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TRANSCRIPT
22 June 2017
CAACM
Better, Faster, Different – Analytics and Internal Audit
Page 1 |Page 1 |
Understand what is Analytics
Integration and Value of Analytics to an IA process
Understand applicable technologies
Better, Faster, Different –
Analytics and Internal Audit
1
2
3
Page 2 |
“Looking for a needle in a
haystack”
What is Analytics
Page 3 |
What is Analytics
“Extracting the nuggets of gold
hidden under mountains of data”
Page 4 |
Analytics Definition
► Analytics is a commonly used term which can have
a variety of meanings – it can be defined narrowly
as “A process involving the analysis of data to
derive insight, intelligence and/or improvement to a
business problem, process or issue”.
► The term ‘analytics’ is also used to refer to the wider
business intelligence, data management, the
application of business rules, reporting, etc. This is
better know as Enterprise Intelligence of which
Analytics is a subset.
Page 5 |
Transformation of data to information,
information to insight
and insight to intelligence….
Another definition
Page 6 |
So what?
► Changing what is
commercially possible
► Changing the economics
of scale
► Changing competitive
differentiation
Explosive growth of digital info
Falling cost of computing and storage
Democratization of tools
Analytics market trends
► Analytics is transforming how businesses operate and where/how value
gets created in most sectors. It is a strategy and operational issue, NOT
a technology issue.
Page 7 |
Types of analytics
Imp
ac
t
Sophistication
Query Reporting Descriptive Analytics Predictive Analytics Prescriptive Analytics
►Objective
►Use data to inform
decisions
►Objective
►Understand characteristics
of historical data
►Uncover relationships
between variables
►Objective
►Leverage data to make
predictions about the
future
►Forecast expectations
about the future effects of
decisions
►Objective
►Improve efficiency and
effectiveness of business
practices
►Optimize performance
►Techniques
►SQL Queries
►Techniques
► Descriptive Statistics
► Visual Analytics
► Text Analytics
►Techniques
►Simulation
►Linear Programming
►Integer Programming
►Techniques
►Classification
►Cluster Analysis
►Time-Series Analysis
►Machine Learning
What do we know? What is happening? How do we make it better?What is going to happen?
Where we typically focus
Page 8 |
This continuum is perhaps easier to understand when you think about what we actually do with the data
Page 9 |
This basic categorization works well
Hindsight Insight Foresight
ReportingWhat happened?
AnalysisWhy did it happen?
MonitoringWhat’s happening now?
PredictionWhat might happen?
PrescriptionHow might is happen?
5 sec
Analytics is business driven and technology enabled
Page 10 |
What is Big Data?
► Big data represents data sets that can no longer be easily managed
or analyzed with traditional or common management tools, methods,
and infrastructure.
► Heterogeneity of the data, speed at which it is growing together with
the ever-increasing volumes makes the data - “Big Data”.
High
Volume
Page 11 |
Sources of Big Data
Mobile SensorsSocial
Media
Electronic
Payments
Video
Surveillance
Video
Rendering
Smart gridsGeophysical
exploration
Medical Imaging
Gene
Sequencing
LOGO
Page 12 |
Data sources concealing fraud
Source: Gartner Research
80% Unstructured
Data
Text
Graphics
Presentations and spreadsheets
Unstructured data
20% 80%
CRM
Databases
Accounting systems
Structured data
► Journal entry analysis
► Transactional testing
► General ledger analysis
Page 13 |
Applying Analytics…start with Business Questions…
Page 14 |
Internal Audit AnalyticsWhat is it and why now?
There are several environmental and technological factors that are driving the need for IA to address the issue of analytics:
Big Data are the massive and varied amount of data existing in and relevant to the Business. Related risks
cannot be effectively addressed using traditional methods
There is expanding focus on behavioural drivers and audit response frameworks that tailor audit scope to both
nature and severity of risks
Focus on issue based audits, thematic audits, key advisory reviews including material capital programs, mergers
and acquisitions, etc.
IA needs to respond to business processes are becoming increasingly complex (and in some cases automated)
as the technology landscape progresses.
As the pace and volume of transaction increase (especially in ecommerce) there is an emerging need for
businesses to respond more rapidly to control failures .
Page 15 |
► Testing results are analyzed
and summarized
► Audit reports are finalized
including results of internal
audit analytics
Results Interpretation
An integrated approach between IA and Analytics
Planning Pre-audit Assessment Testing Reporting
Analytics Integration Risk Diagnostic Analytics Testing
► Analytics tests are determined based on audit objectives / scope
► Analytics design documents are prepared and validated with the core audit team
► Scripts are developed and
preliminary results are
generated / analyzed
► Risks are validated / refined
and analytics tests are
updated as appropriate
► New logic is designed for any new procedures determined in risk diagnostic
► Ad-hoc analytical requests are managed and results are generated for field team
► Final analytical results are
analyzed and integrated into
audit reports
► Analytics debrief session is
held and feedback summary
is completed, including
evaluation metrics
► Objectives and scope of audit are determined
► Audit program and integrated test procedures are designed
► Documentation
walkthroughs are performed
and pre-audit analytics are
analyzed
► Test procedures are refined
based on risk assessment
results
► Audit programs are executed and test results are documented
► Ad-hoc analytical requests are submitted as needed
Core Internal Audit
Internal Audit Analytics
Page 16 |
Benefits of using analytics to IA
IA
Increased efficiency
Depth of Insight
Single Source of
truth
Improved control
• Audits are more efficient where testing through the system Vs around the system
• Absolute insight into what individuals are doing, how processes are really being performed across the business
• Use of correlations. e.g. for banks between interest and underlying position
• Deep dives on portfolios
• Enhancing risk assessments by using analysis of movements to identify higher and lower risk areas
• Full population testing ensures that the full extent of control failures can be quantified and the impact understood,
• One point to refresh and maintain —reduces cost and effort
• Leverages your existing data to deliver insight and value
Page 17 |
The Internal Audit Frameworkwith fully integrated analytics components
The successful application of analytics within Internal Audit is reliant on having the analytics Define, Produce and Consume components fully integrated within the IA framework
Page 18 |
Overview of Internal audit analytics maturity
Define
Produce
Consume
Ad hoc analyticsRoutine planed analytics
Continuous Auditing
• Analytics definition completed by individual auditors, and are not integrated with Internal Audit methodology
• Analytics are produced by Auditors at or near to execution of fieldwork
• Auditors review outputs and try to apply finding to risks and control objectives
• Some standard analytics defined
• Auditors can choose to request analytics or not
• Analytics platform centrally developed
• Analytics executed on an as needed basis
• Auditors follow standard procedures to review outputs to address specified process steps
• Methodology includes Control Risk Assessment and analytics integrated into the workplan at a workstep level; Workplan evolves
• IA Analytics platform is seamlessly integrated into work programs
• Standardised analytics with limited manual intervention
• Auditors convert analytics to insight and provides business insights
Integration with Control Monitoring and other assurance functions
• Continuous Auditing and Control Monitoring objectives aligned
• Continuous Auditing and Control Monitoring delivered from a consistent analytics platform
• Auditors focus on reviewing all control activities
Transforma-tional internal audit
• Risk management embedded in business strategy
• Assurance and performance improvement intelligence delivered seamlessly
• IA is a strategic advisor to the business
Page 19 |
Typical challenges and key considerations
Define
► Complex organisational processes and structures exist within the business which is a challenge to scoping and design of data analysis
► Knowledge of the business processes is not readily available within the organisation
Produce
► Difficulty in accessing data from the ERP systems (mixture of legacy and customised ERP systems
► Data extraction can be time consuming
► Poor data quality when data is extracted from the system, or uncertainty over dataintegrity
Consume
► Auditors struggle to convert the data to insight (lack skills to interpret analytics results)
► Auditors are unclear on what activities they can stop doing
► CRA and analytics looking over periods (trend analysis) will represent new activities (and require new skills) for Auditors
Governance and Strategy
► Internal auditors may have the technical skills but lack industry, business process, issue or other experience
► Return on investment can be unclear
Framework Typical challenges
Page 20 |
Potential Uses Analytics
• Fraud detection – anomalous ATM activity; irregular teller transactions, etc.
• Analysis of financial statement figures for reasonableness – Testing balances and using predictive analysis and thresholds
• Data conversion testing
• Mining data to unearth discrepancies or anomalous actions or transaction – banks have thousands (sometimes millions) of transactions daily
• Continuous monitoring
Page 21 |
Typical Workflow One Test (One Excel File)
Client
Report
Risks:
► There is no way for
accurately time control
because of possible Excel
hang
► Repetitive operations due to
errors or data changes
► Incorrect data display in
Excel because of a large
amount of data
► Difficult to control / verify the
correctness of data
f(x)
f(x)
2 hours for:
one test in
one file
Data Preparation
Data Processing
► manually ► Macro in Excel (VBA)
► in Excel
Directory
Page 22 |
How We Analyse Data Now – with the right tools
Instead of repetitive filters, calculations, mapping a number of 16 files with other Directories
now we can use one repeatable and sharable workflow for data
blending – which takes 6-7 minutes
Benefits:
► Easy to control
► Work with multiple data
sources easier
► Understanding of time
needed for processing
► Elimination of errors
made by a person
► Opportunities for data
visualization
Page 23 |
Typical Architecture
Page 24 |
The popular auditor’s tool - ACL
Access and analyze complete data
populations with easy and 100%
coverage for superior assurance
Visualize, widely share and act on
information uncovered in analysis
testing across the business
Web client and add-in for Microsoft Excel®
designed for working with data results
produced by AX
Enterprise
Data
SQL
ERP
Page 25 |
There are many other analytic enablers
• Tableau
• Spotfire
• R
• Alteryx
• Ideas
• Monarch
• Etc.
• The key is to define an architecture that suits your business and IA objectives!!!
Page 26 |
Our Workflow with Alteryx -
Input Data Output DataData Processing
Page 27 |
DEMOS
Page 28 |
Analytics tomorrow
By 2018…
Most stand-alone self-
service data preparation
offerings will either have
expanded into end-to-
end analytical
platforms or been
integrated as features of
existing analytics
platforms
Smart, governed,
Hadoop-based, search-
based and visual-based
data discovery will
converge in a single
form of next-generation
data discovery that will
include self-service
data preparation and
natural-language
generation
Most business users
and analysts will have
access to self-service
tools to prepare data for
analysis as part of the
shift to deploying
modern BI platforms
Page 29 |
How companies feel about analytics (US)
89%Percentage of organizations that agree that change management is a barrier to realizing value
12%Percentage of organizations that describe their analytics maturity as leading
66%Percentage of organizations that are investing $5m+ in analytics
78%Percentage of organizations that agree big data and analytics is changing the nature of
competitive advantage
Data analytics impact survey
Page 30 |
Questions
Presentation title
Page 31 |
Are you an intelligent enterprise?
Thank you.