Call or for any enquiries. Data Analysis, abstract, table of content:: The chapter however, concluded with scope, limitation and delimitation of the work, delimitation of terms and reference.
Technology has transformed business processes and created a wealth of data that can be leveraged by accountants and auditors with the requisite mindset.
Data analysis can enable auditors to focus on outliers and exceptions, identifying the riskiest areas of the audit.
The authors introduce the process, with a review of some emerging approaches and compilation of useful resources for auditors new to the topic. This article aims at introducing basic data analysis concepts to enable accounting professionals to understand how to navigate within this new environment.
Specifically, the focus will not be on auditing and accounting standards and their current required procedures, but rather on what the profession can progressively achieve with data analytics. Most analytical procedures, in the right circumstances, may be applicable to the entire audit process, from risk assessment to test of details.
What follows is a step-by-step overview Exhibit 1 of best practices for the process of applying analytics, with an emphasis on audit by exception ABE.
Understanding the elements of a certain cycle or application is essential for selecting data and understanding risk. Flowcharting is also possible in Microsoft Excel or PowerPoint. Exhibit 2 shows a sample flowcharting process taken from an insurance company.
With the risks in mind, the next step is to choose the data fields to be extracted and examined. This type of analysis is not very different from what would be done on a traditional audit. A progressively increasing number of audit apps are being sold or shared that can serve to simplify the audit task e.
Nevertheless, many audit software providers e. It is very important for the sake of completeness to understand the nature, distribution, and limitations of the population to be tested.
Understanding the scope and limitations of the data is imperative, as it enables an accountant to choose the most appropriate and effective analytical technique. Understanding the fields with descriptive statistics.
The examination of key fields for their characteristics and statistical parameters e. Modern tools of visualization e. Auditors can focus more extensive testing on the areas highlighted as highest risk.
Choice of analytic methods and alternative approaches. A great number of analytic methods have been applied to audits in a research mode Deniz Appelbaum, Alexander Kogan, and Miklos Vasarhelyi, Analytics for External Auditing: Exhibit 3 provides examples of several analytic methods.
Given this variety of choices, auditors need to know the data as intimately as possible, as well as understand the specific analytic task, in order to reduce the pool of potential analytical methods. Confirmatory data analysis and finding outliers.
Having identified the riskiest areas of the audit, an auditor should next use some of the techniques discussed above to evaluate the data. These techniques are used first to infer analytic models to provide audit benchmarks or expectations; the actual values are then compared with the benchmarks.
Any significant deviations should be investigated by auditors. For example, regression analysis can be used to derive a model for the revenue account based on archival data.
The values calculated by this model should be compared against the actual revenue amounts, and any significant differences investigated. Evaluating results evaluation and integrating with traditional findings.
Ideally, the outliers should be segregated from the population for more detailed audit examination, as discussed above. Theoretically, ABE provides a more efficient and effective approach for identifying questionable numbers. The main difference between the ABE and a sample-driven audit is how the subset to be examined is obtained.The analysis of the performance of computerization on Nigeria financial system.
(A case study of union Bank Plc Enugu) PROPOSAL. A research proposal is an abridged account of what the research intends to do and the procedure he intends to adopt in doing it (Orji ). Introduction to qualitative research methods used to study computerization and information systems, such as open-ended interviewing, participant observation and ethnography.
Studies of the methods in practice through . Introduction: The Benefits, and Drawbacks, of Computerization In crime analysis, it’s important to remember that computer applications are not an end unto themselves, but a means of accomplishing tasks—usually analyzing data and. presenting the results of the analysis—that existed before the computer applications were available.
Introduction Computer-based patient records is a system in which its function are becoming an essential technology for health care in part because the information management challenges were being faced by health care professionals that are increasing daily.
computerization, the process of computerization, effects of computerization and the actors’ roles in the process. Statements related to intended effects of computerization have in the analysis solely origi-nated from documents, protocols dated from that time, since we wanted to minimize the time effect.
Hence, the project work will treat and handle properly and comprehensively the analysis of the performance of computerization on Nigeria financial system; using the Union Bank Plc Enugu as a case study.