Data Mining Techniques for Auditing Attest Function and Fraud Detection

The need for data mining in the auditing field is growing rapidly. As the online systems and the hi-technology devices make accounting transactions more complicated and easier to manipulate, the use of data mining in the auditing profession has been increasing in recent years. Since auditing involves evaluation of massive data in the attest function, data mining allows this process to be done in an easier manner. Auditors use computer aided audit software (CAATs) to make the process more accurate and reliable. There are three basic approaches to data mining: mathematical-based methods, distance-based methods, and logic-based methods 1 The first approach, mathematical-based methods, uses neural networks, which are networks of nodes modeled after a neuron or neural circuit that mimics the human brain. These neural networks are used in the auditing profession in many different ways, such as risk assessment, finding errors and fraud, determining the going concern of a company, evaluating financial distress, and making bankruptcy predictions. The next approach to data mining is distance-based methods which uses clustering to put large sets of data into groups and classifications based on attributes. This method is commonly used in marketing but is also useful in auditing. The third approach to data mining is the logic-based approach which uses decision trees to organize data. In the field of auditing, the logic-based method is mos