IAES
Inter
national
J
our
nal
of
Artificial
Intelligence
(IJ-AI)
V
ol.
8,
No.
4,
December
2019,
pp.
391
398
ISSN:
2252-8938,
DOI:
10.11591/ijai.v8i4.pp391-398
r
391
Intelligent
cr
edit
scoring
system
using
kno
wledge
management
Bazzi
Mehdi
1
,
Chamlal
Hasna
2
,
El
Kharr
oubi
Ahmed
3
,
Ouaderhman
T
ay
eb
4
1,2,3
Department
of
Mathematics
and
Computer
Science,
Laboratory
of
Modeling,
Analysis,
Control
and
Statistics,
F
aculty
of
Sciences
A
ˆ
ın
Chock,
Hassan
II
Uni
v
ersity
,
Casablanca,
Morocco
4
Department
of
Mathematics
and
Computer
Science,
Research
Laboratory
in
Materials
Science
and
Modeling,
soult
an
moulayslimane
uni
v
ersity
,
Khouribg
a,
Morocco
Article
Inf
o
Article
history:
Recei
v
ed
July
25,
2019
Re
vised
Sep
11,
2019
Accepted
Oct
18,
2019
K
eyw
ords:
Credit
limits
Credit
scoring
Entrepreneurship
Kno
wledge
management
ABSTRA
CT
Promoting
entrepreneurship
among
Moroccan
young
people
has
been
challenged
by
a
plethora
of
economic
and
social
problems
in
the
aftermath
of
the
Arab
Spring.
Se
v
eral
go
v
ernment
programs
ha
v
e
been
set
up
for
young
entrepreneurs.
Thus,
f
aced
with
the
lar
ge
number
of
credit
applications
solicited
by
these
young
entrepreneurs,
banks
resorted
to
artificial
intelligence
techniques.
In
this
respect,
this
article
aims
at
proposing
a
decision-making
system
enabling
the
bank
to
automate
its
credit
granting
process.
It
is
a
tool
that
allo
ws
the
bank,
in
the
first
instance,
to
select
promising
projects
through
a
scoring
approach
adapted
to
this
se
gment
of
entrepreneurs.
In
the
second
step,
the
tool
al
lo
ws
the
setting
of
the
maximum
credit
amount
to
be
allocated
to
the
selected
project.
Finally
,
based
on
the
kno
wl
edge
of
the
bank’
s
e
xperts,
the
tool
proposes
a
breakdo
wn
of
the
amount
granted
by
the
bank
into
se
v
eral
products
adapted
to
the
needs
of
the
entrepreneur
.
Copyright
c
2019
Insitute
of
Advanced
Engineeering
and
Science
.
All
rights
r
eserved.
Corresponding
A
uthor:
Bazzi
Mehdi,
Department
of
Mathematics
and
Computer
Science,
Laboratory
of
Modeling,
Analysis,
Control
and
Statistics,
F
aculty
of
Sciences
A
ˆ
ın
Chock,
Hassan
II
Uni
v
ersity
,
Casablanca,
Morocco.
T
el:
212
615
10
65
13
Email:
bazzimehdi@gmail.com,
hasnachamlal@gmail.com
1.
INTR
ODUCTION
The
W
orld
Bank
classifies
Morocco
as
a
lo
wer
-middle-income
economy
[1].
The
country
is
characterized
by
a
gr
eat
potential
for
gro
wth
thanks
to
a
relati
v
ely
de
v
eloped
economic
di
v
ersification
compared
to
other
countries
in
the
Middle
East
and
North
Africa
(MEN
A)
re
gion.
Despite
the
global
financial
crisis
and
political
unrest
in
the
re
gion
stirred
by
the
Arab
Spring,
the
Moroccan
economy
[2]
achie
v
ed
a
gro
wth
of
3%
in
2018.
According
to
Doing
B
u
s
iness
2017,
Morocco
ranks
second
in
Africa
and
fourth
in
the
MEN
A
re
gion
[3].
In
se
v
en
years,
the
country
has
g
ained
60
ranks
in
this
ranking
mo
ving
from
128th
position
in
2010
to
68th
in
2017.
In
addition,
in
2018,
the
Micro,
small
(MSEs)
and
medium-sized
enterprises
(SMEs)
represent
99%
of
the
national
economic
structure
(94%
of
MSEs
and
5%
of
SMEs).
This
f
act
prompted
the
Moroccan
go
v
ernment
to
adopt
se
v
eral
measures
to
impro
v
e
the
b
usiness
climate
for
young
micro-entrepreneurs,
which
is
the
subject
of
this
study
.
F
or
the
purpose,
this
paper
pro
vides
a
set
of
re
gulatory
measures
(statutes
specific
to
MSEs,
auto-entrepreneurs,
compan
y
re
gulations),
administrati
v
e
measures
(reduce
formalities
and
processes
for
the
creation
of
companies),
and
tax
measures
(tax
e
x
emptions
and
benefits
for
ne
wly
created
companies),
without
for
getting
support
before
and
after
the
creation
and
the
financing
arrangements.
J
ournal
homepage:
http://ijai.iaescor
e
.com
Evaluation Warning : The document was created with Spire.PDF for Python.
392
r
ISSN:
2252-8938
Banks,
o
n
their
side,
pro
vide
support
for
these
young
entrepreneurs
to
realize
their
projects.
Thus,
f
aced
with
the
mass
of
credit
requests
solicited
by
these
young
entrepreneurs,
banks
are
encouraged
to
put
in
place
tools
to
fluid
the
procedures
for
handling
these
applications.
In
this
conte
xt,
we
propose
a
decision-
making
tool
bas
ed
on
the
kno
wledge
management
[4]
of
the
bank’
s
e
xperts
to
select
promi
sing
projects
and
propose
financing
formulas
adapted
to
their
needs.
At
first,
we
will
ta
ckle
the
concept
of
Kno
wledge
Management
(KM)
and
its
v
arious
f
acets
in
banking.
Secondly
,
we
will
present
the
process
follo
wed
to
acquire
the
kno
wledge
of
the
e
xperts
used
in
the
s
election
of
promising
projects
and
the
formulas
used
for
their
finances.
Finally
,
we
will
apply
to
a
credit
application
all
the
kno
wledge
acquired
from
b
usiness
e
xperts.
2.
KNO
WLEDGE
MAN
A
GEMENT
IN
THE
B
ANKING
SECT
OR
In
the
f
ace
of
an
e
v
er
more
stringent
re
gulatory
en
vironment
combined
with
strong
commercial
competition
and
the
rene
w
al
of
their
b
usiness,
banks
are
determined
to
put
in
place
measures
to
promote
the
management
of
their
or
g
anizational
kno
wledge.
Indeed,
according
to
[5]
in
their
article
”Financial
risk
and
the
need
for
superior
kno
wledge
management”,
t
h
e
transfer
of
kno
wledge
to
decision
mak
ers
beforehand,
a
v
ailable
access
to
information,
or
the
generation
of
ne
w
kno
wledge
on
the
e
v
olution
of
risk
management
requirements,
should
lead
to
more
ef
ficient
risk
management.
According
to
[6],
kno
wledge
management
is
the
management
of
kno
wledge
within
an
enterprise
through
specified
or
g
anizational
procedures
for
the
acquisition,
or
g
anization,
maintenance,
application,
sharing
and
updating
of
emplo
yees’
kno
wledge
to
impro
v
e
their
performance
and
create
v
alue.
In
their
article
”What
is
kno
wledge
management
for
banks”,
the
authors
[7]
propose
three
possible
readings
of
kno
wledge
management:
its
finality
,
its
place
of
production,
and
its
discipline.
Dif
ferent
f
acets
of
kno
wledge
management
as
sho
wn
in
Figure
1.
Figure
1.
Dif
ferent
f
acets
of
kno
wledge
management
The
first
axis
deals
with
the
finality
of
kno
wledge
management.
Indeed,
tw
o
dif
ferent
streams
are
required.
The
first,
in
a
logic
of
creation
of
the
ne
w
kno
wledge
to
inno
v
ate
[8].
The
second
in
a
replication
logic
of
good
practice
[9].
Banks
that
ha
v
e
adopted
the
kno
wledge
management
approach
ha
v
e
put
in
place
systems
for
kno
wledge
sharing
(acti
vity
reports,
procedure
manual)
based
on
Lotus
notes,
intranet
and
internet,
a
customer
relationship
management
system
[10]
or
softw
are
allo
wing
emplo
yees
access
to
the
digital
library
(e
learning)
to
de
v
elop
their
kno
wledge
of
the
banking
profession.
The
second
axis
of
analysis
is
the
place
of
production
of
kno
wledge.
In
f
act,
tw
o
places
of
conception
of
kno
wledge
oppose
one
another
.
The
first
is
e
xternal
[11]
adv
ocated
by
the
a
u
t
hors
who
c
ombine
customer
relationship
management
with
kno
wledge
management.
It
is
a
question
of
kno
wing
the
profiles
of
the
bank’
s
customers
in
order
to
of
fer
those
customized
products
and
services.
The
second
place
is
internal
[12]
and
IJ
AI,
V
ol.
8,
No.
4,
December
2019
:
391
–
398
Evaluation Warning : The document was created with Spire.PDF for Python.
IJ
AI
ISSN:
2252-8938
r
393
concerns
the
kno
wledge
mobilized
in
or
g
anizational
practices.
It
in
v
olv
es
initiating
a
process
of
e
xchange
and
brainstorming
between
emplo
yees
to
create
and
transfer
kno
wledge
within
the
bank.
The
third
axis
of
analysis
identifie
s
the
users
of
the
concept
of
kno
wledge
management
within
the
bank.
In
f
act,
tw
o
major
department
ha
v
e
been
identified.
It
is
primarily
the
information
systems
department
[13]
whose
objecti
v
e
is
to
put
in
place
a
system
that
allo
ws
the
process
of
creation,
collecti
on,
or
g
anization,
access
and
use
of
kno
wledge
to
be
automated
as
much
as
possible.
The
second
department
that
uses
kno
wledge
management
is
the
human
resources
.
The
latter
is
interested
in
the
management
of
job
forecasts
and
the
creation
of
a
f
a
v
orable
conte
xt
for
learning
and
kno
wledge
sharing
within
the
bank
[14].
In
the
follo
wing,
we
aim
to
pro
vide
risk
management
function
within
a
bank
with
a
credit
decision
support
system
whose
purpose
is
to
replicate
good
practices
in
the
selection
and
financing
of
young
entrepreneurs.
3.
FUNCTION
AL
AN
AL
YSIS
OF
THE
CREDIT
DECISION
SUPPOR
T
SYSTEM
(CDSS)
The
CDSS
system
(which
we
propose)
is
a
decision
support
system
which,
follo
wing
the
capitalizat
ion
of
the
e
xpert’
s
reasoning,
will
allo
w
the
selection
of
the
good
projects
of
the
young
entrepreneurs,
generate
credit
lines,
their
amounth
and
the
adequate
guarantees.
The
tool,
as
sho
wn
in
Figure
2,
will
be
able
to
combine
se
v
eral
quantitati
v
e
criteria
(model
for
the
assignment
of
scores
and
calculation
of
credit
limits)
and
qualitati
v
e
criteria
deduced
from
the
reasoning
of
b
usiness
e
xperts
follo
wing
se
v
eral
meetings.
The
system
is
a
response
to
se
v
eral
needs
from
a
set
of
potential
users
(in
bold):
Figure
2.
Description
of
the
credit
decision
support
system
a.
First,
the
system
will
allo
w
the
b
usiness
entity
;
In
particular
,
account
managers
gi
v
e
concise,
ef
ficient
and
reflecti
v
e
guidance
to
customers
by
focusing
more
specifically
on
credit
lines
that
are
appropriate
to
their
needs.
b
.
The
system
directly
serv
es
the
entr
epr
eneur
,
who
will
not
ha
v
e
to
w
ait
for
ef
fecti
v
e
advice
b
ut
will
be
serv
ed
in
a
v
ery
precise
and
timely
manner
,
which
will
allo
w
him/her
to
sa
v
e
time
and
e
v
en
stronger
confidence
in
the
services
of
the
bank.
c.
The
error
is
human,
and
this
is
one
of
the
main
constrai
nts
that
the
system
will
a
v
oid
to
an
y
risk
analyst
,
since
the
tool
will
impro
v
e
the
risk
management
of
an
y
application
by
being
more
automatic
and
follo
wing
a
well-chained
process.
The
entity
in
char
ge
of
the
Risks
will
be
able
thanks
to
this
tool
to
retrace
all
the
stages
on
which
it
based
itself
to
gi
v
e
its
decision
concerning
the
credit
limits
and
the
guarantees
solicited.
d.
The
system
can
also
serv
e
the
Risk
Management
entity
by
a
v
oiding
forcing
and
by
f
acilitating
the
writing
of
the
appropriate
reports
to
each
application
thus
allo
wing
a
f
acility
of
risk
management.
e.
V
oluntary
departures,
resignations
or
retirements
can
influence
the
proper
approach
of
the
bank’
s
services
and
gi
v
e
rise
to
a
great
loss
of
kno
wledge
and
e
xperience.
The
bank
will
be
forced
to
train
new
r
ecruits
ne
w
recruits
and
thus
lose
the
kno
wledge
that
has
been
b
uilt
up
o
v
er
time
with
great
e
xperience.
The
Intellig
ent
cr
edit
scoring
system
using
knowledg
e
mana
g
ement
(Bazzi
Mehdi)
Evaluation Warning : The document was created with Spire.PDF for Python.
394
r
ISSN:
2252-8938
system,
in
this
case,
will
be
able
to
capi
talize
and
unite
this
reasoning
to
become
a
pedagogical
tool,
which
wil
l
mak
e
it
possible
to
identify
a
well-b
uilt
reflection
and
a
v
oid
the
loss
of
the
e
xperiences
of
former
collaborators.
4.
THE
ARCHITECTURE
OF
THE
CDSS
SYSTEM
4.1.
Description
Figure
3
sho
ws
the
architecture
of
the
credi
t
decision
support
system
(CDSS)
allo
ws
us
to
outline
the
o
v
erall
design
of
the
system
by
describing
its
essential
components.
The
CDSS
system
will
recei
v
e
in
the
first
place
all
the
inputs
in
particular
,
the
kno
wledge
of
the
b
usiness
e
xpert
resulting
from
the
intervie
ws
and
meetings
as
well
as
the
data
subtracted
from
the
internal
system
of
t
he
bank
(Project
data,
en
vironment,
Profile
of
the
project
holder
...).
The
data
cited
will
serv
e
as
a
kno
wledge
base
(f
acts
and
rules)
to
an
inference
engine
that
serv
es
as
a
receptacle
and
allo
ws
to
map
the
reasoning
mechanism,
then
to
ha
v
e
a
graphical
interf
ace
that
the
non-e
xpert
and
e
xpert
user
will
use
to
display
the
v
arious
proposals
for
e
xits
to
kno
w
(lines
of
credit,
their
limits
and
the
appropriate
guarantees).
The
system
has
a
tw
ofold
meaning
since
the
non-e
xpert
user
can
inte
grate
the
credit
request
at
the
graphical
user
interf
ace
(GUI)
that
will
be
recei
v
ed
by
the
inference
engine
to
feed
the
system’
s
f
act
base.
Figure
3.
Description
of
the
credit
decision
support
system
4.2.
The
CDSS
kno
wledge
base
The
CDSS
system
is
based
on
a
kno
wledge
base
that
brings
together
in
a
structured
w
ay
the
rules,
relationships
and
problem-solving
strate
gies
for
the
credit
application.
The
content
of
the
CDSS
kno
wledge
base
w
as
acquired
through
a
series
of
meetings
with
b
usiness
e
xperts,
through
intervie
ws
and
structured
meetings.
The
course
of
these
meetings
generally
consisted
of
identifying
each
rele
v
ant
element
and
asking
the
e
xpert
to
produce
the
rules
used
daily
.
The
kno
wledge
base
is
di
vided
into
tw
o
components,
a
f
act
base,
and
a
rule
base.
4.2.1.
The
fact
base
The
f
act
base
is
a
database
that
records
all
e
xisting
historical
data
and
information
in
the
bank’
s
internal
system,
which
will
e
xploit
the
bank
to
deri
v
e
a
concise
structure
of
quantitati
v
e
and
qualitati
v
e
data
[15].
In
our
article
[16]”
we
ha
v
e
de
v
eloped
a
qualitati
v
e
model
to
e
v
aluate
the
projects
of
young
entrepreneurs.
The
authors,
after
hundreds
of
hours
of
discussion
with
se
v
eral
stak
eholders
(risk
management
e
x
ecuti
v
es,
netw
ork
manager
,
etc.)
and
dealing
with
se
v
eral
e
v
aluation
models,
ha
v
e
identified
10
to
20
questions
from
a
starting
list
containing
100
and
about
200.
The
selected
questi
o
ns
co
v
er
tw
o
important
aspects
of
the
select
ion
of
young
entrepreneurs’
projects.
This
is
a
questionnaire
designed
to
assess
the
ability
of
the
young
entrepreneur
to
manage
the
project
and
a
n
ot
her
questionnaire
to
assess
its
feasibility
.
The
final
e
v
aluation
of
the
project
is
the
combination
of
the
”Entrepreneur”
and
”project”
scores.
The
follo
wing
equation
gi
v
e
the
final
score:
F
inal
s
cor
e
=
E
ntr
epr
eneur
scor
e
(62%)
+
P
r
oj
ectscor
e
(38%)
(1)
IJ
AI,
V
ol.
8,
No.
4,
December
2019
:
391
–
398
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IJ
AI
ISSN:
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r
395
The
authors
justify
the
o
v
erweighting
of
the
entrepreneur
block
by
the
f
act
that
a
good
e
n
t
repreneur
can
succeed
i
n
an
a
v
erage
project,
whil
e
a
less
trained
(or
less
supervised)
entrepreneur
can
f
ail
e
v
en
with
a
promising
project.
The
rating
scale
v
aries
between
0
and
100,
a
score
of
100
is
gi
v
en
to
the
best
couple
(Entrepreneur
,
project).
Finally
,
the
final
score
is
se
gmented
into
7
classes
as
sho
wn
in
T
able
1.
T
able
1.
Model
MasterScale
Classe
Probability
of
def
ault
A
2,10%
B
3,90%
C
3,93%
D
6,70%
E
10,00%
F
14,40%
G
17,70%
H
35,10%
I
46,20%
The
criterion
used
to
split
the
score
into
se
v
eral
classes
is
to
group
in
one
class
projects
with
the
s
ame
risk
profile
and
to
disperse
those
with
dif
ferent
risk
profiles.
Once
the
projects
are
selected,
the
question
of
their
financing
arises.
In
the
follo
wing,
we
will
present
the
rules
and
models
used
to
set
the
credit
limits
granted
to
young
entrepreneurs
carrying
selected
projects,
as
well
as
the
breakdo
wn
of
this
limit
by
type
of
product
(Cash,
discount,
consumption,
foreign
e
xchange).
4.2.2.
The
rule
base
a.
Setting
the
cr
edit
limit:
In
our
article
”Concentration
risk:
setting
credit
limits
in
loan
portfolios,
case
of
Morocco”
[17],
we
propose
an
analytical
method
which
will
allo
w
us
to
decide
for
a
contractor
carrying
the
project
selected,
the
amount
of
credit
limit
to
be
granted
to
it
according
to
its
risk
profile
and
the
risk
appetite
of
the
bank
about
this
entrepreneur
and
his
project.
The
model
is
as
follo
ws:
CC
e
=
PD
e
limit
e
+
(
V
ar
(99%)
E
L
)
limit
e
E
with:
CCe:
The
consumption
of
capital
allocated
to
this
entrepreneur;
PDe:
The
probability
of
def
ault;
V
ar
(99%):
V
alue
at
risk
that
presents
the
maximum
loss
that
the
credit
institution
is
lik
ely
to
incur
by
financing
selected
projects
with
a
probability
of
1%
o
v
er
a
one-year
horizon.
EL:
Expected
loss
(to
be
pro
visioned)
associated
with
the
funded
project
portfolio.
E
:
T
otal
amount
to
finance
the
selected
projects.
In
what
follo
ws,
we
will
present
the
rules
of
e
xperts
used
to
allocate
the
authorized
limit
to
a
project
leader
in
a
set
of
products
(Consumption,
e
xchange
.
.
.
)
adapted
to
his
needs.
b
.
Br
eakdo
wn
of
the
global
limit:
The
kno
wledge
base
of
the
e
xpert
and
his
e
xperience
will
allo
w
our
system
thanks
to
the
set
of
criter
ia
deduced
to
guide
us
to
w
ards
the
choice
of
lines
of
credit.
The
b
usiness
rules
and
the
kno
w-ho
w
of
the
e
xpert
will
mak
e
it
possible
to
set
and
calibrate
these
lines
of
credit
to
get
closer
to
the
authorization
or
the
limit
that
the
credit
institution
must
grant
to
the
cus
tomer
.
In
our
system,
as
an
illustration
after
the
intervie
ws
and
the
analysis
of
the
data
recei
v
ed
from
the
b
usiness
e
xperts,
we
ha
v
e
been
able
to
implement
some
criteria
allo
wing
the
choice
of
lines
of
credit
as
sho
wn
in
the
T
able
2.
Intellig
ent
cr
edit
scoring
system
using
knowledg
e
mana
g
ement
(Bazzi
Mehdi)
Evaluation Warning : The document was created with Spire.PDF for Python.
396
r
ISSN:
2252-8938
T
able
2.
Extract
from
the
rule
base
(1/2)
Acti
vity
of
the
entrepreneur
Need
of
the
entrepreneur
Import:
Customs
Economic
Re
gime
T
emporary
admission
Import:
do
wn
payment
method
and
deals
A
v
al
local
supplier
Import
Export
Lines
of
e
xchange
Sector
Construction
and
Public
W
orks
CPro
visional
/
final
security
and
Adv
ance
on
Mark
et
Discount
P
ayment
method:
commercial
bills
Escompte
Seasonal
acti
vity
Credit
companion
The
table
abo
v
e
reflects
the
reasoning
of
the
b
usiness
e
xpert
when
choosing
or
directing
a
contractor
for
a
line
of
credit.
At
the
le
v
el
of
this
table
we
appl
y
a
schematization
of
the
reasoning
of
the
human
brain;
(If,
then.
.
.
).
These
b
usi
n
e
ss
rules,
which
we
will
e
xplain
in
the
T
able
3,
allo
w
the
credit
analyst
to
deduct
the
amounts
that
the
credit
institution
should
authorize
for
each
product
line.
T
able
3.
Extract
from
the
rule
base
(2/2)
Credit
lignes
limit
amounth
guarantees
Pro
visional
Guarantee
=T
urno
v
er
soumissions
bank
guarantee
/
definiti
v
e
rate
payment
deadlines
/360
AS
Mark
et
=T
urno
v
er
administration
rate
payment
deadlines
/360
Escompt
=
T
urno
v
er
payment
mode
rate
commercial
bills
payment
deadlines
/360
is
in
itself
a
guarantee
Cash
f
acilities
1
mounth
of
the
turno
v
errr
the
pledging
of
a
b
usiness
b
usiness
the
pledging
of
treasury
bills
and
go
v
ernment
bonds
Collaterals
F
actoring
Untel
90%
of
the
bill
personnal
g
arantee
proof
of
the
operation
:
bills
AS
Commodities
Untel
80%
the
commodities
pledge
agreement
Insurance
Credi
Con
v
ention
Openning
F
ore
x
e
xchange
line
Untel
6
Mois
of
international
turno
v
erl
5.
EXPERIMENT
A
TION
5.1.
Description
of
the
case
As
an
illustration
of
the
application
of
our
decision
tool,
we
propose
the
follo
wing
case:
a.
An
entrepreneur
operating
in
the
construction
sector
,
who
forecasts
a
turno
v
er
of
12
000
000
MAD
and
who
hopes
to
carry
out
international
operations
(Import-Export).
b
.
The
compan
y
is
paid
up
to
20%
by
and
o
v
er
a
period
of
60
days,
up
to
50%
by
transfer
and
30%
by
endorsement.
c.
On
the
other
hand,
the
compan
y
pays
its
clients
up
to
20%,
30%
by
ef
fects
o
v
er
a
period
of
60
days
and
50%
by
ef
fects
o
v
er
a
period
of
90
days.
d.
The
compan
y
carries
out
a
seasonal
acti
vity
and
follo
ws
a
customs
economic
re
gime
and
bids
in
a
public
mark
et
up
to
70%,
the
share
of
the
administration
is
3%.
e.
The
compan
y
plans
an
international
turno
v
er
of
MAD
1,000,000.
After
inte
grating
the
pre
vious
data
into
our
system,
the
results
are
as
follo
ws:
5.2.
Choice
of
lines
and
cr
edit
limits
fixed
by
the
expert
T
aking
into
account
the
rules
specified
abo
v
e,
an
analysis
of
the
credit
application
independently
on
the
risk
profile
(mesured
by
probability
of
def
ault
[18])
of
the
client
and
the
risk
appetite
of
the
bank
to
w
ards
this
client
allo
ws
the
latter
the
follo
wing
the
set
of
products,
as
sho
wn
in
T
able
4.
IJ
AI,
V
ol.
8,
No.
4,
December
2019
:
391
–
398
Evaluation Warning : The document was created with Spire.PDF for Python.
IJ
AI
ISSN:
2252-8938
r
397
T
able
4.
Credit
limits
by
product
Proposed
products
Limits
(En
MAD)
%
Limits
(En
MAD)
without
risk
adjustment
with
risk
adjustment
T
emporary
admission,
IT
Documentary
remittance
documentary
credit
cash
discount
2
100
000
57%
1
640
838,21
Credit
companion
pro
visional
g
arantee
27
300
1%
21
330,90
definiti
v
e
g
arantee
27
300
1%
21
330,90
cash
adv
ance
to
get
commodities
39
000
1%
30
472,71
fore
x
e
xchange
line
500
000
14%
390
675,76
cash
f
acilities
1
000
000
27%
781
351,53
T
otal
amounth
autorised
3
193
600
100%
2
886
000
Ho
we
v
er
,
the
analysis
of
the
risk
profile
of
the
client
through
the
credit
scoring
model
[16]
a
nd
the
model
of
setting
the
credit
limits
[17]
allo
ws
us
the
follo
wing
results,
as
sho
wn
in
T
able
5.
T
able
5.
Risk
profile
of
the
client
and
its
etimated
limit
Entreprise
classe
Autorisation
Capital
limit
estimated
Gap
(limit
estimated,
Autorisation)
SME
C
3
193
600
200
000
2
886
000
-28%
The
credit
limit
calculation
model
authorizes
this
client
an
amount
equal
to
2.886.000
MAD.
Therefore,
we
need
to
do
wngrade
the
amount
allo
wed
by
the
e
xperts
(a
decrease
of
28%).
One
w
ay
to
re
vie
w
this
amount
is
to
apply
the
shares
of
each
product
to
the
risk-adjusted
authorized
amount.
Indeed,
the
authorized
amount
for
the
product
cash
dis
count
for
e
xample
is
2
100
000
MAD,
or
57%
of
the
initial
amount
(without
risk
adjustment).
An
appl
ication
of
this
percentage
to
the
theoretical
li
mit
of
2
886
000
MAD
mak
es
it
possible
to
de
v
ote
to
this
product
an
amount
of
1.897.733
MAD.
F
or
this
credit
application,
the
proposed
CDSS
system
slightly
lo
wered
the
amount
allo
wed
to
the
selected
project
applicant
because
of
its
a
v
erage
risk
profile.
Ho
we
v
er
,
for
other
profile
considered
less
risk
y
by
the
system,
the
limit
is
re
vised
upw
ards
and
the
same
approach
is
applied
to
find
the
appropriate
set
of
product
to
the
application
studied.
6.
CONCLUSION
The
go
v
ernment’
s
ambition
to
promote
the
b
usiness
climate
has
enabled
the
deplo
yment
of
se
v
eral
programs
to
support
the
economic
inte
gration
of
young
entrepreneurs.
Ho
we
v
er
,
successful
program
rollout
deserv
es
strong
bank
in
v
olv
ement
through
the
funding
of
promising
projects.
Hence
the
need
for
these
credit
institutions
to
set
up
systems,
based
on
artificial
intelligence,
t
hat
can
deal
with
the
issues
of
young
entrepreneurs
in
a
timely
manner
and
also
support
these
young
people.
Thus
we
ha
v
e
established
a
decision-
making
tool
(SADC)
allo
wing
the
bank,
selection
and
funding
of
promising
projects
based
on
implicit
kno
wledge
including
rating
models
and
design
of
credit
lines,
and
also
on
e
xpert
kno
wledge
of
the
banking
profession.
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