innovation in action - ncr...ncr innovation conference 2017: confidential 29 customer support use...

INNOVATION CONFERENCE 2017

INNOVATION IN ACTION Suzan Szollar & Marshall Yuan DI Labs

Please use the Innovation Conference Event

App to check-in to this session #S667

NCR Innovation Conference 2017: Confidential

Ram Ridgeway - Director of Retail Support at Wright-Patt Credit

Union

Mariano Maluf – Director of Disruptive Innovation and Platforms,

Financial HW Solutions

Suzan Szollar – DI Labs Product Management

Marshall Yuan – DI Labs Product Management

Speakers

3NCR Innovation Conference 2017: Confidential

Why AI Now? (And… What Is It?) Transforming Physical Space with AI (Smart Everything)

Digitally connected physical space (Mariano)

Vision project – retail example (Shuki)

Transforming Digital Experience with AI Conversational banking (Marshall & Ram)

Building a Foundation Data and Data Science

Visualizing Insights & Taking Action

Agenda

Why AI Now? (And What Is It?)

Being Human, We Connect with Everything

1966 Joseph Weizenbaum created

Eliza, a chatterbot. Meant as a parody

of therapy.

“What I had not realized is that

extremely short exposures to a

relatively simple computer program

could induce powerful delusional

thinking in quite normal people”

Joseph W.

Were We Always Like This?

7

Tech’s Role in Connections and Relationships

Today – tech provides a platform (facebook), or a channel (watch, bots, voice) for communication and information

In an AI ecosystem, tech becomes a responsive, predictive fabric that can enable stronger connections to better outcomes

We used to talk

on the phone Now we

talk to our

phone

AI – What Is It?

General AI

Machine Learning

Classic

Machine

Learning

Neural

Networks

General AI: can do anything equal or better

than a human

Task Based AI: equal or better than a

human at a task

Why Now? • Computing horsepower – GPUs, Quantum

• Big data, real time

• Off the shelf frameworks, new algorithms

Both can uncover unknown

and unexpected

relationships…

Classic Neural• Told “what” to look for

• Needs human intuition

• Doesn’t typically improve

with more data

• Learns by example, shown

“how,” keeps learning

• Shows ingenuity &

invention

Classic vs Neural Network Machine LearningAn example

My daughter wants a small dog.

Can I get her muffin instead?

Classic ML Example – if the “rules” fail, the accuracy of

the model will not improve

Neural Network Example - “Generative Adversarial

Network,” Dueling neural networks:

• One tries to classify dogs

• The other tries to trick the classifier

• Makes the classifier more accurate than a human

Transforming Your

Physical Space with AI (Smart Everything)

The Promise of an AI Ecosystem (an example)Ingenuity, Invention, Inspiration

The Past

Beautiful public spaces

Everything “on the way”

The Present

Transactional spaces

Everything “out of the way”

The Promise of the Future

Experiential “living” spaces

Transportation ingenuity

NCR Innovation Conference 2017: Confidential 13

NEW INTERACTION MODALITIES


NEW POINTS OF PRESENCE

Interactive signage Manager & Staff AppTeller AutomationTeller Assistant

Easy Authentication

Smart Staff

Mobile Payments

Smart Lockers

Consulting

Online Banking Assisted Service

Teller+Customer Display Active WaitingATM- Interface

BranchMobility

Stage Director

Location based services

AI Smart Edge Ecosystem

Taking Shape - Some Examples

Robo BranchesMulti-Tenant Kiosks

Smart Lockers

To learn more: 3:15PM Wednesday Session – IoT and the Digital Branch Ecosystem

Transforming Your

Digital Experiences with AI

The Promise of an AI Ecosystem

The Past

Banking face-to-face

A human interaction

The Present

Convenience, efficiency

Transactional tech interactions

The Promise of the Future

Connected experiences

Collaborative interactions...

https://vimeo.com/177580990

Consumers increasingly spending time on mobile messaging apps

Messaging could become the new mode for business to engage with consumers

Image from TechCrunch “Forget Apps, Now the Bots Take Over”

Fintech Startups are pioneering conversation interfaces to engage users… and FIs

21

2. Standalone consumer fintech

chatbots

1. Fintech exploring chat

interface capability

3. Fintech chatbots moving to

white label for FIs

Abe.aiOlivia Penny

Cleo Finn.ai

Larger financial institutions are following suit with both internally developed solutions…

Bank of America announces

“Erica” to be integrated in mobile

app (not live yet)

Capital One innovation team

goes live with SMS-only

conversation banking

…and with solutions developed by partner capabilities

23

Ally Assist• Personetics

USAA Virtual Assistant• Nuance

• Clinc

Kai• Kasisto


Will users be willing to talk to a bot? --- Yes

“Call us now”600 clicks/week

FAQ Chatbot500 clicks/first week

WPCU Chatbot Test


Will the bot be able to deflect calls from call center? --- Yes


500 sessions

200 questions

26 calls to call center

Chatbot Call Driver Funnel

87% of those who asked a question don’t call in!

How much work could a bot help automate for service teams?

17%

17%

16%

50%

1. Check Balance CBS for user txninfo

2. Routing#/ Account#

Workflow to set up direct deposit/bill pay

3. Branch hours/ locations

Branchscheduling integration

4. Depositavailability

Core/RDCintegration

5. Reg D limits Core integration

6. Lost Credit Card

Card management

7. Pin Reset CAS?

Initial accuracy for correctly

answered questions

1


How much more could we improve?

17%

17%

16%

50%

1. Answer incorrectly matched

• Retrain to correct existing

answers

2. Answer not available, but

frequently asked

• Create new answer

Improve accuracy for

incorrectly answered questions2


What do we do with the long tail?

17%

17%

16%

50%

Answer not available, and

infrequently asked

Send to call center or Live Person

Seamless transfer

to live help3


Data we get from conversations:

Familial relationships: “How do I open up a credit card account for my husband. He is not

on my current Wright paTT bank account”

“Can I transfer money from one account to my sons account”

X-sell opportunities: “Can I add a bank account to my profile to pay my loans with?”

“What is my business daily transaction limit?”

“How do I accept popmoney from the app”

Opportunity to detect life stages, demographics, family relationships to provide advice and x-sell at the right moment


Customer Support use case helped answer questions and did

not drive additional calls

Training takes a lot of work

Natural Language Processing needs deeper expertise

Use cases around PFM insights still to be proven

What else did we learn


First Credit Union Skill Live on Alexa!


Building an AI/Data Science

Foundation

AI - A Many Layered Cake

Data Storage

Computational

Horsepower

Data Science

Platform

Frameworks &

Models

User Experience

“Channel”

User Experience Immersive Gamified Interactive/Chatty Behavioral/Nudgery ….

VR AR Chatbot Voice Mobile …

TensorFlow Caffe PyTorch Keras SciKit Theano….

Jupyter… Zeppelin Python…Java…Scala…R… Apache

Spark…

CPU GPU Quantum…

Permission Privacy Compliance

Feed Insights into Actions via DI or FI Products & Services Logic

Product or Service

Data Sources Internal (Prod Systems, Distributed DBs…) External (Customer, 3rd Party)

Data Lake API Access Distributed DBs…

Data Access

Use Cases

Data Science – Big Picture & Out The Gate

Vision: AI infused products and services that create strong customer

connections and high impact outcomes

Hypothesis: Applying machine learning to customer problems will enable us to anticipate and respond to customer needs better than ever before

Problem: FI-s need to do more with what they have to grow revenue. 60% - 75% of FI income is from interest (credit cards, loans).

Opportunity: FI-s can get new income, differentiate themselves, and grow a bigger credit worthy customer base, by identifying and helping the right customers shift spending behavior to achieve their financial goals.

POC Goals:Understand where machine learning surfaces valuable, actionable insightsUnderstand what data we need to get meaningful resultsShow results - insights in action (via VR/AR, Alexa) to you!

Data Science – First Steps

Questions Customer Problems

Analytical Strategy

Gather &

Clean Data

Pre-processing

Explore & Experiment with Models

Evaluate

Visualize

Focus on Use Cases That:

• Grow interest income and help

customers achieve financial goals

• Detect engagement shifts, early

signals of churn

• Customer lifecycle/lifestyle

opportunities

Actions

New products/services

New Experiences

Prioritization Criteria:

o Business impact

o Mutual value for FI

and customer

o Differentiation

o Actionable

Right strategy?

Good data?

Right data?

Enough data?

What Data: Batch FI file

3 years of debit card and other non credit-card transactions, bill payments on 3rd party CC

No balance, demographics, credit score, credit card transactions, digital banking logging…

Use Case: Credit Card Switching Behavior

What We Did:

Extracted features and labeled data

Clustering on credit card switching behaviors

Predict switching using deep learning model

What We Learned:

Interesting grouping of credit card usage behavior, limited insight into why

86% accuracy on switching prediction

We need better data!

Our 1st Time – 3rd Party Credit Card Switching Behavior

36

3rd Party CC Behavior Groups

StableVisa fansLow: income, spend, loanHigh: Transfers

From shifty to stableDiscover fans with someswitchingLow: loanMedium: income, spend

Big switchersDon't like DiscoverHeavy debit usersHigh: income,expenditure, loans

StableNo favoritesLow: spendMedium: income, loan,savings

Our 2nd Time – Increase Credit Card Usage

37

What Data: Batch FI file 6 months CC, Debit, Credit Card Transactions

Rich demographics

Use Case: Increase FI credit card usage?

Approach:

Understand strongest feature tied to increasing usage

Is it actionable?

Results will be shared at Conference session

What We Did: Identified behavioral groups

Used bag learner approach to classification

Ran attention mechanism to discover top feature that drove behavior

What We Learned: 90% of time is cleaning data

TBD

Insight Product + Experience

Smart Digital Interaction

Human Connection

From Insight to Action


On credit you’ll cross

30% of your limit, it’ll

hurt your credit score.

Purchase on debit?

You have enough and

can still pay your bills

I have upcoming bills,

do I have enough?

CB

CB

Hi Phoebe,

you’re on track

for a car loan,

what car do you

want?

Opportunity:

No Credit

Needs Car

How can I

afford a new

car?

I’ll help you

improve your

credit score with

Credit Buddy

AI Everywhere


Design Build Experience

You can earn 10 points toward the

Innovation Game right now!

Please go to “Schedule” in the

mobile app, find this session, and

take a short survey to provide

feedback.

Surveys will remain open for 2

hours.

Check in code is S667


Please give us your feedback!

INNOVATION CONFERENCE 2017

THANK YOUDI Labs Innovation In Action

Please use theInnovation ConferenceEvent App to check-in to this session


AI – What Makes It Special Now?

AlphaGo (Go) vs Deep Blue (Chess)

After just the first two moves of a Chess game, there are 400 possible next moves. In Go, there are ~130,000…

You can’t compute every move

AlphaGo trained on 30 million human moves

Then learned by playing itself

Then it invented new moves

AlphaGo: “The top Go players couldn't get enough of its unique and sometimes transcendent style of play”

Augmented reality in the branchPowered by DI analytics in the cloud

43NCR Confidential - Internal Use Only

https://www.dropbox.com/s/gw9yi20ghk65o

es/DI%20AR%20demo.m4v?dl=0

https://www.dropbox.com/s/gw9yi20ghk65oes/DI AR demo.m4v?dl=0

AI Everywhere


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