Automated Clustering for Data Analytics

2019 ◽  
Vol 16 (2) ◽  
pp. 43-58 ◽  
Author(s):  
Paul E. Byrnes
Keyword(s):  
Data Analytics ◽  
Financial Statement ◽  
Decision Point ◽  
Technical Aspects ◽  
Fraud Risk ◽  
Use Of Data ◽  
Decision Points ◽  
Financial Statement Audit ◽  
Mining Methods ◽  
Construction Model

ABSTRACT Today, auditors must consider the risks of material misstatement due to fraud during the financial statement audit (Messier, Glover, and Prawitt 2016). Current audit guidance recommends the use of data mining methods such as clustering to improve the likelihood of discovering irregularities during fraud risk assessment (ASB 2012). Unfortunately, significant challenges exist relative to using clustering in practice, including data preprocessing, model construction, model selection, and outlier detection. The traditional auditor is not trained to effectively address these complexities. One solution entails automation of clustering, thus eliminating the difficult, manual decision points within the clustering process. This would allow practitioners to focus on problem investigation and resolution, rather than being burdened with the technical aspects of clustering. In this paper, automated clustering is explored. In the process, each manual decision point is addressed, and a suitable automated solution is developed. Upon conclusion, a clustering application is formulated and demonstrated.

Dickinson Technologies, Inc.: Assessing Control Environment and Fraud Risk

2003 ◽  
Vol 18 (1) ◽  
pp. 71-78 ◽  
Author(s):  
Christopher P. Agoglia ◽  
Kevin F. Brown ◽  
Dennis M. Hanno
Keyword(s):  
Risk Assessment ◽  
Internal Control ◽  
Financial Statement ◽  
Risk Assessments ◽  
Background Information ◽  
Control Environment ◽  
Abstract Concepts ◽  
Fraud Risk ◽  
Financial Statement Audit ◽  
Fraud Risk Assessments

This instructional case provides you an opportunity to perform realistic audit tasks using evidence obtained from an actual company. Through the use of engaging materials, the case helps you to develop an understanding of the control environment concepts presented in SAS No. 78 (AICPA 1995), Consideration of Internal Control in a Financial Statement Audit, and fraud risk assessment presented in SAS No. 99 (AICPA 2002), Consideration of Fraud in a Financial Statement Audit. This case involves making a series of fraud risk assessments based on company background information and a detailed and realistic control environment questionnaire, which provide you a context that makes the often abstract concepts relating to control environment and fraud risk assessment more concrete.

Developing Student Abilities to Recognize Risk Factors: A Series of Scenarios

2002 ◽  
Vol 17 (1) ◽  
pp. 57-67
Author(s):  
Carolyn A. Strand ◽  
Sandra T. Welch ◽  
Sarah A. Holmes ◽  
Steven L. Judd
Keyword(s):  
Risk Factors ◽  
Financial Statement ◽  
Red Flags ◽  
Professional Skill ◽  
Fraud Risk ◽  
Financial Statement Audit ◽  
Auditing Standards

Misappropriation of assets is an expensive and growing problem. However, detecting this type of fraud is very difficult. Green and Calderon (1996) claim that externally observable risk factors can help signal the likelihood of fraud. Awareness and timely recognition of these “red flags” might improve an individual's ability to assess the potential vulnerability of an organization to fraud. Contained herein is a case consisting of five scenarios that deal with the risk factors identified in Statement on Auditing Standards (SAS) No. 82, Consideration of Fraud in a Financial Statement Audit (AICPA 1997). Throughout the case, you will be confronted with a number of clues that may suggest employee wrongdoing. This case is designed to help you develop your knowledge and professional skill regarding the recognition of fraud risk factors. Although textbooks, and other sources, frequently list various risk factors, these same clues may not be as obvious to you when they actually occur in an organization.

The use of data analytics in external auditing: a content analysis approach

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yeamin Jacky ◽  
Noor Adwa Sulaiman
Keyword(s):  
Information Technology ◽  
Content Analysis ◽  
Working Group ◽  
Data Analytics ◽  
Design Methodology ◽  
Financial Statement ◽  
Content Type ◽  
Factors Affecting ◽  
Use Of Data ◽  
Potential Factors

PurposeThis study examines the perceptions of interested stakeholders on the factors affecting the use of data analytics (DA) in financial statement audits. Response letters submitted by stakeholders of the auditing services to the International Auditing and Assurance Standards Board's (IAASB) Data Analytics Working Group (DAWG) served as sources for analysis.Design/methodology/approachThe modified information technology audit model was used as a framework to perform a direct content analysis of all the 50 response letters submitted to the DAWG.FindingsThe analysis showed that a range of attributes, such as the usefulness of DA in auditing, authoritative guidance (auditing standards), data reliability and quality, auditors' skills, clients' factors and costs, were the factors perceived by stakeholders to be affecting the use of DA in external auditing.Research limitations/implicationsThis study is subjected to the limitations inherent to all content analysis studies. Nonetheless, the findings offer additional insights about potential factors affecting the adoption of DA in audit practices.Originality/valueThe data noted in the published statements highlighted the perceptions of a range of stakeholders with regards to the factors affecting the use of DA in auditing.

scholarly journals A Task-Centered, Multiple Method Approach To Teaching Fraud Risk Assessment

2011 ◽  
Vol 3 (3) ◽  
Author(s):  
Bonnie W. Morris ◽  
Ann B. Pushkin ◽  
William E. Spangler
Keyword(s):  
Risk Assessment ◽  
Financial Reporting ◽  
Financial Statement ◽  
Occurrence Rate ◽  
Fraudulent Financial Reporting ◽  
Fraud Risk ◽  
Financial Statement Audit ◽  
Multiple Method ◽  
Approach To Teaching ◽  
Research In Education

This manuscript provides an approach to teaching fraud risk assessment that is based on an analysis of the task and relevant research in education, cognitive psychology, and artificial intelligence. Fraud risk assessment (FRA) in financial reporting is an important and difficult task that must be performed in every financial statement audit. When auditors fail to detect fraudulent financial reporting (FFR), they are likely to become targets of shareholder and creditor litigation. Although FFR has a low occurrence rate considering the large number of financial statement audits conducted, it has a devastating impact on the investors, creditors and the profession.

Preparing for Audit Data Analytics with the AICPA General Ledger Audit Data Standards

2020 ◽  
Author(s):  
Lorraine S. Lee ◽  
Gretchen Casterella ◽  
Barry Wray
Keyword(s):  
Data Analytics ◽  
Task Force ◽  
Writing Skills ◽  
Financial Statement ◽  
Journal Entry ◽  
Data Standards ◽  
Microsoft Access ◽  
Financial Statement Audit ◽  
Audit Data ◽  
Client Data

It is challenging for auditors to effectively and efficiently use data analytics in audit procedures and general ledger testing when the data acquired from clients is often incomplete and not in a usable format. Considerable time must be spent cleansing, transforming, standardizing, and validating the data prior to analyzing it. This problem motivated the AICPA task force to develop a set of Audit Data Standards for streamlining the exchange of data.  This paper describes an extensive exercise where students: 1) develop a Microsoft Access database that complies with the Audit Data Standards (ADS) for general ledger data; 2) cleanse and transform non-standardized client data for import into an ADS-compliant database; and 3) write queries for general ledger testing and journal entry testing. The exercise strengthens students’ database and query-writing skills while introducing the ADS in the context of realistic tasks to support a financial statement audit.

Financial fraud detection and big data analytics – implications on auditors’ use of fraud brainstorming session

2019 ◽  
Vol 34 (3) ◽  
pp. 324-337 ◽  
Author(s):  
Jiali Tang ◽  
Khondkar E. Karim
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Idea Generation ◽  
Fraud Detection ◽  
Content Type ◽  
Collective Wisdom ◽  
Fraud Risk ◽  
Collection Data ◽  
Audit Team

PurposeThis paper aims to discuss the application of Big Data analytics to the brainstorming session in the current auditing standards.Design/methodology/approachThe authors review the literature related to fraud, brainstorming sessions and Big Data, and propose a model that auditors can follow during the brainstorming sessions by applying Big Data analytics at different steps.FindingsThe existing audit practice aimed at identifying the fraud risk factors needs enhancement, due to the inefficient use of unstructured data. The brainstorming session provides a useful setting for such concern as it draws on collective wisdom and encourages idea generation. The integration of Big Data analytics into brainstorming can broaden the information size, strengthen the results from analytical procedures and facilitate auditors’ communication. In the model proposed, an audit team can use Big Data tools at every step of the brainstorming process, including initial data collection, data integration, fraud indicator identification, group meetings, conclusions and documentation.Originality/valueThe proposed model can both address the current issues contained in brainstorming (e.g. low-quality discussions and production blocking) and improve the overall effectiveness of fraud detection.

scholarly journals Mathematical Issues in Data Science and Applications for Health care

2019 ◽  
Author(s):  
Sitti Zuhaerah Thalhah ◽  
Mohammad Tohir ◽  
Phong Thanh Nguyen ◽  
K. Shankar ◽  
Robbi Rahim
Keyword(s):  
Data Analytics ◽  
Data Science ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Decision Models ◽  
Computational Techniques ◽  
Healthcare Applications ◽  
Use Of Data ◽  
Source Data ◽  
Military Applications

For development in military applications, industrial and government the predictive analytics and decision models have long been cornerstones. In modern healthcare system technologies and big data analytics and modeling of multi-source data system play an increasingly important role. Into mathematical models in these domains various problems arising that can be formulated, by using computational techniques, sophisticated optimization and decision analysis it can be analyzed. This paper studies the use of data science in healthcare applications and the mathematical issues in data science.

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