BANKING SECTOR ANALYSIS OF IZMIR PROVINCE:A GRAPHICAL DATA-MINING ANALYSIS (R SOFTWARE APPLICATIONS)

BANKING SECTOR ANALYSIS OF IZMIR PROVINCE:A GRAPHICAL DATA-MINING ANALYSIS

(R SOFTWARE APPLICATIONS)

FATMA ÇINAR MBA, CAPITAL MARKET BOARD OF TURKEY

Assoc. Prof. Dr. C. COŞKUN KÜÇÜKÖZMEN İZMİR UNIVERSITY OF ECONOMICS

3 4 . O p e r a t i on s

R e s e a r c h & I nd u s t r i a l

E ng ın e e r ı n g N a t i on a l

C on g r e s s 2 5 - 2 7 J u n e

2 0 1 4

Wednesday, December 24, 2014

Real Time Interactive Data Management for the Effect and Response Analysis

Technique; Lattice and ggplot2 Graphical Packages of R Software

İzmir Dataset Graphical

Datamining Analysis

Purpose of the study


Yöneticilerin, IT dünyasının karmaşık kavram ve süreçlerinden arındıran, interaktif ve yalın görseller aracılığı ile veri bankalarının karmaşıklığını yok eden bir yaklaşımla stratejik karar verme pozisyonuna ulaştırabilmektir.

• Recent advances in computing and accompanied with the development of analytical tools it is now possible to use state of the art techniques at a very rapid pace.

• Increased use of data analysis techniques and the emergence of gigantic data led to the birth of a new discipline called data science.

• Financial institutions in general and banks in particular need to process the data in order to have prior and valuable information about the safety and soundness of their institutions.



Yöneticilerin, IT dünyasının karmaşık kavram ve süreçlerinden arındıran, interaktif ve yalın görseller aracılığı ile veri bankalarının karmaşıklığını yok eden bir yaklaşımla stratejik karar verme pozisyonuna ulaştırabilmektir.

• We propose a new data-mining system via graphical representations.

• To achieve this object we use R software and its advanced graphical packages for graphical inquiries.

• Thus, all data in a single pool of resources can be managed and integrated with the reports consolidated while summary reports which provide spontaneous pictures and graphics are provided to be displayed instantly.


• We generously exploit the richness of the data information pools and the power of our analytics to bring our new approach into reality against traditional analysis.

• To test our model we use regions/province specific bank data which belong to the third largest as well as commercial city of Turkey, İzmir.


Agenda Dataset: Financial Transactions of İzmir and Similar Cities of Turkey. (BRSA)*

Dataset: 15 cities and 6 account period Graphical Datamining Analysis

Period: 2008-2013 Accounts

Dataset are factorizet according to city and year factors.

Graphical Datamining applied on this factorizet data.


*BRSA: Banking Regulations and Supervisison Agency


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


• We do factor-based analysis for the begining with the simplest graphical form of the histogram.

• Histograms of a single numeric value by one factor are the starting point of the factor based graphical analysis

Histogram graph of the lattice

packakge for 1 numerical 1

factor values


Distribution of Total

Cash Credits According

the City Factor

Histogram (lattice)


Default Credits

According to the City

Factor Histogram

(lattice)


Default Credits

According to the Year

Factor Histogramı

(lattice)

Dotplot graph of the lattice

packakge for 1 numerical

many factor values


• Dotplot of Lattice R software package for one but many factors

• This we are able to analyze all the factors that affect a single numeric variable many factor data


Default Credits

Percents According to the City and Year Factors DotPlot Graph (lattice)


Total Cash Credits

According to the City and Year Factors DotPlot Graph (lattice)


Default Credits

According to the City and Year

Factors DotPlot Graph (lattice)

Scatterplot graph of the

lattice packakge for 2

numerical 1 factor values


• When it comes to analyze of the two numerical values against a factor, scaateerplot graph of the lattice packakge is useful

• This graph type also gives us the anomalies of the data which is quite useful for risk analyses


Total Cash Credits Vs.

Default Credits

ScatterPlot Graph

(lattice)



Default Credits


Factors ScatterPlot

Graph (lattice)


Default Credits Vs.

Default Energy

ScatterPlot Graph (lattice)


Default Credits Vs.

Default Energy


Factor ScatterPlo

t Graph (lattice)


Default Credits Vs.

Default Energy


Factor ScatterPlot

Graph (lattice)


Default Credits Vs.

Default Vehicles

ScatterPlot Graph

(lattice)


Default Credits Vs.

Default Vehicles


Factor ScatterPlo

t Graph (lattice)


Default Credits Vs.

Default Mortgage According to the Year

Factor ScatterPlo

t Graph (lattice)

XYPLOT graph of the lattice

packakge for 2 numerical and

many factor values


When it comes to analyse of the relation of 2 values under the effect of many factors XYPLOT graph of lattice package is very useful.


Default Credits Vs.

Default Energy


and City Factors XYPlot

Graph (lattice)


Default

Credits Vs. Default

Mortgage According to the Year

and City Factors XYPlot Graph

(lattice)

Ggplot2 packakge for 3 variables under

many factor effects.

Beautiful graphs in matrix form


• Lattice package are not capable of to analyse of 3 numerical values under the effect of many factors.• In this case we use ggplot2 package of R

software.• This quite intelligent esthetical packakge

provide us beatiful graphics with comprehensible information.


Default Credits Vs. Default

Energy According to

the Total Cash Credits and City

Factors BaloonGraph

(ggplot2)


Default Credits Vs. Default

Energy According to

the Total Cash Credits and

City and Years Factors

BaloonGraph (ggplot2)


Default Credits Vs. Default Mortgage

According to the Total Cash

Credits and City and Years

Factors BaloonGraph

(ggplot2)



Default Credits

According to the Year and City Factors MatrixPlot

Graph (ggplot2)


Energy Vs. Default Energy

According to the City and Year Factors MatrixPlot

Graph (ggplot2)



Default Credits

According to the Year and City Factors MatrixPlot Graph with Free Scales

(ggplot2)

Dataset: 15 cities and 6 account period Graphical Datamining Analysis

Period: 2008-2013 Accounts


Dataset are factorizet according to city and year factors. Graphical Datamining applied on this factorized data.

We analyset the behaviour of the default credits according to the factors of the city and year with the support of the R Softwre packages of lattice and ggplot2 with a new approach of Graphical Datamining.


[email protected]

http://www.ieu.edu.tr/tr

[email protected]

http://www.coskunkucukozmen.com

[email protected]

http://www.spk.gov.tr/

http://www.riskonomi.com

@fatma_cinar_ftm

@ckucukozmen

@RiskLab Turkey

@datanalitik

@Riskonometri

@Riskonomi

tr.linkedin.com/in/fatmacinar/

tr.linkedin.com/in/coskunkucukozmen

Contact

http://www.spk.gov.tr/

http://www.riskonomi.com/

BANKING SECTOR ANALYSIS OF IZMIR PROVINCE:A GRAPHICAL DATA-MINING ANALYSIS (R SOFTWARE APPLICATIONS)

Data & Analytics

Transcript of BANKING SECTOR ANALYSIS OF IZMIR PROVINCE:A GRAPHICAL DATA-MINING ANALYSIS (R SOFTWARE APPLICATIONS)