AI & Co Potential & Implications for Finance...
Transcript of AI & Co Potential & Implications for Finance...
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AI & Co – Potential & Implications for Finance Leaders11 January 2018
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Panelists:
Timo Elliott
AI Expert and Corporate Evangelist
SAP
Timo Elliott is an innovation evangelist and passionate advocate of digital
transformation, analytics, and artificial intelligence. He has presented the latest
digital trends to business and IT audiences in over fifty countries around the
world and his articles appear regularly in online pu...Full Bio
Lars Sudmann (Moderator)
Council Director, CFO Business Unit Council
The Conference Board
Lars Sudmann held various management roles in Finance, for instance as CFO
Belgium of Procter & Gamble. He is the principal of Sudmann & Company, a
consulting firm specialized in strategy execution and change
leadership. Additionally, he is a lecturer on leadership and high...Full Bio
Finance of the future
?
Technology Business Social
1. A.I., Predictive Analytics & Co2. Data & Systems Management 3. RPA & Co.
4. Innovation interaction5. Digital Biz Models6. Entrepreneurial Thinking7. Volatility & Risk
8. Real time & creative analytics9. Data storytelling10. Style flexibility
New Skills & Focus Areas for the Finance of the Future
Source: Own Assessment, Workshop results with Heads of Finance
8
AI Humor
Shiny AI
How much involved were you already in pilots, tests or full fledged execution of AI & Co.?
What’s next?
© Timo Elliott
Timo Elliott, Innovation Evangelist
Artificial Intelligence: The Potential and Implications for Finance Leaders
13
Artificial intelligence
AI is a “sociotechnical construct” indicating machine capabilities which solve complex tasks that were
recently only possible by humans (equally well or better)
Technical disciplines that
solve business problems
through the extraction of
knowledge from data.
Deep learning
Machine learning
Data Science
Big Data
Advanced
Analytics
Predictive
Analytics
Data
mining
14
Why now?
Big DataAlgorithms Hardware
15
Algorithms are taking over
Voice transcription Lip reading Image Descriptions
16
What’s different with machine learning?
Classical software must
be programmed
Humans learn from
experience
Machine learning learns
from data
17
Data Training Inference
Apply
model
Services(such as invoice processing,
profile matching)
…and more
Applications (such as cash application)
Text
Image
Video
Speech
… and more
Train
model
Prepare
data
Capture
feedback
How does machine learning work?From data to insights
18
In the future, all processes will be “self-improving”
Do your financial processes
improve automatically over time?
19
40% 70% 94%
20
Machine learning everywhere
Source; McKinsey & Company
Machine learning
has broad potential
across industries
and use cases
21
▪ Automating end-to-end processesIncrease efficiency and reduce costs
▪ Detect and prevent Detect and rank information out of Big Data
▪ PredictDerive knowledge from historical information to increase
the accuracy of predictive scenarios
▪ Proactive context-sensitive supportDigital assistants boost productivity of financial experts
Machine learning for financeChanging the world of finance by adding intelligence to applications
Automate
PredictDetect
Assist
22
Big opportunities
Source: http://gartner.com/SmarterWithGartner
“In 2018, half a billion users will save two hours a day
thanks to AI-powered tools.” Gartner
23
Finance of the futureInverting the effort pyramid through automation
Strategy
and growth
Keeping the
lights on
Compliance, efficiency,
and business performance
Strategy and
growth
Keeping the
lights on
Today 2020
Automation
“Up to 70% of finance tasks are potentially automatable
with next-generation technologies”(McKinsey 2016)
24
Example: accounts receivable teams
Joni Chen
AR accountant
Lump
sum
Missing infoDiscounts
Exchange
Customer
call
???
Payments
25
Intelligent Invoice Matching
History
Payments
Invoices
Matching proposals
Intelligently learn matching criteria from your history
and automatically clear payments.
Machine Learning
Improves days sales
outstanding
Allows shared services to
scale as the business
grows
Empowers finance to
focus on strategic tasks
and service quality
26
Automatic extraction of information from invoices
27
Companies with 10,000 employees
and more process 300,000+ invoices
every year
A subset of these needs to be corrected,
because of formal or content errors
Historical data is used to identify
patterns for invoices that need
corrections
These patterns are used to warn the
approver proactively
Intelligent invoice correctionProactively improve invoice accuracy
28
Automatic extraction of information from employee expenses
29
Record
to Report
Procure
to Pay
Order
to Cash
▪ Eliminate manual FI/CO reconciliation efforts and make business self-service access
to data easier and more intuitive
▪ Improve forecast accuracy, accelerate and automate the financial close along with lower
compliance and auditing costs
▪ Significantly simplify and automate the interaction between buyers and suppliers through
digitizing the exchange of information and achieve high invoice automation rates
▪ Automate core Accounts Receivable processes like Credit Management, Dispute
Management and Cash Application
Plan to
Forecast
Using AI to help automate End-to-End processesIncrease efficiency and reduce costs
30
▪ Automating end-to-end processesIncrease efficiency and reduce costs
▪ Detect and prevent Detect and rank information out of Big Data
▪ PredictDerive knowledge from historical information to increase
the accuracy of predictive scenarios
▪ Proactive context-sensitive supportDigital assistants boost productivity of financial experts
Machine learning for financeChanging the world of finance by adding intelligence to applications
Automate
Predict
Assist
Detect
31
Fraud investigators can detect unknown fraud patterns and reduce false
positives leveraging their company’s investigative history – without expert
knowledge in data science and algorithm tuning.
▪ Detection of new fraud patterns
▪ Reduction of false positives
Predictive detection methods allow your business analysts and fraud
investigators to
▪ Automatically detect and rank attributes within classified data that
positively correlate with fraudulent cases;
▪ Incorporate them with existing detection methods into new fraud
management strategies.
Business integrity screeningDetect and rank information that positively correlates with fraud
Detect Fraud
Reduce false positives
Save money
Fraud Management Team
32
Assist
Detect
▪ Automating end-to-end processesIncrease efficiency and reduce costs
▪ Detect and prevent Detect and rank information out of Big Data
▪ PredictDerive knowledge from historical information to increase
the accuracy of predictive scenarios
▪ Proactive context-sensitive supportDigital assistants boost productivity of financial experts
Machine learning for financeChanging the world of finance by adding intelligence to applications
Automate
Predict
33
Augmenting financial analysis with artificial intelligence Bringing together actuals, forecast and simulation to uncover market trends before they happen
Cleanse and
match data from
different sources
Spot outliers, perform
forecasts, and
determine causality
Share and
operationalize data
more intelligently
Run what-if analysis
and zero-in on key
influencers for any
business challenge
34
Predictive accountingAccelerate accounting processes by machine learning powered forecasting
Create a common view
of all financial &
operational data
Predicts key
accounting KPIs & how
they impact key metrics
Provides easily
consumable reporting
Anticipates the impact
of currency fluctuations
for the end of a period
Optimize processes to close the financial books at the end of every fiscal period, and
reduce costs, cycle times, and error rates.
Data of
former
period end
positions
ML used to forecast
costs, revenues &
recurring costsMachine Learning
35
Constantly-updated predictive key performance indicators
Group
Prediction
36
Predictive accounting vision
37
DetectPredict
▪ Automating end-to-end processesIncrease efficiency and reduce costs
▪ Detect and prevent Detect and rank information out of Big Data
▪ PredictDerive knowledge from historical information to increase
the accuracy of predictive scenarios
▪ Proactive context-sensitive supportDigital assistants boost productivity of financial experts
Machine learning for financeChanging the world of finance by adding intelligence to applications
Automate
Assist
38
Digital enterprise assistants and self-service finance
Business context awarenessUnderstanding the business context, and pro-actively suggesting
solutions using predictive functionality
Conversational user interfaceConversational interface that uses natural language processing
functionality to create a human-like experience
Cross applicationsAllows seamless transition across platforms; start a task on a
mobile device and continue later, on a desktop or vice versa
Self learning Using machine learning functionality to gain knowledge based
on historic data, experience, and take action in response to new
or unforeseen events“By 2020, the average person
will have more conversations
with bots than with their spouse”
Gartner
39
Your data is your most important asset
▪ Investments in systems to ensure high-quality, consistent data will be even more
amply rewarded
▪ Invest in gathering leading “signal” data (e.g. social sentiment data, or foot-traffic
statistics) to augmented “lagging” finance data
It’s about people
▪Automation is a huge opportunity, but it’s also about “augmented intelligence” –
displacing work, not replacing workers
Watch out for side-effects
▪You’re delegating more decision-making to machines: make sure there’s still
oversight and auditing
New ways of working
▪ Implementing AI in new ways requires new skills – your strategic partners can
help!
Implementing AI: requirements for success
Thank you.
Contact information:
Timo Elliott
VP, Innovation Evangelist
SAP
timo.elliott@sap
@timoelliott
Poll: Are you involved in training, upskilling yourself in the field of AI?
High
Low
Low High
Data
Matu
rity
Level
Possibilities from A.I., Predictive Analytics & Co
Dual Strategy for Data & Systems Maturity
Where do we stand?
1. What is the best tool we canuse now?
2. How can we move up to tohigher data maturity levels?
Source: Sudmann & Company
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