Ruixin Xu puts together a how-to guide:
Across industries, teams use Power BI to understand what has already happened. Dashboards show trends, highlight performance, and keep organizations aligned around a shared view of the business.
But leaders are asking new questions—not just what happened, but what is likely next and how outcomes might change if they act. They want insights that help teams prioritize, intervene earlier, and focus effort where it matters. This is why many organizations look to enrich Power BI reports with machine learning.
This challenge is especially common in financial services.
Consider a bank that uses Power BI to track customer activity, balances, and service usage. Historical analysis shows that around 20% of customers churn, with churn tied to factors such as customer tenure, product usage, service interactions, and balance changes.
Click through for the architecture example and process. The actual model is a LightGBM model, which is generally fine for two-class classification.