garanti bank´s journey to big data - oracle · big data discovery projects customer network...

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Garanti Bank´s Journey to Big Data

Ayşen Büyükakın

Business Intelligence & Analytics Unit Manager


...through a planned change journey in BI & Analytics

2004200320022001200019991998

Bankwide Reporting Concept Infrastructural DevelopmentExpanding BI & Analytics

Platform Investment in Fast Computing and

New Analytical Tools

Consultancyformanagementreporting

Segmentation

Management ReportingPlatform

Dashboards

Campaign & CommunicationManagement

Strategic change projects…….

…. Enabled by technology

Single version of Truth

Aggregationmethodology

Analytical platform for Data Mining

ETLs Financial Data Warehouse

Campaign Management System

Finalist in Information Awards

AnalyticallySupportedRetentionPrograms

Profiling on Issuing & Acquring

Propensity models Attrition models EXADATA SAS product suite

First Management Dashboard Financial Analysis Report

EXADATA SAS product

suite

2


...through a planned change journey in BI & Analytics

3

20162015201320082007

Scoring engine forlending processes

New modelingtechniques

Streaming Social data & useof optimization

Big Data ArchitectureMachine Learning &

Event processing

Strategic change projects…….

Price predciton Models Pricing Simulations AUTONOMY

Sentiment & Categorization

Data Pool Introduction of Big Data

technologies Daily populated

Analytical DW

AnalyticallySupported Sales

Efficiency in ATM replenishment

Analyticallysupported Risk Decisions

PersonalizedPricing

Social CRM Unsctructured data

Utilization of Big Data

Federation of Data

Rısk Decision DW Application scorecards Behaviour scorecards Risk capacity Models Fraud Models

Marketing Decision DW Propensity V2 Income prediction ATM replenishment Models

RTD OEP SNA DNA Volume Model

…. Enabled by technology

Fraud decisionmanagementaward

2014


Identify / FormulateProblem Gather /

PrepareAnalytics

Data

ExploreData

TransformData

BuildModels

ValidateModels

Deploy / ExecuteModels

MonitorModels

PredictiveAnalytics LifeCycle

Oracle BI

DVD

BDA

SAS EM v14.1 & EG v7.1

Oracle BDD

Oracle DVD


SAS Visual Analytics

R w Oracle Advaced Analytics


R w Oracle Advanced Analytics


SAS Visual Statistics


FICO Blaze Advisor

Model Repository - Inhouse

SAS Decision Manager v14.1

Predictive Analytics LifeCycle - Tools

Data Engineer

Analytical DW & Big Data

Management

Model Deployment

Model Execution

Model Monitoring

Report Design

R&D Activities

Data Scientist

Data Dictionary Management

Exploratory Data Analysis

Predictive Modelling

Model Validation

Model Monitoring

4


Predictive Analytics Lifecycle – Platforms

5

Data Store

Hive

BDA

Data

Warehouse

Exadata

On Demand

Ma

ste

r D

ata

/ O

pe

ratio

na

lD

ata

On Demand

Data

Enriched

Data

SAS

Discovery & Exploration

SAS VA

R

SQL / HSQL

Modelling

R StudioOracle R Advanced

Analytics for Hadoop

SAS Visual

Statistics

Oracle R Enterprise

on Exadata

R

R SAS

Studio

SAS In-Memory

Statistics for Hadoop

Enterprise Guide

& Miner

SAS

Analytics Laboratory

Oracle Big Data

Discovery

Scoring

R

SQLite SQL

FICO Blaze

Advisor

Java

R

Decision Tree

Text Miner

SAS

Production

Mo

de

l M

on

ito

rin

g

Changed Data

Model

Repository

SAS Decision

Manager

Deployment

Data

Qu

alit

y


Analytical Inventory

Marketing

Propensity ChurnIncome

PredictionVolume

Estimation

TermDeposit

Pricing & Simulation

Risk

Application Scorecard

BehaviorScorecard

FraudDetection

Collection Optimization

Process

ATM Replenish.

6


7 Pillars

7

Next Best Offer

AlertSystem

Data Pool

SandboxingCustomerNetwork

Urban Analytics

CustomerDNA


7 Pillars

8

Next Best Offer

AlertSystem

Data Pool


Urban Analytics

CustomerDNA


Data Pool

9

Customer Complaints

Social Media data

Speech – to – text

System and Application

Logs

Channel logs (ATM,

Internet, IVR, CC )

Click Stream Data

Credit Card Behaviour

Customer Financials

Customer Behaviour

Credit Bureau

Smart Application location

Structured Data Semi Structured Unstructured


10

Social Media Monitoring

Keeps socialmedia data in a

distributedqueue

(Staging Storage)

Apache Kafka Cluster

Big Data Appliance

CTG

Exalytics

Categorize socialmedia streamingdata and applybusiness rules

Apache SparkCluster

ConsumeContent

Apache HBASE Cluster

InsertContent

Index socialmedia data andprovide search

dashboard

Apache SOLRCluster

PropagateData

Manage socialmedia reports

with usingHBASE table

data

OBI 12C

HTTPS Rest Services

TCP

UNIX Server

Managebusiness rules

for social media

Drools (BRMS)SAS

Categorizationof tweets

SAS Text Miner

SOAP WS

Dat

a D

isp

atch

er


7 Pillars

11

Next Best Offer

AlertSystem

Data Pool


Urban Analytics

CustomerDNA


Sandboxing

• Citizen data scientists

• Self - service data preperation

• Self - service reporting

• Data visualization

• Storytelling

12


7 Pillars

13

Next Best Offer

AlertSystem

Data Pool

SandboxingCustomer

SNA

Urban Analytics

CustomerDNA


Real Time Decisions - RTD

A solution that addresses a business

issue faced by all organizations :

…how to make personalized and accurate

decisions, using the most up to date

information, in real time…

…consistently and in large volumes.

Real-Time Closed Loop

Self-Learning

Business Process ExecutionBusiness Process Execution

INFORMANTProcess data

ADVISORRecommendations

DATA SUPPLIERS

Enterprise Information Model

OLTP DW Grid

14


15

Next Best Offer

In Garanti Bank’s public page (http://www.garanti.com.tr/en/ ), there is a link for Internet Banking. Garanti customers login by clicking and supplying necessary credentials.

RTD implemented

Training OOT Test %25 sales increase

http://www.garanti.com.tr/en/


7 Pillars

16

Next Best Offer

AlertSystem

Data Pool


Urban Analytics

CustomerDNA


17

Alert Systems

Start Date End Date Company

# of compl


18

ATM Journal - Oracle Event Processing (OEP)


7 Pillars

19

Next Best Offer

AlertSystem

Data Pool


Urban Analytics

CustomerDNA


20

Big Data Discovery Projects

CustomerNetwork

CustomerDNA

Social

Warning

Systems

Demographics

Financial

Lifestyle

Life Stage


Financial Network

Network Analysis of Delinquent

and LabeledCustomers

Sector andCustomer

Type BasedNetwork Analysis

Network Analysis for

DifferentUsage Areas

Demographics & SocialNetwork

Data Quality

improved

New Relatives

Found

SocialRelationships

21

Customer Network


Goal

Identification of customers’ relational networks using customer

demographics, marital status, spouse info

22

Demographics Networks

Totally 17 million customers are analyzed

Data Description

Customer demographics and National-Id database are utilized

Active retail customers with verified demographical information are used


23

Current spouse information of customers are gathered via National-Id database

40.23% of 1.5M newly identified spouse pairs can be added to relational database since both customers are found to be active GB customers

42.4K of single, widowed or divorced customers are found to be married in National-Id database

Demographic Networks – Customer Relations vs. National-Id Database

New relations between GB customers are identified


24

81.8% of current parent-children relations are verified

39.9% of current sibling relations are verified

Customer Relationship vs. National-Id Database

4M new sibling relations are identified in addition to current 2.9K

3.4M new parent-children relations are identified in addition to current 198K

New Relationship Identification

Matching Affinity Relations with Locations

Among customers with affinity relations (excluding spouse relations);3.9% of them live in the same house / apartment,8% of them live in same neighborhood,23.4% of them live in same district

Demographic Networks – Other Relations


Goal

To define financial network among customers using money transfer

data

25

Financial Network

SME &Commercial Customers’ transaction data is chosen to build thenetwork.

Data

The network is built for 3 monthperiods to examine seasonalchanges.

Cheque and Money transfer transactions among customers aretaken into account.


26

Financial Network- SME’s in Tourism Sector – Labels

2.2.2016 3.2.2016 9.2.2016 25.2.2016 15.3.2016 22.3.2016

Customer 1 DelinquentLegal

Customer 2 Customer 3 Customer 5 Customer 6Customer 4

Money received from Cust1 /

Total money received%100 %100 %80 %75 %60

Money sent to Cust by Cus1 /

Total money sent by Cust1%29 %2 %2 %2 %2

%25

Etki %si = %25

%25

%10

%10

%5


27

Customer DNA- Life Style

Kart Profilleme

Luxuries

Essentials

Fashion

Tech-geek

Weekend Lovers

Traveller

Stable

SummerHouse

Credit Card

BehaviorCampaign Preference

CampaignLovers

Curious

Installment

Bonus Seekers


28

Customer DNA Analyses with Oracle Big Data Discovery


29

Goal

• Identify life stage of customer by analyzing credit card and ATM usage data

Findings

Moving

o Decrease in ATM & credit card usage in current district

o Increase and stability in ATM & credit card usage in

another district

Getting Married

o Decrease in Food & Drink and Education categories

o Increase in Home & Decoration category

Expecting a Baby

o Decrease in Nightclub & Bar category

o Increase in Infant & Child Clothing and Medical Testing

categories

Customer DNA – Life Stage

0

10

20

30

40

50

Moving Customer

Sabitlik DeğişiklikStability Change


7 Pillars

30

Next Best Offer

AlertSystem

Data Pool

SandboxingCustomer

SNA

Urban Analytics

CustomerDNA


Urban Analytics

1. Analyze spatial data to create a better market intelligence for the branches

2. Increase sales team’sproductivity

3. Intellegent marketing campaignmanagement

31


Urban Analytics - Implementation

32


Urban Analytics - Implementation

33


34

Thank You...

[email protected]

garanti bank´s journey to big data - oracle · big data discovery projects customer network...

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