The machines we use on a daily basis are getting smarter, and with AI shaping our world far more than many of us realize, how does it affect the online betting industry? How do you predict churn? Fraud? How do you identify problem gamblers? Find out how AI is shaping the online betting industry.

1. AI for optimizing customer journeys
in online betting
31.01.2019
Webinar:

2. Meet the presenters
Kamal Memon
ML Engineer
Matias Sjöblom
Technical Project Manager
2
Timo Luoto
Senior Advisor

3. Agenda
01 The evolution of AI
02
03
04
How AI is shaping the online betting industry
Prospects and possibilities in customer journey
Customer journey
05 Customer references or how we helped our customers
3

4. The evolution to AGI
1: Statistics
based
- Extremes,
means and
medians etc.
2: Expert
systems
- Predefined
automatised
scenarios
(statistics +
insight)
3: Algorithm
based (ML/AI)
- Full automation
and
self-improving
models
4: Artificial
General
Intelligence
(AGI)
Full
consciousness
4

5. 5
• Why is AI such a big topic in gambling:
• From the perspective of games: it all started with Deep Blue beating Kasparov.
• From the user perspective: the quantities of data.
• The goal of AI is to create a better service and better UX. The user gets to
be in the center again.
• Customer service initiatives - chat bots that actually work.
• Fraud attempts and service abuse can be identified faster so that service
providers can focus on their customers.
How AI is shaping the online
betting industry

6. What is the
process?
Data collection Cleaning/ normalization/
Feature Engineering
Finding the right model Getting the output
Frosmo front end solutions, CRM
systems, email automatization,
recommendations
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7. DATA
is the
new
OIL!
7
Like GA,
Frosmo can
collect data
from the site
Clients often
have large data
repositories just
sitting there
Combinations
of both
Data is key

8. How the machine learns

9. How the machine learns
9
Machine learning is a form of Artificial Intelligence.
A machine ‘learns’ by finding a pattern in the data available to it.
And when it sees a pattern, it adjusts the program (called a model) to reflect the what it found.
The more data underlying a pattern we expose to the machine, the better it gets.
When the model is properly fitted to the pattern, it begins to make predictions based on that pattern.

10. 10
For example; A simple case of early
identification of VIP users:
● If we consider just the deposits from a user,
it's easy to set a statistical criteria of for
example > 5000$ to consider a user VIP.
The decision boundary represent this
divide.
● But this criteria is not representative of real
world or is pragmatic in nature as there are
dozen of factors affecting a user's journey
to be a VIP.
● Hence when we have numerous features in
the data and fitting a decision boundary
becomes increasingly complex for
frequentist statistical inference.
How the machine learns

11. How the machine learns
Here comes Machine Learning! Dimensionality reduction, clustering, and classification:
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12. 12
How can we know
a model works?
Train/Test split
Cross validation
Predicted vs Actual
Evaluation
metrics:
Accuracy,
Precision and
Recall
Sanity tests
Manual
validation
A/B Testing

13. Real life examples
Collaborative filtering
(just like Netflix, Amazon, Zalando...)
Fraud detection
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Identify self-excluders (help them know their limits
and grow your business in a responsible way)
Next action during a session
Churn model
(make them fall in love again with your service
before they go try their luck at another providers)
VIP model (know you are spending your
money on keeping users better)
(Is there something in the behaviour that stands
out and needs further scrutiny?)

14. Early churn prediction
14
By definition,
churn represents
the act of a
customer leaving
the platform for
good. Specifically
in online betting
industry, the
churn rate is
relatively high.
Therefore it is of paramount
importance to address the
customer churn early in
order to enable the
businesses to employ a
successful prevention and
retention methodology.
Frosmo specializes in
early user churn
prediction.
Based on the
historical data of
the user base, we
implement
supervised
machine learning
models to learn
from the past
behaviour of the
users and predict
future probabilities
of any user leaving
the service.

15. Fraud detection
15
Experts have predicted online credit
card fraud to soar to a whopping $32
billion in 2020. Europay reports that
more than 20% of online financial fraud
cases occur on gambling sites
ML algorithms account the actual data
and this allows them to make real time
judgements, tackling all the fraud types
common in online gambling including
bonus abuse, promo abuse and account
hijacking.
Machine learning, with its ability to
learn from old data and change its
behavior on the basis of new data, is
the most potent tool against fraud.
Crucially, however, they also shield
honest players, providing a smoother,
undisrupted experience of the platform.

16. Questions?
16

17. Interested? Contact our Sales: sales@frosmo.com
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18. Thank you!
www.frosmo.com

19. The evolution to AGI
1: Product
statistics
based
- Most purchased,
most viewed,
bundle products
2: Expert
systems
- Predefined
automatised
scenarios (most
basic form of ML)
3: Algorithm
based (ML/AI)
- Collaborative
filtering
4: Artificial
General
Intelligence
(AGI)
?
19