A Framework for Information Mining from Audit Data

Companies seek new technologies to enhance their business processes. As information systems in companies become more complex, the traditional audit trail is diminished or eliminated. The importance of audit automation and the utilization of IT in modern audits has grown significantly in recent years due to both technological developments and changing regulatory environment. Automation of business processes has inevitably led to changes in auditing procedures and standards. Additional drivers of audit automation adoption include the ever growing complexity of business transactions and increasing risk exposure of modern enterprises. Therefore, the audit’s purpose, which is namely to examine the true and fair view of financial statements, is heavily increasing in complexity. On the other hand, the prevalence of the data paradigm has manifold impacts on the accounting-relevant processes. To cover the requirements to Audit Information System, we strive for the development of a framework for information mining from audit data. In this paper, we report on the framework we have developed in the department of Accounting and Finance. Our study identifies the management of audit alarms and the prevention of the alarm floods as critical tasks in this implementation process. We develop an approach to satisfy these requirements utilizing the data mining techniques. We analyse established audit data from a well-known data repository considering the dimensions of the data paradigm. This led us to a tentative proposal of a conceptual mechanism for an integrated audit approach. With the increasing number of financial fraud cases, the application of data mining techniques could play a big part in improving the quality of conducting audit in the future.