big data analytics & machine learning next gen audit … · • the advantage you have...
TRANSCRIPT
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Big data Analytics & Machine learning – Next Gen
Audit approach.
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The Knowledge that we have
NO FULL Knowledge is the
GREATEST Knowledge of All
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Agenda
• Check in
• The Nokia Syndrome
• Who Moved my cheese!!!
• Understanding Big Data Analytics landscape & audit Impact
• Machine learning & its Impact on Audit
• Approach toward Analytics- Baby Step
• Check out
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Statement by Nokia CEO
During the press conference to announce Nokia’s
smartphone division being acquired by Microsoft,
Nokia CEO ended his speech saying this “we
didn’t do anything wrong, but somehow, we lost”.
Upon saying that, all his management team,
himself included, teared sadly.
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Why it happened at Nokia !!!
• The advantage you have yesterday, will be replaced by the trends of
tomorrow.
• To change and improve yourself is giving yourself a second chance……….
To be forced by others to change, is like being discarded.
• Those who refuse to learn & improve, will definitely one day become
redundant & not relevant to the industry. They will learn the lesson in a hard
& expensive way
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5%(3.7 Trillion USD)
of annual revenues of organizations are lost to fraud* ACFE
Why to understand Big Data & Machine
learning?
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7
Initial Detection of Occupational Frauds- Audit is failing the business!!
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Deloitte has been ordered to pay damages to the theatre company’s creditors after
an Ontario judge ruled the accounting firm failed to detect fraud at the company,
even though there were plenty of warning signs that something fishy was going on
in the 1990s.
The judgment is a very rare situation. For years creditors and investors have been
looking for ways to sue corporate auditors.
Latest…in the news!
Livent’ Auditor Deloitte ordered to pay $ 84.8 million for failing to detect fraud
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Fraud’s are nothing new!!!!!
Forty Possible frauds were listed in “ The Arthashastra by Kautilya”
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Check in :The Bits & Bytes
· 1 Bit = Binary Digit
· 8 Bits = 1 Byte
· 1024 Bytes = 1 Kilobyte
· 1024 Kilobytes = 1 Megabyte
· 1024 Megabytes = 1 Gigabyte
· 1024 Gigabytes = 1 Terabyte
· 1024 Terabytes = 1 Petabyte
· 1024 Petabytes = 1 Exabyte
· 1024 Exabytes = 1 Zettabyte
· 1024 Zettabytes = 1 Yottabyte
· 1024 Yottabytes = 1 Brontobyte
· 1024 Brontobytes = 1 Geopbyte
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Explosion of digital data: 90% of the world’s data created in the last
2 years
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tools & technologies
• Large volume of data
• External data
• Un-structured data
• Statistical modeling
• Machine Learning
• Real-time analysis
• Interactive visualization
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And then the BigData…
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And then the Big Data…
• What is BIG DATA?
Big Data refers to datasets that grow so large that it is difficult to
capture, store, manage, share, analyze and visualize with the
typical database software tools.
It is “massive (Volume)” data, in “different (Variety)” format, from
“different” sources, growing at “lightning(Velocity)” speed!!!
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We have to transform Unstructured Data into meaningful
insights
Flash Files
Images,
Archives
PDF Files
Log Files
Text Data
Transaction
Systems,
CRM, ERP,
BI, EPM,
DWH
Websites,
URLs,
Social
Media’s,
Forums,
Blogs,
Messengers
News Portals
E-Commerce
Transactional Data
Unstructured Data
Semi Structured Data
Structured Data
World Wide Web & Social Media
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Why business analytics?
Five big trends are driving the adoption of new approaches to business analytics. Taken together they
underscore an unforgiving demand for improved performance and a wake-up call for more disciplined risk
management.
Data Volumes & Technology Capacity
Regulations
Cost reduction
New Signals
Hidden Sight
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Machine learning
Aspect of AI: creates knowledge
Definition:
“changes in [a] system that ... enable [it] to do the same task or tasks drawn from the same population more efficiently and more effectively the next time.'' (Simon 1983)
There are two ways that a system can improve:
1. By acquiring new knowledge
acquiring new facts
acquiring new skills
2. By adapting its behavior
solving problems more accurately
solving problems more efficiently
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Opportunity for Machine learning within Audit & risk management
• Data supports fact-based analysis in investigation
• Automated techniques can cover up to 100% of the Data populations of the assesse.
• Regression Analysis based Machine learning for Audit population review
Data analytics has significant potential to be
Exploited in the Assessment & investigation Space
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Analytics In Investigation : Key Technology features
Pattern analysis
External data analysis
Machine Learning
Log MiningBehavioral
analysisInteractive
Visuals
Unstructured data
Real-time event
monitoring
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What type of questions analytics can answer
Hindsight Insight Foresight
What happened?
Where is the
problem?
What actions are
needed?
Why is this
happening?
What if these
trends continue?
What will happen
next?
What’s the best
that can happen?
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Tools and Technology
Representative analytical tools are outlined below — deciding which tool is best for the situation is key.
Excel/
AccessProductivity/Database SQL
ACLTraditional Audit Idea
SASStatistical/Analytics SPSS
TableauVisualization/Self
Organizing MapsViscovery
CognosBI Platforms Micro-strategy
• Most organizations use a combination of traditional audit and
statistical/analytics tools.
• Many of these tools may be available via existing ELA’s with key
vendors that IA can leverage.
• Engineer and deploy the necessary infrastructure and
accelerators (with appropriate IT support) such that the IA
teams can focus on analytics vs. routine tasks.
• Multiple ways to source the data — feeds, secure ftp, read only
access to views, etc.
• Acknowledge gaps with data — but with some diligence it is
possible to work through the data quality issues
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Six guiding principles – getting started with Advanced Analytics
Link your Goals and
objectives with Clear
Business Drivers
Know your data
Start Simple
Leverage Existing
Insights
Make it Actionable
and Measurable
Test and Learn
Establish a clear understanding of expected benefits from Analytics and ensure linkage to audit planning.
This will translate into clear objectives that drive the strategy, long term vision and surface the near term
opportunities.
Data is the key ingredient. It drives the insights that fuel the benefits from any Analytics program. It
is critical to understand both the data you have and the data you don’t have when determining how
and where you should begin. This knowledge also prioritizes efforts to collect what’s missing for
future analyses and enhancements to your Analytics program.
There is no need to boil the ocean at the outset. Starting with a targeted, ad hoc analytic program
will yield greater benefits in terms of speed to insights, learning and value. Take the time to learn
first and then deploy necessary capabilities across the enterprise later.
When possible, leverage existing analytics capabilities (look within the business) to jump-start the
program and build consistency with prior initiatives. These insights should also provide clues
related to the risks and business areas to start with.
Develop a plan to take action and measure results accurately early in the game. The organization,
systems and process that support execution must be able to take action with the insights that are
generated recommendations.
Be willing to test different approaches and areas of the business. Learn from results and try new
approaches based on what is learned.
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Expect the Unexpected…
Be Prepared!!!
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Questions and
Thank YouCA ANAND PRAKASH JANGID
+91 9620233516
www.quadrisk.com