The document discusses a joint collaboration between researchers and Worldline to develop real-time machine learning systems for credit card fraud detection. It highlights the challenges involved, such as handling big data, non-stationary and noisy data, and adapting to novel fraud patterns. The initiative aims to create a scalable and effective solution for identifying fraudulent transactions.

1. Adap%ve
real-­‐%me
machine
learning
for
credit
card
fraud
detec%on
h6p://mlg.ulb.ac.be/
• Dal
Pozzolo
A.,
Johnson
R.,
Caelen
O.,
Waterschoot
S.,
Chawla
N.V.
and
Bontempi
G.
(2014)
Using
HDDT
to
avoid
instances
propaga%on
in
unbalanced
and
evolving
data
streams,
IJCNN
2014.
• Dal
Pozzolo
A.,
Le
Borgner
Y.A.,
Caelen
O.,
Waterschoot
S.
and
Bontempi
G.
(2014)
Learned
lessons
in
credit
card
fraud
detec%on
from
a
prac%%oner
perspec%ve,
Expert
Systems
with
Applica%ons.
• Dal
Pozzolo
A.,
Caelen
O.,
Waterschoot
S.
and
Bontempi
G.
(2013)
Racing
for
unbalanced
methods
selec%on,
IDEAL
2013.
Publica%ons
Andrea
Dal
Pozzolo
-­‐
adalpozz@ulb.ac.be,
Olivier
Caelen
-­‐
olivier.caelen@worldline.com,
Serge
Waterschoot
-­‐
serge.waterschoot@worldline.com
and
Gianluca
Bontempi
-­‐
gbonte@ulb.ac.be
• Joint
collabora%on
between
MLG
-­‐
ULB
and
Worldline,
funded
by
Innoviris
“doc%ris”
programme.
• Worldline
is
a
leader
in
electronic
payment
services,
services
for
financial
markets
as
well
as
eCS.
It
has
more
than
25
years
of
experience
in
figh%ng
card
related
fraud.
• Goal
is
to
design
an
automa5c
systems
that
is
able
to
detect
fraudulent
pa6erns
in
transac%ons
in
real-­‐5me
and
compare
in
parallel
a
large
number
of
alterna5ve
models.
• Challenges:
big
data
(need
of
scalable
machine
learning
techniques),
non-­‐sta%onarity
and
noisy
data,
novel
pa6erns,
fraudsters
adapta%on
and
cost
func%on
modifica%on.