Summary DSA
In de tabbladen die hierna volgen zijn per college de belangrijkste onderwerpen besproken.
Ook worden de belangrijkste codes in Python besproken.
,pen besproken.
,College 1a
Introduction to data science in practice and into Python
Audit data analytics (ADA or ADAs) are defined as:
The science and art of discovering and analyzing patterns, identifying anomalies, and extracting
other useful information in data underlying or related to the subject matter of an audit through ana
modeling , and visualization for the purpose of planning or performing the audit.
Examples when Data Science can be useful:
1. Improved understanding of an entity's operations and associated risks, including the risk of fraud
2. Increased potential for detecting material misstatements.
3. Improved communications with those charged with governance of audited entities.
What is data science?
Data Analytics in every stage of audit process:
* Inherent risk management (Understanding the entity and its environment).
* Control risk management (Existence, design and operation of internal controls).
* Analytical procedures (Hypothesis testing on relationships).
* Test of details (Sample or complete).
Main findings article from Gepp et al. (2018):
1. The analysis revealed four main genealogies, which we review below: (1) financial distress mode
(2) financial fraud modelling, (3) stock market prediction and quantitative modelling, and (4) auditi
2. Our literature review reveals a general consensus that big data is underutilized in auditing. A po
explanation for this trend is that auditors are reluctant to use techniques and technology that are f
ahead of those adopted by their client firms.
, 3. Earley (2015) acknowledges that big data could be a game-changer in auditing, and Schneider e
(2015) predict that data analytics will significantly change the way auditors work.
4. There are many opportunities to use big data techniques in auditing, particularly when rigorous
procedures are combined with traditional audit techniques and expert judgement.
Example fraud and anomaly detection:
* Logistic regression: calculate the probability of fraudulent payment based on previous frauds.
* Knn: calculate if something is a fraud based on similarities of other frauds.
* k-means clustering: create clusters of payments and look at uncommon clusters.
In de tabbladen die hierna volgen zijn per college de belangrijkste onderwerpen besproken.
Ook worden de belangrijkste codes in Python besproken.
,pen besproken.
,College 1a
Introduction to data science in practice and into Python
Audit data analytics (ADA or ADAs) are defined as:
The science and art of discovering and analyzing patterns, identifying anomalies, and extracting
other useful information in data underlying or related to the subject matter of an audit through ana
modeling , and visualization for the purpose of planning or performing the audit.
Examples when Data Science can be useful:
1. Improved understanding of an entity's operations and associated risks, including the risk of fraud
2. Increased potential for detecting material misstatements.
3. Improved communications with those charged with governance of audited entities.
What is data science?
Data Analytics in every stage of audit process:
* Inherent risk management (Understanding the entity and its environment).
* Control risk management (Existence, design and operation of internal controls).
* Analytical procedures (Hypothesis testing on relationships).
* Test of details (Sample or complete).
Main findings article from Gepp et al. (2018):
1. The analysis revealed four main genealogies, which we review below: (1) financial distress mode
(2) financial fraud modelling, (3) stock market prediction and quantitative modelling, and (4) auditi
2. Our literature review reveals a general consensus that big data is underutilized in auditing. A po
explanation for this trend is that auditors are reluctant to use techniques and technology that are f
ahead of those adopted by their client firms.
, 3. Earley (2015) acknowledges that big data could be a game-changer in auditing, and Schneider e
(2015) predict that data analytics will significantly change the way auditors work.
4. There are many opportunities to use big data techniques in auditing, particularly when rigorous
procedures are combined with traditional audit techniques and expert judgement.
Example fraud and anomaly detection:
* Logistic regression: calculate the probability of fraudulent payment based on previous frauds.
* Knn: calculate if something is a fraud based on similarities of other frauds.
* k-means clustering: create clusters of payments and look at uncommon clusters.
