Didier Delanoye en Jasper Kerremans lichten toe hoe de steeds sneller veranderende processen bij hun klanten de aanpak van financiële audits beïnvloeden. Ze tonen hoe ze daarop antwoorden bieden via de de data-enabled audit methodologie en process mining technieken. Vertrekkende van enorme volumes aan gegevens, biedt de data-enabled audit methodologie nieuwe inzichten en meer assurance via verbeterde risico-inschattingen, analyses en testing, bijvoorbeeld van journaalboekingen. Process mining wordt enerzijds ingezet om het functioneren van key controls te bevestigen, en anderzijds om betere inzichten in bedrijfsprocessen te verwerven, bijvoorbeeld door middel van visualisaties en animaties die belangrijke afwijkingen van de verwachte processen blootleggen of efficiëntieverschillen tussen entiteiten tonen.

1. Data-enabled audit
www.pwc.be
Didier Delanoye
Director Financial Assurance Services PwC
Jasper Kerremans
Manager Risk Assurance Services PwC

2. Agenda
Changing context and developments1
Data-enabled audit2
Data-enabled process analysis3
What’s next4
2

3. Access to data has radically changed
Big Data = Transactions + Interactions + Observations
Text
• Purchase detail
• Purchase record
• Payment record
• Segmentation
• Offer details
• Customer Touches
• Support Contacts
• Web logs
• Offer history
• A/B testing
• Dynamic Pricing
• Affiliate Networks
• Search Marketing
• Behavioral Targeting
• Dynamic Funnels
• Sensors/RFID/Devices
• User Click Stream
• Mobile Web
• User Generated Content
• Social Interactions & Feeds
• Spatial & GPS Coordinates
• External Demographics
• Business Data Feeds
• HD Video, Audio, Images
• Speech to Text
• Product/Service Logs
• SMS/MMS
Big Data
Web
CRM
ERP
Increasing Data Variety and Complexity
Petabytes
Terabytes
Gigabytes
Megabytes
Doing things differently Doing different things
3

4. Business Value increases together with Complexity
Descriptive
What happened?
Diagnostic
Why did it happen?
Predictive
What will happen?
Prescriptive
What should I do?
Decision Support
Decision Automation
Decision ActionData
Analytics Human Input
Feedback
4

5. Data-enabled audit
Is someone outside of the finance department posting journals?
Big data
Small data
5

6. How can we reduce or remove the need to
perform substantive tests of detail?
Data-enabled audit of Revenue
How can we identify higher risk
transactions to focus our detailed testing?
Big data
Small data
6

7. Data-enabled audit
How does seasonality of
revenue impact profit?
Are the drivers of profit
changing over time?
Big data
Small data
7

8. Is there a three-
way match proces
in operation for
all invoices?
Data-enabled audit
Where was
segregation of
duties breached?
Big data
Small data
0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 -1,23
19,84
0,00
-7,77
Purchase
Document and
Vendor
Good Receipt
and Vendor
Invoice Receipt
and Vendor
Clearing and
Vendor
Purchase
Requisition
Purchase
Requistion and
Purchase
Document
Purchase
Document
Purchase
Document and
Goods Receipt
Purchase
Document and
Invoice Receipt
Goods Receipt
and Invoice
Receipt
Invoice Receipt Invoice Receipt
and Clearing
0,0
0,5
1,0
1,5
2,0
2,5
3,0
3,5
-10,0
-5,0
0,0
5,0
10,0
15,0
20,0
25,0
Line Items AmountLine Items (x1000) Amount (x1Mio)
8

9. Approach to process analysis
Traditional
Elementary Process Description
System:
Order Management
System
Sub-system Author: Date:
Proc ID Process
Name
Description
2.1
Take enquiry
details
Enquiries are received by
telephone, post, fax of e-mail. The
details of the enquiry will be
entered on an enquiry form, which
is then passed to the quotations
clerk.
2.2
Prepare
quotation
The quotations clerk will prepare a
quotation using the standard
procedures. If the customer has
requested an urgent quotation,
brief details will be given to the
customer by telephone, email or
fax. A standard quotation
document, including a copy of th
company’s terms and conditions of
trading, will in all cases be sent to
the customer by post or fax.
2.3
Update
quotation file
This quotation and all related
documentation will be files in the
quotation file. Brief details of the
enquiry, includinf the quotation
numer, will be entered in the
quotation register.
Big data
Small data
Data-enabled:
Process mining
9

10. Data-enabled approach to process analysis
Activity-related data
extracted from IT system
Data including information about:
• Behaviour: activity executions,
document creations, approvals
• User executing behaviour
• Time of behaviour execution
1
Reconstruction of links among
activities
2
Visualisation and detailed
analysis of the process
3
10

11. Advantages of data-enabled approach
Allowing for
high quality audits
• Detailed understanding of
end-to-end processes based on
factual data.
• Identification of cases deviating
from the regular flow for a better
risk assessment.
• Full population testing for all key
control activities in the process.
• Identification of manual controls
executed in less than a minute.
• Drill-down capabilities and
full details available for easier
follow-up.
Providing
business insights
• Measurement of process
standardisation level.
• Identification of
redundant activities and
duplicate transactions.
• Identification and root cause
analyses for long-lasting activities
and transactions.
• Identification of potential
inefficiencies in the process
(e.g. transactions stopped late in
the process, duplicate effort).
• Multiple view angles on the
process – users, work load,
lead times, transaction types,
automation, etc.
Initial investment is balanced by low effort for performing future analyses (re-runs)
Can be applied to any digital process, independent of systems and activities involved
11

12. Outliers are easily isolated for further investigation
8% outliers
12

13. We compare and benchmark entities, countries, teams,…
Entity CEntity BEntity A
13

14. 14
(Censored)
(Censored)
We visualise data and improvement opportunities
(Censored)
(Censored)
Big data
Small data

15. Teamwork and Engagement
Big data
Small data
We perform organisational analyses
15

16. We assist in process improvements
Big data
Small data
This animation shows the flow
of transactions for an
organisation's top 20 suppliers
for a period of 6 months.
16

17. Artificial Intelligence /
Machine learning
Blockchain
What’s next
Know what’s right and
find what’s wrong across
your general ledger on
demand, with the
objectivity of AI.
Analyse
expected
Unexpected
Analysed
population
Uses periodic sampling,
mostly manual
Data auditing analyses
a whole data set using
algorithms to determine
expected and unexpected
transactions
The unexpected transactions
can be broken down and
analysed further, resulting in
greater insight and an
enhanced risk-focused audit
A more significant
portion of selected
population is
analysed
Sample
size Further
analyse
unexpected
Traditional Trend
17

18. jasper.kerremans@be.pwc.com
Jasper Kerremans
Manager Risk Assurance Services PwC
Process Mining
Thank you!
didier.delanoye@be.pwc.com
Didier Delanoye
Director Financial Assurance Services PwC
Audit Transformation
18