Talk on Artificial Intelligence for business applications, delivered by Marlon Dumas at the Pärnu Finance Conference on 12 April 2019 https://pood.aripaev.ee/finantskonverents-2019

1. Marlon Dumas
University of Tartu
Institute of Computer Science
Demystifying AI:
From Technology to Business Value
Pärnu Finance Conference, 12 April 2019

2. AI in 1988
(Expert Systems)
Traceable
Decision
Rule-Based System
2

3. 3

4. AI in 2006
Small
Data
Traceable
Decision
Rule-Based Model
4

5. 5

6. 1998-2019: From Rules to Models,
From Small Data to Big Data
Small
Data
Big
Data
Untraceable
Decision
Traceable
Decision
Rule-Based System
Machine Learning Model
6

7. 7

8. AI in 2019
Robotic Systems
• Domestic robots / smart homes
• Surveillance systems
• Autonomous vehicles
Natural Language Processing
• Machine translation
• Question-answering systems
• Content robots
(Virtual) Assistants
• Recommender systems
• Chatbots
Automated Decision Systems
• Predictive & prescriptive monitoring
• Dynamic pricing
• Credit scoring
• Medical diagnosis / precision medicine
Machine Learning /
Deep Learning
8

9. Predictive Process Monitoring – Concept
9

10. Predictive Process Monitoring
• What is the next activity for this case?
• When is this next activity going to take place?
• How long is this case still going to take until it is finished?
• What is the outcome of this case?
• Is the compensation going to be paid? Or rejected?
10

11. Predictive Process Monitoring
Event
stream
Predictive
models
Detailed predictive dashboard
Alarm-based prescriptive dashboard
Aggregate predictive dashboards
Event
logDatabase
Enterprise
System
11

12. Event log
Classifier
/
Outcome
Predictions
Attributes
Traces
Predictive Process Monitoring:
Approach
Event log
Regressor /
structured
predictor
Future “paths”
prediction
AttributesTraces

13. Predictive Monitoring Example:
Debt Recovery Process at Estonian Company X
Debt repayment due Call the debtor Send a reminder Payment received

14. Debt repayment due Call the debtor Send a reminder Send a warning Call the debtor Call the debtor
Send to external debt
collection agency
Call the debtor
Send a reminder Send a warning Call the debtor Call the debtorCall the debtor
Call the debtor
Call the debtor
Call the debtor
Call the debtor Call the debtor
Predictive Monitoring Example:
Debt Recovery Process at Estonian Company X

15. Predictive Monitoring Example:
Debt Recovery Process at Estonian Company X
15
I. Teinemaa, M. Dumas, F. Maria Maggi, C. Di Francescomarino: Predictive Business Process Monitoring with Structured and Unstructured
Data. Proc. of BPM 2016, pp. 401-417.
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16. Predictive Monitoring Example:
Debt Recovery Process at Estonian Company X
16
Classifier
Encoding of
textual dataCase attributesEvents
Will repay in 60
days or not?
> 80%
accuracy

17. Apromore: Open-Source Process Mining &
Predictive Monitoring
/
process mining
algorithms
live data
historical data
process model
differences,
root-causes
conformance
report
performance
measurements
A ⇒ B
15
4,318
14
14
858
13
7,128
26
3,794
32
31
734 28
6,212
9
1,526
941
4,324
258
186
4,360
4,360
Created
4,360
Waiting for Support
12,587
Waiting for Customer
8,681
Resolved
5,023
Closed
4,360
Waiting for Internal
923
Escalation
42
Waiting for Approval
14
Waiting for Triage
31
Enterprise System
predictions
Apromore

18. • Predict process outcome (e.g. “Is this loan offer going to be rejected?”)
• Predict process performance (e.g. “Will this claim take longer than 5 days to be
handled?”)
• Predict future events (e.g. “What activity is likely to be executed next? And after that?”)
Event log
Training module
Training Validation
Predictor Dashboard
Runtime module
Information system
Predictions
Stream
(Kafka)
Predictive
model(s)
Event stream Event stream
Batched
Predictions
(CSV)
Apromore
Predictive process monitoring in Apromore
18

19. Case Study: Logistics Monitoring at Israeli Company X

20. • Given a history of actions/choices made by users, e.g. purchases made by
customers…
• ... I can tell you what is a given customer likely to want to do (next)
• Magic ball for:
• Cross-selling and up-selling
• Increasing customer engagement (e.g. news sites, media companies,
content providers)
• Personalized marketing, e.g. customized marketing campaigns
Recommender systems
22

21. 23
Kasutajad Tooted
Recommender systems: How?

23. Other case studies…
● Payment classification
○ Increased the percentage of payments that can be automatically
classified from 70% to 95% with an accuracy of 90%+
● Detecting Suspicious Financial Behaviour
○ Detecting suspicious blocks of transactions with accuracy of 90%+
● Fraud detection at Skype
○ Detecting fraudulent transactions earlier
● Propensity models
25

24. • Where to get the data?
• It might just be there in your CRM / ERP system (e.g. Dynamics)
• How much data is enough?
• Sometimes 500 – 1000 examples is enough for predictive models over
simple data
• For complex tasks (e.g. text data), sometimes 2000+ examples needed
• Quality is at least as important as quantity
How to go about it…
26

25. • Should I try to build AI development capacity internally? Should I get
consultants? Should I outsource? Should I buy a product?
• Building internal AI delivery capacity internally is large, somehow risky,
long-term investment.
• It is very easy to build bad AI solutions. Dozens of things need to be right to
get it to work.
• Beware of products – AI is not (yet) plug-and-play
• You probably need consultancy at least in the initial stages
• In any case: You need to build adoption internally
• Oftentimes, AI substantially changes your processes, you need strong
internal buy-in at all levels
How to go about it…
27

26. Levels of AI
29
• A – Brute force, use heaps of data,
optimize to death trying “all” moves
(with “smart pruning” of course)
• incl. combinatorial optimization, machine
learning (hyperopt), deep learning
• B - Selective optimization based on
trial-and-error and/or human input.
• Incl. Reinforcement learning, active learning
• C – Augmented Intelligence
• Humans & machines complementing
each other, learning from each other
https://www.theregister.co.uk/2018/05/10/heres_what_garry_kas
parov_an_old_world_chess_champion_thinks_of_ai/