1. BASIQ 2016 International Conference
Perspectives on Big Data and Business Intelligence Technologies in the Context of Audit Tasks
BASIQ 2016 International Conference
2-3 June 2016, Konstanz

2. Agenda Purpose and approach in the audit of financial data
Application of IT on mass data capture for decision making and controlling
Information technologies in the audit process

3. 1. Purpose and approach in the audit of financial data I/II
inherent risk
control risk
detection risk
audit risk
Multiple Model:
A high inherent risk leads to high audit risk, given the premises that control and detection risk remain constant.
Accordingly, an effective internal control system of a company generating the financial data can lead to a lower control risk which reduces audit risk

4. 1. Purpose and approach in the audit of financial data II/II

5. 1. Purpose and approach in the audit of financial data II/II

6. 1. Purpose and approach in the audit of financial data II/II

7. 2. Application of IT on mass data capture for decision making and controlling

8. 3. Information technologies in the audit process I/III
IT in the audit process
Evidential character
Solutions
Usage
To what extent can these technologies be used to generate audit assertions?
Which Big Data/ BI technologies are available to support the respective audit tasks?
Which requirements exist for the proper application of these technologies?

9. 3. Information technologies in the audit process II/III
Evidential character: Sufficiency and Reliability
Solutions:
Data
Use 1
Use 2
Security videos
Receipt and exit confirmation
Shipping costs confirmation
News videos
Overall market position
Product/ service problems
Social networks
Customer satisfaction
Fraud detection
RFID
Inventory confirmation
Pricing of goods
Web hits
Predict purchases
Predict revenue

10. 3. Information technologies in the audit process III/III
Usage:
Solutions
Database
Examples in the Audit Area
Pre-Engagement Risk Assessment
Initial Risk Assessment
Audit of Controls
Audit of financial Data
Forming Overall Conclusion
Prediction of daily fluctuations of the
Dow Jones Industrial Average Index
(Bollen et al., 2011)
Twitter Data (Google’s Profile of Mood States)
Unusual stock price fluctuation of entity
Fraud Risk evaluation, overall financial state of a firm
Risk Management controls
Financial instruments revaluation
Return on investments on financial instruments
Prediction of Customer Behavior to personalize landing pages or management inventories
(Cao et al., 2015)
IP-Addresses of User
US Census data
Transactional Data
Weather Data
Customer Complaints regarding entity
Focusing resources on more risky parts of the business
Controls on Stock Management
Inventories valuation
Overall analytical procedures
Prediction of optimal drilling sites in oil and gas exploration
Geographical Data
Impact of geographical operation sites
Budgeting or
Valuation of drilling rights
Earnings Return

11. Further steps 1.
Systemization of BI/ Big Data application areas in the audit area 2.
Expansion of traditional audit framework 3.
Evaluation of possibilities and limits regarding BI/ Big Data techniques

12. Thank you for your attention! Mulţumesc pentru atenţie!
Danke für die Aufmerksamkeit!

13. Authors: Aykut Bußian, Wiebke Geldmacher and Jan Kopia
Aykut Bussian
PhD Candidate at the Bucharest University of Economic Studies
B.Sc. and M.Sc. in Computer Science and Business Administration
B.A. and M.A. in Philosophy
Fund Investment Consultant Partner at Baker Tilly, Germany
Wiebke Geldmacher
PhD Candidate at the Bucharest University of Economic Studies
Research on the acceptance and implementation of a new carsharing model in Germany
M.A. in General Management
B.A. in International Business
Business Consultant at DB Engineering & Consulting GmbH, Qatar
Jan Kopia
PhD Candidate at the Bucharest University of Economic Studies
IT-Security Consultant
B.Sc. Computer Science
M.Sc. Business Administration