Garanti Bank s Journey to Big Data Ayşen Büyükakın Business Intelligence & Analytics Unit Manager

Transcription

1 Garanti Bank s Journey to Big Data Ayşen Büyükakın Business Intelligence & Analytics Unit Manager

2 ...through a planned change journey in BI & Analytics Strategic change projects Segmentation Finalist in Information Awards Profiling on Issuing & Acquring Consultancy for management reporting Management Reporting Platform Dashboards Campaign & Communication Management Analytically Supported Retention Programs. Enabled by technology Bankwide Reporting Concept Infrastructural Development Expanding BI & Analytics Platform Investment in Fast Computing and New Analytical Tools Single version of Truth Aggregation methodology Analytical platform for Data Mining ETLs Financial Data Warehouse First Management Dashboard Financial Analysis Report Campaign Management System Propensity models Attrition models EXADATA SAS product suite EXADATA SAS product suite 2

3 ...through a planned change journey in BI & Analytics Strategic change projects Analytically supported Risk Decisions Analytically Supported Sales Efficiency in ATM replenishment Personalized Pricing Social CRM Fraud decision management award Unsctructured data Utilization of Big Data Federation of Data. Enabled by technology Scoring engine for lending processes New modeling techniques Streaming Social data & use of optimization Big Data Architecture Machine Learning & Event processing Rısk Decision DW Application scorecards Behaviour scorecards Risk capacity Models Fraud Models Marketing Decision DW Propensity V2 Income prediction ATM replenishment Models Price predciton Models Pricing Simulations AUTONOMY Sentiment & Categorization Data Pool Introduction of Big Data technologies Daily populated Analytical DW RTD OEP SNA DNA Volume Model 3

4 Predictive Analytics LifeCycle - Tools SAS Decision Manager v14.1 FICO Blaze Advisor Model Repository - Inhouse Deploy / Execute Models Monitor Models Identify / Formulate Problem Predictive Analytics LifeCycle Gather / Prepare Analytics Data Explore Data Data Scientist Data Dictionary Management Exploratory Data Analysis Predictive Modelling Model Validation Model Monitoring Oracle BI DVD BDA SAS EM v14.1 & EG v7.1 Oracle BDD Oracle DVD SAS EM v14.1 & EG v7.1 SAS Visual Analytics SAS EM v14.1 & EG v7.1 Data Engineer Analytical DW & Big Data Management Model Deployment Model Execution Model Monitoring Report Design R&D Activities Validate Models Build Models Transform Data R w Oracle Advanced Analytics SAS EM v14.1 & EG v7.1 SAS Visual Statistics R w Oracle Advaced Analytics SAS EM v14.1 & EG v7.1 4

5 Master Data / Operational Data Predictive Analytics Lifecycle Platforms Model Monitoring Data Quality Changed Data Data Store BDA Hive Exadata Data Warehouse Discovery & Exploration R SAS R Oracle R Enterprise on Exadata Enterprise Guide & Miner Analytics Laboratory R Studio SAS Studio Modelling Oracle R Advanced Analytics for Hadoop SAS In-Memory Statistics for Hadoop Scoring Java R SQLite FICO Blaze Advisor Production SQL SAS SQL / HSQL SAS Visual Statistics SAS R Enriched Data On Demand Data SAS VA Oracle Big Data Discovery Deployment SAS Decision Manager Model Repository Text Miner Decision Tree On Demand 5

6 Analytical Inventory Marketing Risk Process Propensity Churn Income Prediction Volume Estimation Term Deposit Pricing & Simulation Application Scorecard Behavior Scorecard Fraud Detection Collection Optimization ATM Replenish. 6

7 7 Pillars Alert System Next Best Offer Customer Network Data Pool Sandboxing Urban Analytics Customer DNA 7

8 7 Pillars Alert System Next Best Offer Customer Network Data Pool Sandboxing Urban Analytics Customer DNA 8

9 Data Pool Structured Data Semi Structured Unstructured Credit Card Behaviour Customer Financials Customer Behaviour Credit Bureau Smart Application location System and Application Logs Channel logs (ATM, Internet, IVR, CC ) Click Stream Data Customer Complaints Social Media data Speech to text 9

10 Social Media Monitoring Data Dispatcher Big Data Appliance HTTPS Rest Services Apache Kafka Cluster Keeps social media data in a distributed queue (Staging Storage) Consume Content Apache Spark Cluster Categorize social media streaming data and apply business rules Insert Content Apache HBASE Cluster CTG Propagate Data Apache SOLR Cluster Index social media data and provide search dashboard SOAP WS TCP UNIX Server Exalytics SAS SAS Text Miner Categorization of tweets Drools (BRMS) Manage business rules for social media OBI 12C Manage social media reports with using HBASE table data 10

11 7 Pillars Alert System Next Best Offer Customer Network Data Pool Sandboxing Urban Analytics Customer DNA 11

12 Sandboxing Citizen data scientists Self - service data preperation Self - service reporting Data visualization Storytelling 12

13 7 Pillars Alert System Next Best Offer Customer SNA Data Pool Sandboxing Urban Analytics Customer DNA 13

14 Real Time Decisions - RTD Business Process Execution 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. INFORMANT Process data ADVISOR Recommendations Real-Time Closed Loop Self-Learning DATA SUPPLIERS OLTP DW Grid Enterprise Information Model 14

15 Next Best Offer RTD implemented Training OOT Test %25 sales increase In Garanti Bank s public page ( ), there is a link for Internet Banking. Garanti customers login by clicking and supplying necessary credentials. 15

16 7 Pillars Alert System Next Best Offer Customer Network Data Pool Sandboxing Urban Analytics Customer DNA 16

17 Alert Systems Start Date End Date Company # of compl 17

18 ATM Journal - Oracle Event Processing (OEP) 18

19 7 Pillars Alert System Next Best Offer Customer Network Data Pool Sandboxing Urban Analytics Customer DNA 19

20 Big Data Discovery Projects Demographics Social Lifestyle Customer Network Customer DNA Financial Warning Systems Life Stage 20

21 Customer Network Data Quality improved Network Analysis of Delinquent and Labeled Customers Demographics & Social Network Financial Network Social Relationships New Relatives Found Network Analysis for Different Usage Areas Sector and Customer Type Based Network Analysis 21

22 Demographics Networks Goal Identification of customers relational networks using customer demographics, marital status, spouse info Data Description Customer demographics and National-Id database are utilized Active retail customers with verified demographical information are used Totally 17 million customers are analyzed 22

23 Demographic Networks Customer Relations vs. National-Id Database 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 New relations between GB customers are identified 23

24 Demographic Networks Other Relations Customer Relationship vs. National-Id Database 81.8% of current parent-children relations are verified 39.9% of current sibling relations are verified New Relationship Identification 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 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 24

25 Financial Network Goal To define financial network among customers using money transfer data Data The network is built for 3 month periods to examine seasonal changes. Cheque and Money transfer transactions among customers are taken into account. SME &Commercial Customers transaction data is chosen to build the network. 25

26 Financial Network- SME s in Tourism Sector Labels %25 %10 %5 %25 Etki %si = %25 %10 Customer 1 Legal Delinquent Customer 2 Customer 3 Customer 4 Customer 5 Customer Money received from Cust1 / Total money received Money sent to Cust by Cus1 / Total money sent by Cust1 %100 %100 %80 %75 %60 %29 %2 %2 %2 %2 26

27 Customer DNA- Life Style Luxuries Campaign Lovers Summer House Essentials Stable Credit Kart Card Behavior Profilleme Fashion Bonus Seekers Campaign Preference Curious Traveller Techgeek Weekend Lovers Installment 27

28 Customer DNA Analyses with Oracle Big Data Discovery 28

29 Customer DNA Life Stage Goal Identify life stage of customer by analyzing credit card and ATM usage data Findings Moving Customer 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 Stability Sabitlik Değişiklik Change 29

30 7 Pillars Alert System Next Best Offer Customer SNA Data Pool Sandboxing Urban Analytics Customer DNA 30

31 Urban Analytics 1. Analyze spatial data to create a better market intelligence for the branches 2. Increase sales team s productivity 3. Intellegent marketing campaign management 31

32 Urban Analytics - Implementation 32

33 Urban Analytics - Implementation 33

34 Thank You... 34