Retail Banking 2.0Citi Mobile Challenge US 2014Prithiviraj Damodaran

RetailBanking 2.0 – A One-liner

RetailBanking 2.0 = Data-driven Personalization + Loyalty + WOW

Motivation

• World has gone Mobile.• Mobile has gone Cloudy.• Huge volumes of data can be processed at affordable

scale.• NoSQL stores available for every type of data.• Big data ecosystem is maturing as we speak.

Now is the best time for data-driven Innovation

Why RetailBanking 2.0

“In todays market, competition is not for the customer’s wallet. Its for the customer’s emotions”

The above marketing quote applies to Banks as well, RetailBanking 2.0 focuses on giving a personalized and exclusive banking experience for retail consumers.

What is RetailBanking 2.0 ?

Simply put RetailBanking 2.0 is a “Google Now” for Retail Banking Consumers. Idea is to leverage and learn from consumer’s transaction data and provide a exclusive experience.

So what is so untapped in consumer and transaction data ?

What banks already know about consumers ?

Architecture

What can Banks do for consumers?

Credit Card Swipes – A Use Case

• …your XXXX bank CREDIT card ending 7248 for $.8279.00 in BANGKOK at LOYLTY11673 on 2014-10-12:17:33:36.

• …your XXXX bank CREDIT card ending 7248 for $.1774.00 in NEWYORK at SAFFRON ROOM CUISINES on 2014-10-11:21:18:37.

• …your XXXX bank CREDIT card ending 7248 for $.3699.00 in MUMBAI at LEVIS on 2014-09-16:13:27:19.• …your XXXX bank CREDIT card ending 7248 for $.489.00 in MUMBAI at KFC Nungambakkam Chennai

on 2014-09-15:19:55:32.• …your XXXX bank CREDIT card ending 7248 for $.900.00 in MUMBAI at TONI & GUY on 2014-10-01

:13:48:18.• …your XXXX bank CREDIT card ending 7248 for $.3090.00 in MUMBAI at INFLUENCE SPA & WELLNESS

on 2014-10-09:21:35:27.• …your XXXX bank CREDIT card ending 7248 for $.1826.00 in MUMBAI at VODAFONE-BILLDESK on

2014-10-10:13:01:23.• $.9770.00 was spent on ur XXXX bank CREDIT Card ending 7248 on 2014-02-22:18:34:32 at CLEARTRIP

TRAVEL SERVI

Credit Card Swipes – A Use Case contd..

• In the above sample transactions the highlighted phrases both in RED and GREEN are keywords for the machine learning algorithm to build consumer preference profile.

• After few rounds of learning we could build a preference profile like this.

Consumer ID

Value Type Key Value Marketing Lead Type

X Preferences Entertainment SPA & WELLNESS Affiliate or 3rd Party

X Favorite Brands Garments LEVIS Affiliate or 3rd Party

X Favorite Brands Beauty/Care TONY & GUY Affiliate or 3rd Party

X Profile isFrequentTraveller YES Direct Marketing

Typical Consumer Profile

Income Group• Salaried• 65K and 100K Per annum

Age Group• Married• 25-35

Prefences• Predominantly Credit Card user• Frequent Flyer – Travels every month• SPA Customer• Expedia Travel site customer.

Buying Behavior/Favorite Brands• Vodafone – Monthly once• TONY & GUY – Monthly once• LEVIS

Credit Card Reward Points• Current Points – 16000• Available to Redeem - 12000

Mortgage and Loans

End to End Flow

Credit card Txn for $1950.00 in Chicago at Expedia.com

Machine Learning Module

Consumer Profiles

Convert your recent txn for $.1950 into Equate monthly installments. 6 months – 0%, 12 months – 2%. Respond with Y = Yes, N=No or S = Stop alerts. This text will be toll free

We wish you a great journey in your upcoming travels. For your FOREX and travel insurance needsRespond with Y=Yes, N=No or S =Stop alerts. This text will be toll free

Possibility 1

Possibility 2

Possibility 3

Your credit reward points balance is 16000 which is equivalent to 2500 miles. If you need assistance converting respond Y=Yes, N=No

Thanks for your time