Knowledge is of the past, wisdom is of the future

Big Data Analytics Using RAMZ Bank

RITUPARNA SARKAR

Key Trends

Banks look to 'big fast data' to meet regulator demands Use cases of Analytics in Banking

◦ Consumer Behavior and Marketing◦ Risk, Fraud and AML/KYC◦ Product and PortFollio Optimization

Examples of Banking Analytics Impact◦ A 15% increase in assets by designing unique offers for customers◦ Improve time-to-market by 25%◦ Cut marketing cost by 20%

Size and Growth of Analytics in Banking industry

Case Study Problems Highlights

AMZ Bank◦ Recognized for their high standards and advanced service philosophy ◦ 7th largest lender in terms of assets ◦ Network of over 400 branches in Asia Pacific ◦ Relationships with 1000 banks in 70 countries around the world

Vision Statement: To become ultra-modern and data driven—an organization enabled to use any and all of their data to drive business excellence

Goal and objective:◦ Wish to reward their active and paying customers and reduce overheads on maintaining not so

paying customers.◦ Maximize the number of active credit card customers

better targeting marketing incentives to those most likely to activate and use for their business transactions

◦ Want to isolate the cards that would likely never be activated to reduce wasted marketing spend◦ Reduce loss of customers to their competition

Case Study Problems Highlights

How did AMZ get here:◦ Realized the potential of Big Data that could help them through predictive analytics and guided

actions for their business decisions◦ Large institution with large data

ability to scale is mandatory, but they wanted to place themselves at the leading edge◦ Efficiency was one of their top considerations

wanted an analytic architecture that was fast, flexible, affordable and simple-to-use, all at the same time

Current Situation◦ Recently set out to create a state-of-the-art analytic environment to support and fuel their fast

growing credit card business◦ Bank Credit Card Center was already familiar with predictive analytics ◦ Had used conventional products in the past for BI and Reporting for their business outcomes and

decision making.

Proposed Solution: Real Time Analytics

To Visualization & Reporting Layer

Connection to Data-lake

Open-source Message Broker - provides a unified, high-throughput, low-latency platform for handling real-time data feeds.

Open-source real-time computation system- processes unbounded streams of data, doing for realtime processing what Hadoop did for batch processing.

A. Setting up a right Datawarehouse platform: Leveraging from the large data

Many hundreds of systems are distributed throughout the organization; each system is largely independent; any customer experience data is concentrated within that systemOption A: Traditional Datawarehouse Option B: Big Data (MPP Database) Approach

• extensive data definition work• extensive transfer of data• data sources are incomplete, do not use

the same definitions, and not always available

• Sampling the data would have been very problematic, as the objective was to construct a customer view over time from all the events that took place. 

• Timescale to implement considerably high

• Need for elastic scalability, extreme performance, faster data access,

• Workload Management, Fault-Tolerance, Advanced Analytics feature support

• Massively Parallel Processing data warehouse set up

• Can execute complex SQL analytics on very large data sets at speeds multiple times faster

Proposed Solution

Proposed Solution: Batch Analytics Platform

Connection to Real-time layer

To Visualization & Reporting Layer

Proposed Solution

B. Identifying the predictive analytics software◦ Need to use the complete data, and not just a sample to get the complete picture towards making

the right decisions◦ Tool would need to work with massive datasets, support the datawarehouse platform, provide

visualizations/predictive modelling capability◦ Fully scalable data modeling against all data, improving analytic model accuracy and efficacy.◦ Faster time from modeling to scoring, delivering rapid results and enabling iteration.◦ Reduced data movement and latency, thus improving productivity of data analysts and IT staff.◦ Efficient utilization of data assets and IT resources, reducing costs and increasing ROI ; essential to

factor in the cost of having dedicated data miners versus a tool based approach◦ Scoring directly within the database, leveraging the database’s common security, auditing and

administration capabilities and reducing data movement and increasing data utilization.

Ad-hoc Data Analysis & Reporting in Excel & R

Automated Reporting & Alerting system

Delivered to all form-factors

Advance analytics capabilities

Data-lake

Visualization & Reporting Platform

Proposed Solution Benefits

With centralization and democratization of data, business user become more empowered to mitigate risk and propel development.

Rational and data supported decisions will increase efficiency in processes

2-layered data warehouse (MPP & HDFS) bring the best of both worlds. Mix of flagship products & open-source gives technical flexibility with

unbounded scope.

Recommended Data model

Data Model◦ HDFS

Unstructured data : JSON formatted Flat File schema. Structured data : Flattened Star Schema.

◦ Pivotal Greenplum Structured data : Snowflakes schema

◦ Apache Storm All data : JSON formatted Flat File schema.

Solution design mock up Analyzing the issue 1: Reduce the number of inactive credit

cards / Maximize the number of active credit card customers Definition, Benefits and Cause

◦ In-active credit cards - No transaction over an year◦ Benefits of reducing In-active Credit Cards

Restricted lines of credit can be re-distributed among active users and hence opening new opportunities of earning.

Suggested Solution◦ Design dynamic products that grows and shrinks with customers

behavior.◦ Restrict pushing low fees cards.◦ Define in-activity period limits, apply re-activation strategy and

churn customers on expiry.◦ Increase presence of ATMs, POS Card Swipe Machines, online

merchant partnership to tap more information.◦ Design system to push customized 1-to-1 based on location and

situation.

Possible Causes

Low Quality Custom

er

Low Quality Products & Eco-system

Customer

Product Mismatc

h

Solution design mock up Analyzing the issue 2: Reduce customer churn Definition, Benefits and Cause

◦ Loss to competition - Customers shifting to competitor’s product◦ Benefits – Steady revenue, focused incentives, increased loyalty

Suggested Solution◦ Push products with high exit barrier to customer with multiple short term credit

lines.◦ Improve brand value and trust by showcasing the strength.◦ Empower customer support with updated-till-last-minute details of customer

and prescribed recommendation to serve better.◦ Revamp the customer acquisition process with best mix of online and offline

processes.◦ Achieve all regulatory and compliance certification◦ Design dynamic products that grows and shrinks with customers behavior.◦ Restrict pushing low fees cards.◦ Define in-activity period limits, apply re-activation strategy and churn

customers on expiry.◦ Increase presence of ATMs, POS Card Swipe Machines, online merchant

partnership to tap more information.◦ Design system to push customized 1-to-1 based on location and situation.

Possible Causes

Weaker brand value

Higher Cost of

ownership

Low quality

customer service

Innovative

products

Data Requirements

Data Sources

Internal Data External Data• Customer acquisition history – Sales Team : How, what,

when, why and who • Customer lifetime history – Customer Relations Team

• Current & Historical satisfaction status • Movement between products• Movement in customer’s life an its effect on

behavior • Marketing & Products data – Marketing Teams

• Products • Marketing campaigns

• Customer identification across borders• Across border relationship

• Credit scores and credit reports• Analyze open lines of credit• Analyze metrics like debt ratio, credit utilization etc.

• Domestic and International Fraud and Crime Data• Prevent fraud and money laundering

• Partner Banks, Merchants and Institutions

•Professional Data from LinkedIn•Network and personal life from Facebook & Instagram•Social behavior from Twitter

Social media

•Sensors, Mobile devices and applications•PoS, ATM•Web logs and Online shopping

Others

Major Improvement Tasks

Devise 3600 view of customer◦ Develop customers spend signature to prevent fraud using past transaction data, social media behavior, sensor data from mobile etc.◦ Analyze all interactions like emails, call-center calls etc. with customer to devise the current satisfaction status◦ Develop a view of customers personal life and social connections like “Last 5 significant events”.◦ Link and analyze customer social media activities◦ Develop recommendations for customer which can include offers, merchant offers, upgrade offer etc.

Revamp Marketing and Sales◦ Build Social Media listening and monitoring center to capture, mediate and intervene in Social Media conversations.◦ Analyze live data sources along with customer spend signature to push location-based, situation-based 1-to-1 targeted offers and

notification.◦ Devise aggregated regional potential, market share, brand sentiment to formulate resource allocation, sales targets etc.◦ Empower ground staff with next-gen marketing & sale tools to help them maximize their personal ROI.◦ Design and deploy market watch system to monitor and generate alerts on competitor activity like new product launch, change in interest

rates etc. Empowering Operations

◦ Revamped dashboards with details like customer satisfaction status, customer value, last 5 call highlights etc to be delivered with least time.

◦ Design special call routing to build executive – customer relationship.◦ Develop 1-click recommendation and live offer system which will use data from live current conversation to facilitate best cross selling.◦ Deploy Robotic Process Automation tools to increase efficiency.

Types of Analytics in Banking

Recommended Tech stacks

Setting up a MPP Data warehouse EMC Greenplum Enterprise Data Cloud (EDC) as a powerful MPP data warehouse platform

◦ Greenplum Database® is an advanced, fully featured, open source data warehouse. It provides powerful and rapid analytics on petabyte scale data volumes.

◦ True MPP architecture and features that meet mandatory requirements of enterprise-class data warehousing◦ Allow AMZ market-leading power and scalability on commodity hardware.◦ affordably ride the curve of hardware advances and enjoy the simplicity and flexibility of a private cloud environment ◦ Can get market leading load performance (10X faster than peer data warehouses) with comprehensive ELT transformation

capabilities to enterprise-class high availability/disaster recovery◦ Can use  Specific customer experience analytical packages (ClickFox and Merced) towards data analysis

Setting up a predictive analytics software◦ Alpine Miner as a Predictive analytics software

Alpine Miner on the Greenplum database provides a fully integrated environment for statistical transformation and modeling methods for data analysis, modeling and scoring, with true scalability and top processing speed.

As a completely scalable in-database solution, these processes can be built entirely within the Alpine Miner interface, and then executed directly where the data resides, with no limitations on size or complexity

With Alpine Miner, business and data analysts can flexibly and efficiently conduct end-to-end knowledge discovery and predictive analytics—including data preparation, data transformation, data modeling and data scoring.

All the models built with Alpine Miner are automatically stored in the database and can be published or deployed directly within the database at the press of a button, further assuring data reliability and integrity and accelerating model integration with business applications. 

The Alpine Miner architecture cuts weeks to months from the process because it reduces unnecessary data movement, and supports better data governance and data-mining process standardization.

Source: http://david.portnoy.us/comparison-of-mpp-data-warehouse-platforms/

Comparison of MPP data warehouse platforms

Low Quality Customer : Cause & Solution

Lack of opportunity to usePossible solutions

◦ Increase partner, merchants and network ◦ Don’t sell cards to such geographies

Lack of knowledge and faithPossible solutions

◦ Invest in customer training◦ Showcase your security and compliances

Already owns multiple credit cardsPossible solutions

◦ Analyze list of open lines of credit

Low Quality Products: Cause & Solution

The low performance in the products can be due to various reasons.◦ Product Design, Pricing, Customer segment etc.

Improvement Approaches◦ This can be improved by developing a service delivery strategy. For Ex. “Mobile Wallet”. ◦ Mobile banking: Offers the convenience of banking anytime, anywhere. ◦ Mobile payments: Enables customers to send money to any mobile phone number. ◦ Mobile payments–contactless: Allows customers to save time with tap-and-go mobile payment service. ◦ Mobile marketing: Provides consumers with exclusive promotions, coupons, and alerts based on their current

location.

Customer Product Mismatch: Cause & Solution

Market segment mismatch: The product was not designed for this type of customer. Provide a tailored product based on the historic data.

Customer-specific mismatch: The unique needs of the customer may be causing some friction points even though

the customer is in the product’s target market. Reach out to the customer asking feedbacks on the current offerings.

Geographical mismatch: The customer may be based in a particular country or region with regulations or local

business norms that create unique challenges. Find out features at a region level, which features are used most and which are not.

Summary

Banking industry : Data Landmine with tremendous capabilities Mix of flagship products & open-source gives technical flexibility with unbounded scope: Essential for

the right architecture and design Case Study

◦ 2 key objectives – Maximize active credit card users, Reduce Customer Churn◦ High level architecture followed by a snapshot of proposed solution to meet the objectives◦ Major Tasks for improvement

Challenges◦ Transforming the approach to do business with Big Data.◦ Align all layers of the organization understand and leverage the benefits out of Big Data.◦ Hire, nurture and retain fresh talent to develop and build the new system.◦ Sustain business though the transformation period ranging from 5-10 years on an average.

http://wikibon.org/wiki/v/Financial_Comparison_of_Big_Data_MPP_Solution_and_Data_Warehouse_Appliance

http://david.portnoy.us/comparison-of-mpp-data-warehouse-platforms/ http://

www.emc.com/collateral/hardware/white-papers/h8072-greenplum-database-wp.pdf

http://datascienceseries.com/assets/blog/GREENPLUM_Analyzing_customer_behavior-web.pdf

http://www.emc.com/collateral/campaign/global/forums/presentations/ny-future-data-warehousing.pdf

http://www.ciosummits.com/media/pdf/solution_spotlight/alpine_china-citic-bank.pdf

http://datascienceseries.com/partners/partners/alpine-data-labs http://www.predictiveanalyticstoday.com/top-predictive-analytics-software/

Reference URLs