This Small Business Innovation Research (SBIR) Phase I project will provide a visualization and analysis capability to auditing software that will enable non-scientifically oriented users to identify and monitor patterns, trends, and exceptions faster and with greater accuracy. Existing auditing tools require auditors to randomly sample data or use unfamiliar complex statistical methods to examine data. This problem is complicated by volume of data collected by enterprise systems and regulatory complexity such as the FAR and the Sarbanes-Oxley Act. Wavelets provide a method for exploring greater volumes of data at multiple levels of resolution while highlighting exceptions, trends, and variances. Applying wavelet methods to budgetary and financial control environments, presents two significant challenges: creating a methodology that is inherently scalable and timely; and secondly, enabling auditors, decision-makers, and support systems to visualize and act on the knowledge derived from the raw data. The objective of this Phase 1 research is to demonstrate the feasibility of utilizing wavelets as the basis for an enhanced auditing and financial control methodology. The broader impact of this SBIR initiative will be to improve the decision quality and timeliness of not only financial auditing and management decision-makers, but also of the systems that implement and monitor business processes. The result of this research effort will improve the economic productivity of several sectors and introduce new research opportunities in decision support and data engineering to both academia and industry
