Advanced Analytics in Internal Audit and Compliance April, 23 2014 www.pwc.com
May 07, 2015
Advanced Analytics in Internal Audit and Compliance
April, 23 2014
www.pwc.com
PwC 2
Today’s Discussion
Key Trends in Risk Analytics Today
Trends we are seeing in the market, and defining the value proposition for Advanced Analytics in Internal Audit and Compliance
Analytics in Action
Retail case study and live demonstration of analytics through data visualization
Implementing a Successful Program
Key success factors to develop and sustain a data analytics program
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Key Trends in Risk Analytics Today
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Characteristics of analytics today
Visualization
Dashboarding
Trending & comparisons
Data
Financial, Operational
Structured, Unstructured,
Internal, External
Analytics; discovery and communication of meaningful patterns in data
Big data; collection of large and complex data sets
Speed and portability; available anytime, anywhere
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Major themes we’re seeing at our most successful clients
Data Connectivity
Direct access into enterprise-level data resources
Balanced Talent Mix Strong analytics capability at the core, with training for audit, shared service and business users
Self Service Capabilities w/ Visualization
Approachable and intuitive technology that can be used by the business
Collaboration
Integrated working environment with analysts and business stakeholders
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Quantifiable but not Always Tracked to Amounts:
• Improve operational
effectiveness • Reduce regulatory and
compliance risk exposure
• New policies and procedures enacted
Value Proposition
Qualitative: • Higher quality management
conversations • Risk focused testing • Visual focus on trends and
outliers • Adoption of analytics by
business users • Strengthened relationships
Quantifiable: • Reduce costs • Increase
revenue/profitability • Reduced BI
development cost by leveraging data visualization tools
• Reduced hours (for internal audit)
1. Increase risk coverage, quality, and business impact
2. Greater efficiency through ‘right time’ analytics
3. Better understanding of the business through analytics
4. Cultivate business relationships and drive deeper discussion on issues and opportunities
What are the benefits?
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Analytics in Action
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Case Study: Internal Audit Data Analytics – Retail Pricing Audit
Client Challenges Action Value Delivered
• A large retailer needed to
assess key risks and controls related to product pricing.
• Internal Audit incorporated the
use of data analytics into each phase of the audit life cycle.
• Gained an understanding of
potential anomalies and trends related to pricing.
• Identified key risk indicators (KRI)
• Developed targeted analytics around those KRIs that would identify potential exceptions.
• Developed dashboard’s to interact with the data and ‘audit by sight”
• Enabled Internal Audit to approach
audit of pricing process using a risk based approach, focused on risk indicators within the data.
• Collaboration with the business enabled by visual analytics
• Identified potential issues and
trends relating to pricing that were not initially apparent at the onset of the audit.
• Positively impacted perception of
Internal Audit by the business.
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Auditing Approach Leveraging Data Analytics
Business Development
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Foundation
Fieldwork
Quality
Reporting Planning
1. Foundation: Leverage data to identify key risks to be addressed through the Audit Plan
2. Planning: Plan and scope the audit to focus on high risk areas and newly identified risks
3. Fieldwork: Develop risk based and value added analytics for the audit
4. Reporting: Analyze results to quantify impact, and report findings utilizing data visualization
5. Quality: Monitor managements responses through Dashboards
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Issue
Action
I. Foundation: Risk Assessment
Annual quarter over quarter revenue decline
Analyze financial data to gain insight over declines in net revenue.
Internal audit identified product pricing as a key risk area
Impact Analyze key financial data and adjust their audit plan to address their high risk areas.
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Issue
Action
II. Planning: Project Risk Assessment
Varying gross margins across 3 retail channels
Utilized data visualization software to analyze the stores by retail channel.
Impact Identified limited controls around product price overrides/adjustments have been identified
Unexpected spikes in price overrides
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Issue
Action
II. Planning: Audit Scoping
Identify high risk stores affecting gross profit through the use of overrides
Utilized data visualization software to analyze the total price override amounts against gross profit to identify outliers and anomalies
Impact Identified stores that had negative gross profits or negative overrides Discovered that products can have price overrides that result in losses
Negative gross profit due to price
overrides
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III. Fieldwork: Targeted Analytics Testing
Instances where the override amount is less than the cost of the item.
Total amount and frequency of overrides by store and sales associate.
Total amount and frequency of overrides by override code.
Trending of overrides by store over the audit time period.
Override reason codes leading to negative gross
profit
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IV. Reporting: Confirm and Report Results
Internal Audit team determined there were no controls around overrides of product prices
Sales associates overriding to negative gross profit
Sales associates entering in negative override
amounts
Outlier of sales associate with potential unusual
activity
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Gross Profit vs. Override Amount by Sales associate
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Issue
Action
V. Quality: Remediation Follow Up
Monitor managements response to the findings of the pricing audit
Utilized data visualization software to leverage the analytics and dashboards created during the assessment, scoping, and testing phases
Impact Ability to continuously monitor the compliance in a consistent manner with minimal manual effort
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Implementing a Successful Program
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Core components of a data analytics strategy
There are five core components to consider when developing a data analytics strategy
• Consistent • Sustainable • Efficient
• Financial, Operational • Structured, Unstructured • Internal, External
Change
People
Data
Process Infrastructure & Tools
Business Value
• Ownership • Accountability • Talent Development
• Maintainable • Intuitive • Adaptable
• Collaborative • Timely • Relevant
Vision Acceptance
Mandate Funding
Relationships
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Measure your success
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Audit and business impact
Data
An
aly
tics
Ma
turi
ty
Low
High
Level 0
Initial / Developing
Level 1
Relevant
Level 2
Consistent
Level 3
Integrated
Level 4
Embedded
Level 5
Transformational
• Limited but growing
capabilities
• Ad hoc activities
resulting in
unpredictable and
inefficient
performance
• Success based on
individual
competence
• Capabilities
developed and
adopted
• Capabilities used to
drive audits
• Defined goals and
standardized
processes and tools
• Capabilities are well
developed and
practiced with
appropriate
governance
• Data sources are
readily available
• Activities begin to
become repeatable
and key metrics are
developed
• Core analytics
skillsets within 5-
10% of department
• Scale is achieved for
department specific
teams
• Improvement
methodologies are
implemented
• Monitoring occurring
for metrics and
controls
• Analytics risk models
being adopted by the
business
• Analytics changing
auditor behaviors
• New value
propositions
• Alignment to
consistent platform
that can be
leveraged across
lines of defense
• Game changing to
audit delivery and
value
• Capability limited
to very few
individuals
• Inconsistent
effectiveness
• Limited audit or
business value
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A phased approach
An iterative deployment methodology ensures stakeholder value is achieved and business needs are meet as conditions change
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Define a clear ,
strategic vision
Develop roadmap Execute pilot
Phased Roll- out
Assess solution
Analyze current/
future state
Refine & execute plan
Financial
• Expense reports
• pCard analytics
• P2P analytics
• Revenue financial controls
• Store risk and performance analytics
• Cost Allocation Re-performance
• FCPA/OFAC
• Country Risk Assessment
• Audit Planning
• Price Changes / overrides
• Inventory shrink analytics
• High risk product identification
• Trends by store, items, override category
• Supply Chain Movement ($ & Time)
• Inventory vs Sales
• Procurement / working capital analysis
Areas we commonly see client leveraging advanced data analytics…
Operational
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Key Considerations
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• Start small, think big, and evolve over time
• It’s not about the tool, it’s about capabilities
• Don’t underestimate the complexity of the data
• Analytics is an iterative process
• Need a strong analytics center
• Strong relationship and reputation are crucial
• Don’t underestimate change
• Stakeholder value can be enormous
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