Application of Advanced Data Analytics in the Audit Process

2019 ◽  
pp. 521-530
Author(s):  
Leo Mrsic ◽  
Sanja Petracic ◽  
Mislav Balkovic
Keyword(s):  
Data Analytics ◽  
Audit Process

scholarly journals Principles of International Auditing and Assurance

2021 ◽  
Author(s):  
Rick Hayes ◽  
Philip Wallage ◽  
Peter Eimers
Keyword(s):  
Data Analytics ◽  
Audit Quality ◽  
International Perspective ◽  
International Standards ◽  
National Standards ◽  
Fourth Edition ◽  
Audit Process ◽  
Automated Tools ◽  
Structured Approach ◽  
World Perspective

The first textbook based upon International Standards on Auditing (ISAs), this fully revised and updated fourth edition presents a structured approach to auditing principles using ISAs as its basis. The International Standards on Auditing are now widely regarded as the global benchmark for auditing standards and as such an important audit quality indicator.<br><br> This book describes the developments and practical use of all ISAs, as well as significant national standards in different countries. The new edition has been updated in line with International Standards and presents a truly International perspective. The book provides students with a real-world perspective as close to current auditing practice and thinking as possible.<br><br> Key features:<br> <ol> <li>Structure of the book following the four phases of the audit process</li> <li>Coverage of the latest auditing insights including technology and automated tools & techniques (data analytics)</li> <li>Updates of the most recent auditing & assurance standards, including ISA 315 and 540</li> <li>Highlighting the broader range of assurance engagements</li> <li>Practice exam-style questions with end-of-chapter answers</li> <br><br> <a href="https://principlesofauditing.com/">And take a look at the book website here, for more information and extra materials for both tutors and students.</a>

scholarly journals Are auditors' reliance on conclusions from data analytics impacted by different data analytic inputs?

2021 ◽  
Author(s):  
Jared Koreff
Keyword(s):  
Predictive Models ◽  
Data Analytics ◽  
Relevant Information ◽  
Analytical Models ◽  
Great Promise ◽  
Audit Process ◽  
Use Of Data ◽  
Anomaly Models ◽  
Data Analytic ◽  
Global Stakeholders

Global stakeholders have expressed interest in increasing the use of data analytics throughout the audit process. While data analytics offer great promise in identifying audit-relevant information, auditors may not uniformly incorporate this information into their decision making. This study examines whether conclusions from two data analytic inputs, the type of data analytical model (anomaly vs. predictive) and type of data analyzed (financial vs. nonfinancial), result in different auditors' decisions. Findings suggest that conclusions from data analytical models and data analyzed jointly impact budgeted audit hours. Specifically, when financial data is analyzed auditors increase budgeted audit hours more when predictive models are used than when anomaly models are used. The opposite occurs when nonfinancial data is analyzed, auditors increase budgeted audit hours more when anomaly models are used compared to predictive models. These findings provide initial evidence that data analytics with different inputs do not uniformly impact auditors' judgments.

Big Data Analytics: The Next Big Thing

2019 ◽  
Vol 54 (5) ◽  
pp. 20
Author(s):  
Dheeraj Kumar Pradhan
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics

Strategy in Discovery Mode - Wie Big Data & Analytics strategisches Denken verdrängen kann

2020 ◽  
Vol 49 (5) ◽  
pp. 11-17
Author(s):  
Thomas Wrona ◽  
Pauline Reinecke
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics

Big Data & Analytics (BDA) ist zu einer kaum hinterfragten Institution für Effizienz und Wettbewerbsvorteil von Unternehmen geworden. Zu viele prominente Beispiele, wie der Erfolg von Google oder Amazon, scheinen die Bedeutung zu bestätigen, die Daten und Algorithmen zur Erlangung von langfristigen Wettbewerbsvorteilen zukommt. Sowohl die Praxis als auch die Wissenschaft scheinen geradezu euphorisch auf den „Datenzug“ aufzuspringen. Wenn Risiken thematisiert werden, dann handelt es sich meist um ethische Fragen. Dabei wird häufig übersehen, dass die diskutierten Vorteile sich primär aus einer operativen Effizienzperspektive ergeben. Strategische Wirkungen werden allenfalls in Bezug auf Geschäftsmodellinnovationen diskutiert, deren tatsächlicher Innovationsgrad noch zu beurteilen ist. Im Folgenden soll gezeigt werden, dass durch BDA zwar Wettbewerbsvorteile erzeugt werden können, dass aber hiermit auch große strategische Risiken verbunden sind, die derzeit kaum beachtet werden.

Eagle Ford Fluid Type Variation and Completion Optimization: A Case for Data Analytics

2019 ◽  
Author(s):  
Fahd Siddiqui ◽  
Ali Rezaei ◽  
Birol Dindoruk ◽  
Mohamed Y. Soliman
Keyword(s):  
Data Analytics ◽  
Fluid Type ◽  
Eagle Ford

Data Analytics on Graphs Part II: Signals on Graphs

2020 ◽  
Vol 13 (2-3) ◽  
pp. 158-331
Author(s):  
Ljubiša Stanković ◽  
Danilo Mandic ◽  
Miloš Daković ◽  
Miloš Brajović ◽  
Bruno Scalzo ◽  
...  
Keyword(s):  
Data Analytics

Summary of the SciTech 2020 Technical Panel on In Situ/In Transit Computational Environments for Visualization and Data Analytics

2021 ◽  
Author(s):  
Earl P. Duque ◽  
Steve M. Legensky ◽  
Brad J. Whitlock ◽  
David H. Rogers ◽  
Andrew C. Bauer ◽  
...  
Keyword(s):  
Data Analytics ◽  
In Transit

Data Analytics to Help Improve Hydraulic Fracture Geometry Prediction Using Support Vector Regression: Case Study

2020 ◽  
Author(s):  
Avinash Wesley ◽  
Bharat Mantha ◽  
Ajay Rajeev ◽  
Aimee Taylor ◽  
Mohit Dholi ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Hydraulic Fracture ◽  
Data Analytics ◽  
Support Vector ◽  
Fracture Geometry
Sign in / Sign up

Export Citation Format

Share Document