour. From there, banks can automatically respond with targeted offers and messaging that enhance customer experience, to reduce churn and more effectively cross-sell. ML models are trained to improve over time, providing more accurate insights and more timely alerts.
Faster insights help to accelerate incident management. Companies using Autonomous Business Monitoring saw YoY time to detection drop by as much as 80 percent and YoY incident costs decrease up to 70 percent.