In a data mining project to solve claims fraud, what is the first step you should take?

• A. To develop a model to analyze the data
• B. Understand what it wants to achieve
• C. Prepare the data by cleaning it
• D. To understand the types of data to use

Answer: (B)

Starting with what you want to achieve drives the entire project. In a claims fraud data-mining effort, you must define the problem clearly—what counts as fraud, what success looks like, and how you’ll measure it (for example, reduction in fraud losses, hit rate, or cost per investigation). This objective sets the scope, the stakeholders involved, and the practical constraints, and it determines what signals to seek, what data will be needed, and which evaluation metrics will matter. Without a clear goal, you might collect the wrong data, chase an irrelevant signal, or optimize a metric that doesn’t reflect real business value.

Once the objective is defined, you can correctly identify what types of data are needed, plan data quality and preprocessing steps, and then build and validate models. Those steps become meaningful only in the context of the defined goal.