Advanced Analytics for GRC

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Advanced Analytics For GRC:Breaking The LimitsTimo Elliott, SAP

[email protected] @timoelliott

© 2016 SAP SE or an SAP affiliate company. All rights reserved. 2

Agenda

Why Business Analytics?

Analytics Old and New

Big Data and the “4Vs” – Velocity, Volume, Variety, Veracity

Predictive Analytics and Artificial Intelligence

Using These Technologies to Transform GRC

Conclusion



Technology Priorities for 2016 and beyond

Rank Technology Trend

1 BI/Analytics2 Cloud3 Mobile4 Digitalization / Digital Marketing5 Infrastructure & Data Center6 ERP7 Security8 Industry-Specific Applications9 Customer Relationships

10 Networking, Voice, and Data Comms

Nine out ofeleven years2006 - 2016

ANALYTICS

#1


Business Priorities

What business areas need the most technology support?

Source: Gartner, August 2015Business Analytics


Business Analytics is the Number One Priority of Finance

Source: Gartner, August 2015

“The importance placed on risk management, profitability analysis and reporting, and business intelligence indicates that finance functions continue to want to leverage big data and analytics to broaden how they conceive, organize and perform traditional corporate performance management capabilities.”

2016 Finance Priorities Survey


Internal Auditors Are Also Turning To More Technology


There’s a Lot of Opportunity

Source: Cangemi, Michael. Staying a Step Ahead: Internal Audit’s Use of Technology, IIA Research Foundation, August 2015

Fewer than 4 out of 10 chief audit executives worldwide feel their departments’ use of technology is appropriate or better.


Audit Executives Want To Improve Their Big Data / BI Competency


TipInternal Audit

Management ReviewBy Accident

Account ReconciliationOther

Document ExaminationExternal Audit

Notified by Law EnforcementSurveillance/Monitoring

IT ControlsConfession

There Are Big Opportunities – E.g. Fraud

Most fraud is typically found without technology today

Source: 2016 Report to the Nations on Occupational Fraud and Abuse, Association of Certified Fraud Examiners

More often found by accidentthan by controls or monitoring!


What Do We Mean By Analytics? First, Reporting…

Purchase to PayCritical data fields Split requisitions and POsStale requisitions and POsSegregation of duties PO date after invoice dateInvoice number sequenceGoods received quantity vs. invoice quantityEmployee and vendor matches by name and

by addressDuplicate vendors (by name, address, bank

account number)Duplicate purchases (same vendor same

invoice number, same amount same GL account)

Travel and Entertainment/PurchasingCritical data fields (cardholder master,

expense, etc.)Invalid cardholder (no matching employee

or terminated employee)Duplicate cardholders (by employee ID or

address)Suspicious keyword in the transaction

descriptionDeclined and disputed transactionsSplit purchasesDuplicate purchases (same merchant

same amount)New cardholder watch list/cardholder

watch listGhost card activitiesEven/small dollar amount transactionsWeekend and holiday transactionsPotential duplicate reimbursements: e.g.

gas with mileage Spending limits on transactions (lavish

hotel stays, dinners, etc.)

PayrollCritical data fields (payroll master file)Duplicate employees (same bank account

or address)Employee status not matching the

termination dateHours worked vs. hours paidEmployee start date after paycheck dateTerminations within 14 days of hireInvalid pay rates (actual/calculated vs.

master file)Excessive gross payJob record deletions (data corrections not

using effective date)

Delivery quantity vs. sales order quantityShipment/sales order/price change by an

unauthorized employeeCash receipt vs. invoice amountShipment without a sales order

Order to CashCritical data fields (customer master, sales

order, etc.)Duplicate customers (on name or address)Segregation of duties Unauthorized/excessive commissions


More Automation: “Data Analytics Audits”

“We developed analytics around transactional data. A series of scripts were created to flag anomalous transactions, which would then would be subjected to audit procedures.

This allowed us to analyze 100% of a population and test the controls around the outliers. In some cases these were used as part of routine audits and in other cases these analytics were designed to highlight red flags for fraud and investigations.”

Randa SalehChief Audit Executive at Starwood Hotels & Resorts Worldwide, Inc.


Newer Opportunities: Data VisualizationRisk OccurrencesBy Quarter

254 Risks -24%

Controls TestingBy Status 23

0controls

Easy, self-service access to data with tools like SAP LumiraNow with predictive included!


Insert page title

First levelSecond level Third level

Use Analytics to Optimize Project Success


Cloud-based Analytics and Visualization

SAP BusinessObjects Cloud

SAP Cloud Identity Governance

etc


Spatial and Mobile Analytics


Graph Databases and Network Analysis

18© 2016 SAP SE or an SAP affiliate company. All rights reserved.

SAP Digital Boardroom

19© 2016 SAP SE or an SAP affiliate company. All rights reserved.

Along comes

BIG DATA“Vast new streams of data are changing the art of management”


Finance And Big Data – The Stereotype

43% OF CIOs believe that data is a valuable asset that is being squandered


IT Underestimates Finance’s Data Awareness

CIOs CFOs

23%

3%

CIOs CFOs

9%

52%

“Does your CFO know what Big Data is?” “Is data on the balance sheet with a monetary value?”

More collaboration and communication

needed!“No” “Yes”


Big Data: The Four “Vs”

VELOCITY VOLUME VARIETY VERACITY

Increasing amount of data generated,

ingested, analyzed and managed

Increasing speed at which data must be received, processed

and understood

Beyond traditional structured data

sources to “unstructured” data

The quality and accuracy of received

data


Can you respond to data requests in under a day?

Need to analyzedata more quickly

Data is hard tofind and understand

Only 12% 90% agree 58% agree“Finance executives recognize need for speed in data analysis – but few companies are able to deliver in real time.”

cfo.com research

Financial Velocity


Velocity: Removing Redundancy in Financial Applications

Result: a real-time view of information in the financial system RIGHT NOW

Live Business

Velocity


Savannah Cement

“We would discover fraud only after it had happened – at times, even weeks later”

Brian Wamwenje, CIO


Greater Speed Equals Better Business Partnership

Reliable or very reliable

Valuable or very valuable

Effective or very effective analysis

Well-aligned or very well-aligned with strategy

89%

76%

94%

62%

54%

43%

50%

25%

Do Not UseUse

Source: Grant Thornton, APQC FP&A report Influencing Corporate Performance with Stellar Processes, People, and Technology Feb 2015

Impact of rolling forecasts on business evaluation of finance department


Volume and Variety

Shift in Data Sources: Unstructured data growing is at 10x rate of structured data, but it can be hard to store and exploit using traditional IT systems.

INVOICES

Name Data Type Required?

COMPANY_NAME VARCHAR YES

INVOICE_ID DECIMAL YES

PURCHASE_DATE DATE YES

“Structured” Corporate Databases“Unstructured” Data – text,

documents, images, social, etc



AlertEnterprise Integrate large volumes of structured and semi-structured data from many different systems, in real-time


Mine text data to spot global supply chain issues


Veracity

DATA DEFINITIONS

METADATA

DATA INTEGRATION

DATA QUALITY

DATA AUGMENTATION

MASTER DATA

Data is >90% of the effort



Single source of truthConnect, clean & normalize all relevant data elements

Clean, Organize & Normalize


Real-world exampleConnect, clean & normalize all relevant data elements

Travel System Agency & Supplier.com

Credit cards

HR/hierarchy Expense


Descriptive:What happened?

Diagnostic:Why did it happen?

Predictive:What will happen?

Prescriptive:How can we make it happen?

Hindsight Insight Foresight

Analytics Maturity

SAN FRANCISCO – This is the year artificial intelligence came into its own for mainstream businesses

DATA HARDWARE ALGORITHMS

DATA SCIENCE

QUIZ.

These numbers were found in two expense claims. One is entirely made up. Which one?

EUR

12,-2.86,-

10.98,-69,-

29.30,-3,-

84,-119.84,-18.74,-1.94,-

27,-

EUR

93,-82.65,-18.46,-

72,-98.83,-7.36,-4.53,-

3,-8.32,-

48,-2.94,-

1 2 3 4 5 6 7 8 9

30.1%

17.6%

12.5%

9.7%7.9%

6.7% 5.8% 5.1% 4.6%

Benford’s LawDistribution of the first digit of real-world sets of numbers that uniformly span several orders of magnitude

DATA SCIENCE

QUIZ.

EUR

12,-2.86,-

10.98,-69,-

29.30,-3,-

84,-119.84,-18.74,-1.94,-

27,-

EUR

93,-82.65,-18.46,-

72,-98.83,-7.36,-4.53,-

3,-8.32,-

48,-2.94,-

These numbers were found in two expense claims. One is entirely made up. Which one?


1999 to 2009

“Greece shows the largest deviation from Benford’s law with respect to all measures. [And] the suspicion of manipulating data has officially been confirmed by the European Commission.”

Fact and Fiction in EU-Governmental Economic Data, 2011

Euro-Zone Economic Figures Submitted to European Union…


Putting Benford’s Law to work

Accounts payable

Estimations in the general ledger

Size of inventory among locations

Duplicate payments

Computer system conversion

New combinations of selling prices

Customer refunds

More data means greater statistical significance for multi-digit tests…

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 880

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

Spike at 49

Two first digits of number

Percentage

Benford’s Law expected

Real-Life Banking ExampleThe write-off limit for internal personnel was $5,000. It turned out that the officer was operating with a circle of friends who would apply for credit cards. After they ran up balances of just under $5,000, he would write the debts off…


Impact of Predictive Analysis

Effective

Valuable

Well-aligned

95%

87%

71%

76%

55%

30%

Do Not UseUse

Using advanced analytics in Finance results in better alignment, effectiveness, and value

Source: Grant Thornton, APQC FP&A report Influencing Corporate Performance with Stellar Processes, People, and Technology Feb 2015


The Big Opportunity


Changes and Opportunity

“The advent of analytics and artificial technology does not mean the end of human auditors. It means an end to painstaking checking and crossfooting of debit and credit entries and the beginning of auditing careers that thrive on understanding, monitoring, and improving analytical and cognitive systems”

World Economic Forum: “75% of respondents thought that 30% of corporate audits will be performed by Artificial Intelligence by 2025”

“Eventually, 80 percent of work involved with Sarbanes-Oxley compliance might be automated with analytics.”

Source: Deloitte


SAP FRAUD MANAGEMENT


SAP Fraud Management

• Leverage the power and speed of SAP HANA

• Integration into business processes

• Alert notification and management

• Minimize false positives with real-time simulations

• Ability to handle ultra-high volumes of data by leveraging SAP HANA

Detection based on rules and predictive analytics to adapt to changing fraud patterns

Detect fraud earlier to reduce financial loss

Prevent and deter fraud situations

Improve the accuracy of detection at less cost


SAP Fraud Management – Continuous learning Combine top-down & bottom-up approaches to maximize detection effectiveness

ExpertKnowledge

Database

StrategyDefinition &Calibration

Detection

Predictive Models

Manual Rules

Investigation PerformanceAnalysis

Top-Down

Bottom-Up


Pattern analysisPattern analysis - embedded or highly integrated in SAP HANA

Big Data Predictive AnalyticsText Search and Mining

Terabytes analyzed at the speed of thought

Compress large data sets into memory

Integrate insights from Hadoop analysis

Unleash the potential of Big Data

Intuitively design and visualize complex predictive models

Bring predictive analytics to everyone in the business

Native full text search

Graphical search modeling

UI toolkit

101010101010100010100110010110110


Sophisticated Pattern Analysis

(*) Based on SAP Predictive Analytics optional offerings


Situational Awareness

What do I need to do right

now?

Prediction

What can I expect to happen?

Suggestion

What do you recommend?

Notification

What do I need to know?

Perception

What’s happening

now?

Artificial Intelligence Means New Ways of Working…

Automation

What should I always do?

Prevention

What can I avoid?

Source: Ray Wang, Constellation Research

And new, artificial-intelligence powered applications…


Conclusion: Looking To The Future

“A new kind of audit requires a new kind of auditor. It will still be essential for auditors to have a solid foundation in the fundamentals. However, as the auditor’s role becomes more strategic and insightful, audit professionals will need a variety of enhanced skills including strong capabilities and experience with data analytics.”

Jon Raphael, Audit Chief Innovation Officer, Deloitte

Analytics, Big Data, and Artificial Intelligence allow new ways of working:• Easy, fast access to data, and clear visualizations of exceptions• Ability to examine every transaction, customer and vendor• Reduce manual audit cycles and free up time for more meaningful analysis • Allow the business to do monitoring, not just internal audit


Thank You!Timo ElliottVP, Global innovation Evangelist

[email protected] @timoelliott