To Analyse various credit and financial situation of loans and loans defaults of some of the cities. Relationships and correlations were analyzed by R-based Graphic Data Mining program developed by us. Real Time Interactive Data Management for the Effect and Response AnalysisTechnique; Graphical Datamining with Lattice and ggplot2 Graphical Packages of R Software In this study the data set is transformed into a factor analysis based on the values ​​of time and space factors . Visualization of the data contains valuable findings for incentive system which differs according to the terms of ratings criteria of practitioners and banks.

1. LOANS IN LIGHT OF THE NEW SUPPORT SYSTEM:
THE FINANCIAL MAP
A GRAPHICAL DATA-MINING ANALYSIS
(R SOFTWARE APPLICATIONS)
FATMA ÇINAR MBA, CAPITAL MARKETS BOARD OF TURKEY
Assoc. Prof. Dr. C. COŞKUN KÜÇÜKÖZMEN, İZMİR UNIVERSITY OF ECONOMICS
Istanbul
Conference Of
Economics And
Finance
ICEF’14 08-09
September

2. Friday, December 26, 2014
Real Time Interactive Data Management for the Effect and Response
Analysis
Technique; Graphical Datamining with Lattice and ggplot2 Graphical
Packages of R Software
Investment
Promotion
Dataset
Graphical
Datamining
Analysis

3. Agenda
Data: Ministry of Economy and Foreign
Investment Promotion Practice General
Administration and BRSA*
Dataset: 6 Region Investment Promotion and 6
account period Graphical Datamining Analysis
Period: 2008-2013 Accounts
Dataset are factorized according to city and year
factors.
Graphical Datamining applied on this factorized
data.
Friday, December 26, 2014
*BRSA: Banking Regulations and Supervisison Agency

4. Purpose of
the study
Friday, December 26, 2014
To Analyse various credit and financial
situation of loans and loans defaults of
some of the cities.
Relationships and correlations were
analyzed by R-based Graphic Data
Mining program developed by us.

5. The new incentive system was enacted by the
Council of Ministers 5th June 2012 date and No.
2012/3305.
In this context, taking into account the level of
development Turkey is divided into six regions .
The most developed provinces, are located on
the first level of development while the provinces
lowest level of development are classified as the
sixth of the province .
07:55:26
Purpose of
the study

6. In this study, based on the entry into force of the
subsidies in question various types of
development and change in bank lending
analyzed by R based Graphical Datamining
Analysis Software we developed for this purpose
07:55:26
Purpose of
the study

7. In this study the data set is transformed into a
factor analysis based on the values ​​of time and
space factors .
Visualization of the data contains valuable
findings for incentive system which differs
according to the terms of ratings criteria of
practitioners and banks.
07:55:26
Purpose of
the study

8. Friday, December 26, 2014
1st. Region 2nd. Region 3rd. Region 4th. Region 5th. Region 6th. Region
Ankara Adana Balıkesir Afyonkarahisar Adıyaman Ağrı
Antalya Aydın Bilecik Amasya Aksaray Ardahan
Bursa Bolu Burdur Artvin Bayburt Batman
Eskişehir Çanakkale Gaziantep Bartın Çankırı Bingöl
İstanbul Denizli Karabük Çorum Erzurum Bitlis
İzmir Edirne Karaman Düzce Giresun Diyarbakır
Kocaeli Isparta Manisa Elazığ Gümüşhane Hakkari
Muğla Kayseri Mersin Erzincan K.maraş Iğdır
Kırklareli Samsun Hatay Kilis Kars
Konya Trabzon Kastamonu Niğde Mardin
Sakarya Uşak Kırıkkale Ordu Muş
Tekirdağ Zonguldak Kırşehir Osmaniye Siirt
Yalova Kütahya Sinop Şanlıurfa
Malatya Tokat Şırnak
Nevşehir Tunceli Van
Rize Yozgat
Sivas

9. Friday, December 26, 2014

10. Friday, December 26, 2014
summary(Dataset)
ILKOD SEHIR SYIL NYIL AY
Min. : 1.00 ADANA : 22 Y2008:60 Min. :2008 Min. : 3.000
1st Qu.:16.00 ANKARA : 22 Y2009:60 1st Qu.:2009 1st Qu.: 3.000
Median :33.00 ANTALYA : 22 Y2010:60 Median :2010 Median : 6.000
Mean :29.67 BURSA : 22 Y2011:60 Mean :2010 Mean : 7.227
3rd Qu.:42.00 DENİZLİ : 22 Y2012:60 3rd Qu.:2012 3rd Qu.: 9.000
Max. :55.00 GAZİANTEP: 22 Y2013:30 Max. :2013 Max. :12.000
(Other) :198
SDONEM NDONEM TOPNAKDIKREDI NAKDIKREDI
D200803: 15 Min. :200803 Min. : 2301180 Min. : 2249452
D200806: 15 1st Qu.:200906 1st Qu.: 5006994 1st Qu.: 4775867
D200809: 15 Median :201011 Median : 9001388 Median : 8623443
D200812: 15 Mean :201035 Mean : 14542560 Mean : 14003463
D200903: 15 3rd Qu.:201203 3rd Qu.: 15756949 3rd Qu.: 15263775
D200906: 15 Max. :201306 Max. :113564461 Max. :110692193
(Other):240
TAKIPALACAK GNAKDIKREDI TASIT KONUT
Min. : 39600 Min. : 215400 Min. : 34377 Min. : 313429
1st Qu.: 251686 1st Qu.: 971274 1st Qu.: 70138 1st Qu.: 625852
Median : 339949 Median : 1923710 Median :106403 Median : 944120
Mean : 539097 Mean : 4654118 Mean :168232 Mean : 1740547
3rd Qu.: 599559 3rd Qu.: 2933005 3rd Qu.:213790 3rd Qu.: 1812319
Max. :2872268 Max. :62782383 Max. :789062 Max. :13037891
KMH DIGERTUKETICI KREDIKARTI TAKIPTASIT
Min. : 13457 Min. : 329107 Min. : 2929 Min. : 1454
1st Qu.: 37365 1st Qu.: 728700 1st Qu.: 503812 1st Qu.: 3398
Median : 56261 Median : 1215660 Median : 828166 Median : 5354
Mean : 88532 Mean : 1815939 Mean :1197707 Mean : 8673
Summary of the dataset

11. In this section we investigate the effects of
various factors by the aid of gridplot programme
based on ggplot2 package of R software
Each grid represents 6 graphs describing the
cross effects and profiles of the variables
according to factors
07:55:26
DESCRIPTION
OF GRID
PANELS

12. Friday, December 26, 2014
Overall
Promotion
Regions
ILKOD Vs
(Log10 scale)
Default
Energy
According to
Region
Factorize
Grid Graphics

13. Friday, December 26, 2014
1.st Region
ILKOD
Vs (Log10
scale)
Default
Ebergy
According
to the Year
Factor
Grid
Graphics

14. Friday, December 26, 2014
1st Region
ILKOD Vs
(Log10
scale)
Default
Energy
According
to Year
Factor Grid
Graphics

15. Friday, December 26, 2014
Overall
Regions
Log10
Default
Loans Vs
Log10
Default
Credit Cards
According to
Year and
Region
Factor
Grid
Graphics

16. Friday, December 26, 2014
Overall
Regions
Log10 Default
Loans Vs
Log10 Default
Credit Cards
According to
Year Factor
Density and
Violin Graphs

17. Friday, December 26, 2014
Overall
Region Log10
Default Loans
Vs Log10
Default
Mortgages
According to
Year and
Region
Factors Grid
Graphics

18. Friday, December 26, 2014
Overall
Region
Log10
Default
Loans Vs
Log10
Default
Mortgages
According
to Year
Factor
Density and
Violin
Graphics

19. Baloon graphs of ggplot2 package can show us
3-dimensional relations distributed according 1-3
factors in scatterplot form.
With this type 2-dimensional numerical relations
can be represented under effect of 3rd numerical
value.
07:55:25
DESCRIPTION
OF BALOON
GRAPHS

20. Friday, December 26, 2014
1st Region
Log10 Default
Loans Vs
Log10 Default
Energy against
Noncash
Loans
According to
Year and City
Factors
Baloon
Graphics

21. Friday, December 26, 2014
2nd. Region
Log10
Default Loans
Vs Log10
Default
Energy
against
Noncash
Loans
According to
Year and City
Factors
Baloon
Graphics

22. Friday, December 26, 2014
3rd. Region
Log10 Default
Loans Vs
Log10 Default
Energy against
Noncash
Loans
According to
Year and City
Factors
Baloon
Graphics

23. Friday, December 26, 2014
4th. Region
Log10 Default
Loans Vs
Log10 Default
Energy against
Noncash
Loans
According to
Year and City
Factors
Baloon
Graphics

24. Friday, December 26, 2014
5th. Region
Log10 Default
Loans Vs
Log10 Default
Energy against
Noncash
Loans
According to
Year and City
Factors
Baloon
Graphics

25. Friday, December 26, 2014
6th. Region
Log10 Default
Loans Vs
Log10 Default
Energy against
Noncash
Loans
According to
Year and City
Factors
Baloon
Graphics

26. Facet graphs of ggplot2 package can show us
3-dimensional graphs distributed according 3
factors in matrix form.
In which we can see the anomalies occurs on
which year and which region and which period.
Here we investigate default energy versus
default loans bloonad by total loans according
to region, year and period factors.
Colors period, balloons Total Cash loans.
07:55:26
DESCRIPTIO
N OF FACET
GRAPHS OF
GGPLOT2

27. Friday, December 26, 2014
Overall
Regions
Log 10
Default Loans
Vs. Log10
Default
Energy
According to
Year and
Region
Factors Facet
Graph

28. With this graph we can see which region
represents anomalic behavior on which year and
which period under the effect of Total Cash
Credits.
3rd period of 4th region represents very anomalic
behaviour on the year 2008.
07:55:26
Overall
Regions
Log 10
Default Loans
Vs. Log10
Default
Energy
According to
Year and
Region
Factors
Facet Graph

29. 07:55:26
With this study we investigate 6 Regions
Investment Promotion and 6 account period by
Graphical Datamining Analysis technique
developed by us.
Period: 2008-2013 accounts.
Dataset are factorized according to city and year
factors.
Graphical Datamining applied on this factorized
data and financial anomalies dedected acording to
time and space factors.

30. Concerning the energy investments 1st region.
Promoting an increase in the proportion of the
supports it received in the Energy field by years.
2.region non-performing loans in energy in
year2009 is more risky comparing with other
risky assets. On the other hand in 2013 the
proportion of debt collection prone to decrease in
non-performing loans of energy while the
energy investments in a decrease.
For İzmir and Manisa; Manisa energy
investments are ahead of İzmir.
07:55:26

31. Friday, December 26, 2014
 I would like to express my deep gratitude to;
 Dr. Kutlu MERİH,
 Dr. C. Coşkun KÜÇÜKÖZMEN
 for their valuable contibutions,
Fatma ÇINAR

32. Friday, December 26, 2014
coskun.kucukozmen@ieu.edu.tr
http://www.ieu.edu.tr/tr
coskunkucukozmen@gmail.com
http://www.coskunkucukozmen.com
fatma.cinar@spk.gov.tr
http://www.spk.gov.tr/
http://www.riskonomi.com
@fatma_cinar_ftm
@ckucukozmen
@Riskonometri
@Riskonomi
@RiskLab Turkey
@datanalitik
@Riskanaltigi
tr.linkedin.com/in/fatmacinar/
tr.linkedin.com/in/coskunkucukozmen
Contact

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07:55:26