Case Study: a global credit card leader achieves 5-hour predictive incident response with InsightFinder

Global Credit Card Leader predicts incidents 5 hours early with InsightFinder

The global credit card leader faced significant challenges in its high-stakes, zero-latency environment, where infrastructure health was critical to preventing fraud and avoiding costly downtime. Their reactive monitoring of distributed Spark and Hive clusters was siloed, leading to blind spots and over 1,300 system-level errors in a single quarter. This fragmented approach forced senior engineers into lengthy manual investigations, creating financial risk and potential model drift for their AI fraud detection systems. They engaged InsightFinder to address these issues.

InsightFinder deployed its Predictive AI Observability platform to provide composite pattern recognition and an early warning system. The solution achieved 90.2% prediction accuracy, offering a 5-hour lead time before incidents occurred and automatically identifying the root cause for 93% of failures. This allowed the customer to shift from a reactive to a proactive state, preventing 232 high-risk incidents in one quarter and achieving a 90% productivity gain for engineering teams. InsightFinder's solution enabled multi-million dollar cost avoidance and transformed the organization's incident response into a strategic, data-driven model.