What is Data Mining?

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Data Mining


Data mining is the science of searching large volumes of data for patterns. It combines several different techniques essential to detecting fraud, including the streamlining of raw data into understandable patterns. 
Data mining can also help prevent fraud. Additionally, it is an effective way for fraud examiners to develop fraud targets for further investigation.

The Data Analysis Process
Although the purpose of data analysis involves running targeted tests on data to identify anomalies, the ability of such tests to help detect fraud depends greatly on what the fraud examiner does before and after actually performing the data analysis techniques. 
Without sufficient time and attention devoted to planning early on, the fraud examiner risks analyzing the data inefficiently, lacking focus or direction for the engagement, running into avoidable technical difficulties, and possibly overlooking key areas for exploration.

As a first step—long before determining which tests to run—the fraud examiner must know what data is available to be analyzed and how that data is structured.
Understanding the structure of the existing data will not only help ensure that the fraud examiner builds workable tests to be run on the data, but might also help identify additional areas for exploration that might otherwise have been overlooked.
To ensure the most accurate and meaningful results, a formal data analysis process should be applied that begins several steps before the tests are run and concludes with active and ongoing review of the data. 

While the specific process will vary based on the realities and needs of the organization, the following approach contains steps that should be considered and implemented, to the appropriate extent, in each data analysis engagement: 
  1. Planning phase (Understand the data, Define examination objectives, Build a profile of potential frauds, Determine whether predication exists)
  2. Preparation phase (Identify the relevant data, Obtain the data, Verify the data, Cleanse and normalize the data) 
  3. Testing and interpretation phase (Analyze the data) 
  4. Analyze the data (Respond to the analysis findings, Monitor the data) 
There are five significant advantages using data analysis software.
  1. Allows the fraud examiner to centralise an investigation, relying less on others to gather pertinent information.
  2. Allows the fraud examiner to ensure that an investigation is accurate and complete. 
  3. Allows the fraud examiner to base predictions about the probability of a fraudulent situation on reliable statistical information. 
  4. Allows the fraud examiner to search entire data files for red flags of possible fraud. 
  5. Assist the fraud examiner in developing reference files for ongoing fraud detection and investigation work. 

Source: Module ACFE: Investigation

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