Risk Management and Data Analytics in Accounting (D0R51A)
Summary
Summary of Data Analytics in Accountancy ('23-'24) (Commercial Engineer and TEW) (18/20)
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Course
Risk Management and Data Analytics in Accounting (D0R51A)
Institution
Katholieke Universiteit Leuven (KU Leuven)
This is a summary of the Data analytics section for the Risk management and data analytics in accountancy course taught by prof Steven Haverbeke. It's based on the lesson slides and my own notes
risk management and data analystics in accountancy
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Katholieke Universiteit Leuven (KU Leuven)
Master Handelsingenieur
Risk Management and Data Analytics in Accounting (D0R51A)
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CHAPTER 1: Data analytics in accounting and businesses:
1.What is data analytics:
Data analytics = the process of evaluating data with the purpose of drawing conclusions to address
business questions
provides a way to search through large structured (predefined data format) and unstructured
(pdf, text formats,…) data to identify unknown patterns or relationships
format does not really matter
Goal: transform data into valuable knowledge to make more informed business decisions
Big data = datasets which are too large and complex to be analyzed traditionally
59 billion terabytes of data in 2020 while 2 in 2010
Remember the 4 V’s:
Volume = size of the dataset
Velocity = speed of processing
Variety = different types of data
Veracity = data quality
2.Effect of data analytics:
2.1 EFFECT ON BUSINESSES:
Importance:
85% of CEOs put a high value on data analytics
86% of CEOs place data mining and analysis as second-most important strategic technology
Business analytics tops CEO’s list of priorities
Data analytics could generate up to $2 trillion in value per year
Data analytics is expected to have dramatic effects on auditing and financial reporting as well as
tax and managerial accounting
SO it is not only about having the data but also using it in the best way possible
2.2 EFFECT ON AUDITING
General: enhances audit quality + expanded services + added value to clients
audit process is changing from a traditional process towards a more automated one
1. allows audit professionals to focus more on the logic and rationale behind data queries and
less on gathering the actual data
2. expanded capabilities: testing for fraudulent transactions + automating compliance-
monitoring activities
3. analyze complete dataset rather than sampling financial data
2.3 MANAGEMENT ACCOUNTING
data analytics and management accounting have quit similar task descriptions
Enhancements: cost analysis, better decision-making, better forecasting, budgeting, production and
sales
, 2.4 FINANCIAL REPORTING
better estimates of collectability, write-downs, …
better understand business environment through social media and other external data
sources
analysts identify risks and opportunities through analysis of internet searches
3.The impact model:
The impact model:
Identify the questions
Master the data
Perform the test plan
Address and refine results
Communicate insights
Track outcomes
STEP 1: IDENTIFY THE QUESTIONS:
= understanding the business problems that need to be addressed
attributes to consider:
do we have the right data to answer the question?
Who is the audience that will use the results?
Is the scope of the question too narrow or to broad ?
How will the results be used analyze risk, make decisions, …
Examples questions:
Are employees circumventing internal controls over payments?
Are there any suspicious travel and entertainment expenses?
Are our customers paying us in a timely manner?
How can we predict the allowance for loan losses for our bank
loans?
How can we find transactions that are risky in terms of accounting
issues?
Who authorizes checks above $100,000?
How can errors be identified?
STEP 2: MASTER THE DATA
= know what data are available and how they relate to the problem
Consider the following 7 elements:
Data available in internal ERP systems
Data available in external networks and data warehouses
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