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By mining their existing data, these departments can find the best way to identify non-compliance. Data mining combines powerful analytical techniques with your business knowledge to turn acquired data into the insight you need to identify probable instances of non-compliance. Building models to find non-compliant taxpayers. Data mining now enables this department to predict which tax returns are likely to be non-compliant. This dynamic process gives auditors the power to determine what returns they should target to recoup millions of dollars in otherwise lost revenue. Plus, auditors save hours of time. Step 1: Understand your data Clementine’s visual programming interface makes examining and modeling the audit records straightforward. As the screenshot below shows. Step 2: Set a benchmark to test future models In this step, we consider which types of models we can build to test our data. For instance, we could use linear regression — a popular, straightforward statistical method. Step 3: Set up the data to build the models. Step 4: Set up the models. Step 5: Run the data to build the models. Step 6: Compare the models. Step 7: Use the models on future tax records. Strategically deploy your data mining results for optimum success.
Tags : visual programming interface, compliance data, linear regression, optimum success, building models, audit records, spss data, statistical method, analytical techniques, business knowledge, time step, step 6, step 2, tax returns, benchmark
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