Case Study in Banking and Finance: The Real-World Use of Big Data in Financial Services Big Data World Show, Malaysia Loon Wing Yuen Director, Innovation Group Information Services, Group Information and Operations Division

Shangri-La Hotel, Kuala Lumpur 9-10 September, 2014

The Opportunity in CIMB (circa 2012)

CIMB had the largest Facebook fan-base (over 600k) among banks in Malaysia CIMB also had the largest Facebook fan-base (over 1m) among banks in ASEAN CIMB also had a huge Twitter following CIMB had launched OctoPay (Facebook Banking) and many Facebook-related marketing

campaigns, several which targeted debit card usage – we built a database of matching CustomerIDs and FBIDs with associated FB structured and unstructured data

With this data asset, there were potential debit card revenue opportunities leveraging this asset

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FB Marketing Campaigns (2011 & 2012)

ASEAN’s 1st banking service on Facebook (2012)

Application Use Case – Leveraging the value of our customer’s FB Likes The Debit Card business was new and so the active card base was low, resulting in low transactional volumes. Hence, this did not give the business a good idea on the types of merchant spend. We decided to check if there was a correlation between FB Likes and merchant spend. Assuming there was a good correlation – then the hypothesis was that we could take the wider range of merchant categories in FB Likes and use them for marketing campaign interventions.

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Big Data Platform

(FB Likes) With

Debit Card

With

Spend

Regular

Spender

Irregular*

Spender

Without

Spend

Without

Debit Card

Traditional

Big Data

New

Big Data

2012

Distribution of Customers’ FB Likes

►Total identified distinct users who generated FB Like*: 27,614 (out of a total of 53,482)

►Out of the 53k total, we linked 12,925 users as active CIMB customers

►Approximately 6.5% of users liked more than 500 pages in their Facebook profile.

►These 6.5% of “heavy FB Likes users” accounted for approximately 36% of the total FB Likes captured.

We discovered that the FB Likes generated by customers were very unevenly distributed.

* -- Excluding an FB Like for CIMB.

14544

9006

2277 1787

0

2000

4000

6000

8000

10000

12000

14000

16000

<=100 >100 and <=300 >300 and <=500 >500

Total Number of User Count versus Total Number of Likes per User

567047, 12%

1584913, 34%

874962, 18%

1696692, 36%

Total Number of User Like Pages versus Total Number of Likes per User

<=100 >100 and <=300 >300 and <=500 >500

2012

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Correlation of FB Likes with Merchant Spend

►Significant amounts of data cleansing and transformations required

►Correlations stronger in certain merchant categories/brands

►Not every FB Like is correlated

►Statistical testing required to determine the strength of correlations

We discovered good correlations among certain merchant categories and brands.

• The matching of the merchant_name and fblike_name is based on the simple “Like” SQL statement which does not guarantee the full match between the merchant_name and fblike_name.

• More powerful data cleaning is needed to match the merchant_name and fblike_name more accurately.

Debit Card Txn FB data

2012

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Distinct Count of FBLikes for Starbucks by Micro Segment

Targeted Interventions by Merchant Brand

► Size of bubble represents Total FB Likes from Credit Card Prospect Base* FILTERED BDPP DATASET AS AT 11 MAR 2013:

1. 6 FB CAMPAIGN DATA FROM GMCD - I LOVE NEW YORK, DEBIT CARD RESKIN, MY DEBIT CARD, FOOTBALL FANTASY, YOUTH PEEK BUY, YOUTH VIDEO VOTING

2. FB CIMB_ASSISTS DATA FROM GMCD

3. CUSTOMER TAGGING DATA FROM BIU

We can then partner with a selected existing merchant (eg. Starbucks) and design a very targeted campaign – or on-board a promising new merchant partnership.

6 Note*: Prospect Base is based on active customers aged > 21 yrs old without a credit card.

Micro Segment Distinct Count of

FBLikes for Starbucks

Facebook User Base 4,747

Active Customer Base 1,778

Credit Card Base 96

Credit Card Prospect Base* 1,216

Debit Card Base 946

Distinct Count of FBLikes for Starbucks by Business Segment

Distinct Count of FBLikes for Starbucks by Macro Segment

FBLikes Comparison between Credit Card Base & Debit Card Base for Starbucks by Macro Segment

2013

Application Use Case – Moving on to the Credit Card base

The results from the work on the Debit Card base was promising enough to gain buy-in to next work on the Credit Card base as the next phase.

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2013

The scope was to create actionable insights to:

Increase credit card usage

Reactivate inactive credit card users

The approach was to:

Focus on influencing usage behavior – hence the focus on analyzing customer behaviors

Influence usage behavior by offering targeted merchant offers

Increased usage will generally lead to increased balances

The deliverables were:

Decile analysis of the card user base by card spend, merchant category and merchant brand spend

A range of actionable propositions that can drive card usage

A fully sized segmentation model for targeted offers

Credit Card Usage Analysis

The business goal at high level is to maximise both usage and balances for each credit card customer.

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Usage

Balance

High Usage Medium Balance

High Usage Low Balance (Transactors)

High Usage High Balance

(Core Revolvers)

Medium Usage Medium Balance

Medium Usage Low Balance

Medium Usage High Balance

Low Usage Medium Balance

Low Usage Low Balance

Low Usage High Balance

1 2 3

4 5 6

7 8 9

‘Occasionals’

Profitable

group

2013

From Analysis to Actionable Insights

An example of crafting a marketing proposition for the ‘Occasionals’ cohort.

1. Understand customer

purchases by merchant

categories

2. Understand

merchant

product features

3. Plan campaign

and create offers

4. Generate the customer list for

each offer and execute according

to campaign plan

Which product to offer

Choo-

sing

who

to

target

2013

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Big Data Analytics Platform for Business

In reality though, this is how the business is analyzed – by deciles. The new Big Data Exploration Portal allows “speed of thought” analyses as compared to the traditional multi-week report turnarounds from the data-warehouse – a key metric is now “Time to Actionable Insights”.

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2014

Entire Customer Base with > 30 months of transactional data

500+ different metrics calculated

in < 2 seconds

The problem is that at least two-thirds of our effort and time is spent with data cleansing, filtering, transformation, enrichment, etc. instead of extracting business value from the data.

Our Biggest Challenge though is..

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Big Data requires familiarity with Statistical/Machine Learning and NoSQL approaches

The Statistical/Machine Learning approaches used were:

► Principal Component Analysis (a Statistical dimensionality reduction approach) was used to reveal

key behaviors among the credit card base

► K-Means clustering (a Machine Learning approach) was used to identify and segment “Low to High

(Y1 Y2) Usage” spend behavior

► Neural Networks (a Machine Learning approach) was used to predict spend behavior

► Support Vector Machines (a Machine Learning approach) was used to predict customer inactivity

The NoSQL approaches used were:

► De-normalisation/nesting of the transactional data

► Modeling the data for optimal access for the purposes of supporting long-term customer analytics

and near-realtime customer intervention systems

Some of the statistical/machine learning and NoSQL approaches used were:

2013 - 2014

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Enhancing Business Capabilities with Big Data analytics

Big Data analytics can enhance all business dimensions of “Analytics” and “Management Information”

Compliance & Regulatory Analytics

Basel II & III FATCA Sarbanes Oxley

Act (SOX) Fraud / AML

Suspicious Activity

Compliance Reporting

Regulatory Reporting

Risk Management Analytics

Credit Risk Market Risk Operations Risk Liquidity Risk Capital Analysis Collection Analysis

Exposure Analysis

Sales Analytics

Event/Campaign Analytics

Behavior Analytics

Market Analytics

Transaction Analytics

Customer Analytics

Targeted Marketing /

Sales Lead Analytics

Management Analytics

Income Analytics Cost Analytics

Profitability Analytics

Sales Performance

Payment Analytics

Capital Allocation Analytics

Position Analytics

Balance Sheet Analytics

Weighted Average Analytics

Structured Finance

Analytics

Liquidity Analytics

Corporate Action Analytics

Performance Analytics

Financial Market

Analytics Foreign

Exchange Analytics

Settlement Analytics

Performance vs Benchmark

Asset Allocation Analytics

Product Analytics

Portfolio Performance

Portfolio Risk Analytics

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Rebuilding our Big Data and Machine Learning Platform

There is an incredible opportunity to leverage Big Data and Machine Learning technologies to add advanced capabilities to our digital channels as well as to dramatically reduce “time to actionable insights” for our business stakeholders.

ElasticSearch Indexing

Map - Reduce

Pig Hive

Tez HBase Storm Spark*

Yarn

HDFS

Exploration

Portal

Enterprise Data

Warehouse

Customer NoSQL Repository (Cassandra)

2014

Analytics REST-API layer

Business Analyst

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Focus on the business

priorities first, start with an

engaged business

stakeholder and manageable

pilot

Identify a business

opportunity to address and

prove the viability/business

case, let the next business

use case build upon this

success and expand

Focus on people and

skills, lesser on the

technologies

The technologies are new,

so be prepared to

experiment; use, discard

and replace technology

components as required

(many are open-source,

fortunately)

Data

cleansing/preparation/

management is a big

issue, not to be

underestimated

If the existing EDW is not

primarily built for customer

centricity and insight, don’t

retrofit this into the EDW –

instead build something

akin to the Customer

NoSQL Repository outside

using new Big Data

technologies

Approach Capabilities

There is an incredible

opportunity to leverage

Big Data and Machine

Learning technologies to

add advanced capabilities

to an organisation’s digital

channels and supporting

the business need of

significantly reducing “time

to actionable insights”

There is significant

business opportunity in

leveraging external data

such as FB and Twitter

But rethink approach on

leveraging this FB and

Twitter data – start with

working on the issue of

reliably linking external ids

with internal customer ids

Opportunity

Summary and our learnings along the journey so far 2012 – 2014

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Thank You