Corporate financial analysts and risk management teams waste 70% of their billable hours manually extracting data, cleaning messy spreadsheets, and writing boilerplate Python code to run predictive models. When a sudden market shift occurs—such as a spike in credit defaults or a sharp decline in transaction volume—traditional static business intelligence (BI) dashboards fail because they only show historical data. Building a predictive machine learning pipeline currently requires deep data-engineering expertise and takes days or weeks, preventing companies from mitigating financial fraud or credit risks in real-time.
