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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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