Identifying a Business Opportunity for Modeling
Explore how to determine when predictive modeling is appropriate by translating business goals into modeling tasks, evaluating data reliability, defining actionable decisions, and weighing the benefits of models versus simpler reporting solutions.
A fraud-investigation team wants to prioritize insurance claims for review. The request sounds like a reason to build a model, but the first question is simpler: what decision would change? If a claim receives a high-risk signal, investigators might review it sooner. If no review capacity, action, or recorded outcome exists, a prediction would not improve the process.
Predictive modeling learns a rule from historical examples and uses that rule to estimate an outcome for new cases. For claim review, historical examples would connect information available when a claim arrived with a later, reliably recorded finding such as confirmed fraud. By contrast, descriptive reporting summarizes what has already happened. A report showing claim volume by region, claim type, and week can reveal workload patterns without attempting to predict an outcome. ...