machine learning - ncr...innovation conference 2017 machine learning raghu rajah vice president,...

INNOVATION CONFERENCE 2017

MACHINE LEARNINGRaghu RajahVice President, Engineering & Product ManagementDigital Banking

Please use theInnovation ConferenceEvent App to check-in to this session S687

NCR Innovation Conference 2017: Confidential

1950s 1960s 1970s 1980s 1990s 2000s 2010

ARTIFICIAL

INTELLIGENCEMACHINE

LEARNINGDEEP

LEARNING

Divide this

Start training

Get better

Over many dimensions

Over manymachines

A quick example

Deep Learning playground

http://playground.tensorflow.org/

http://playground.tensorflow.org/

Credit Scoring

State of the art solutions look beyond FICO/D&B scores, consumes multiple features, and manages a live credit decisioning process. Uses many traditional Data Science, AI and some deep learning techniques.

Fraud Detection

Contemporary systems use anomaly detection algorithms (some DL) on streaming data.

Anti-money laundering

Graph Analysis and traditional AI techniques are the common approaches in contemporary AML solutions.

Robo Advisors

Applies a wide variety of AI techniques from Monte-Carlo simulation to Deep Learning at various levels

Customer Engagement

Deep Learning techniques could analyze customer behavior to determine attrition risk, upsell/cross-sell opportunity, and effective engagement

Chatbots

State of the art chatbotsuse deep learning for NLP and use sequence to sequence models achieve performance

Challenges for ML in Financial Services

Learning algorithms tend to learn to discriminate, based on the patterns in data – some of which is actually illegal, other might be unethical.

Deep Learning solutions are very effective, but tend to operate like a black box. This might cause regulatory issues.

Learning Algorithms tend to require a lot of data, the right use of which has some open questions.

Regulatory environment might not be very friendly.


THANK YOURaghu RajahVice President, Engineering & Product ManagementDigital Banking

Please use theInnovation ConferenceEvent App to check-in to this sessionNCR Innovation Conference 2017: Confidential


BLOCKCHAINPatrick WatsonApplication Security Architect, NCR Corporation

Please use theInnovation ConferenceEvent App to check-in to this session S687NCR Innovation Conference 2017: Confidential

TRUSTBUTVERIFY

- THE BLOCKCHAIN WAY

NCR Innovation Conference 2017: Confidential 19

Trust Systems – An Example

20

Physical Digital Blockchain

ACH ACH


Public, Permissionless (Bitcoin)

Private, Permissioned (Real $)

Cryptocurrencies

Public

Creates new assets

Alternate Currencies can be:

Borderless

Low fee, enabling micropayments

Serve the unbanked

Bottom Line Apps

Private

Usually Tracks existing assets or processes

Reduces cost, delays:

Remittance

Clearing & Settlement

International Exchange

Risk Knowledge Sharing

Top Line Apps

Public or Private

New applications

New opportunities:

Smart Contracts

Data Hosting

IoT Swarms

Identity Management

Blockchain Applications


Cryptocurrencies Bottom Line Apps Top Line Apps

Blockchain Applications


How could this work anyway?

23NCR Innovation Conference 2017: Confidential

3. Mined blocks of transactions are propagated to a

record of previous transactions – creating a

distributed database of permanent records

2. Miners verify transactions, put them into

blocks, then cryptographically harden the

records to make it immutable

Nonce, nonce,

nonce, c’mon

nonce!

yah baby!

It’s all

mine!

1. Person-to-person transaction

is initiated – to them, it looks

like any other app

$100 for

the bike

OK

Ethereum

Smart Contract

Auction House

A. Validate crypto & transaction details

B. Assemble transactions into blocks

C. Race to generate valid proof-of-

work/proof-of-stake.

D. Winner receives incentive

• Every full node has all data

• Bitcoin: pseudo-anonymous addresses,

but transfer amounts visible

• Ethereum: depends on contract’s code,

transaction details could be encrypted

or one-way hashed

Vs Traditional Applications


VS: Goes through a centrally

control system

Nonce, nonce,

nonce, c’mon

nonce!

yah baby!

It’s all

mine!

VS: the intermediary (human/manual process,

network, processor, etc.)

Remittance

Service

Provider

Insurer

Notary

Loan agent

VS: silo-d databases where records can be

modified

$100 for

the bike

OK

3. Mined blocks of transactions are propagated to a

record of previous transactions – creating a

distributed database of permanent records

2. Miners verify transactions, put them into

blocks, then cryptographically harden the

records to make it immutable

1. Person-to-person transaction

is initiated – to them, it looks

like any other app


THANK YOUPatrick WatsonApplication Security Architect, NCR Corporation



FINTECH SPEED DATING - IOTGirish Narang

Chief Architect, Digital Banking

Please use theInnovation ConferenceEvent App to check-in to this session S687NCR Innovation Conference 2017: Confidential

IoT


The Internet of Things (IoT) is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction.

Things communicating with other things via the internet, without human

interaction.


High Growth

7 Devices per

human on the

planet - 2020

Connectivity Cheap Hardware Data Storage Customer Delight

Enabling Forces


$1

Health Energy Insurance/Finance Retail

Common Use Cases


Car Insurance

Voice

Banking

In-branch

Location

Beacons

Voice

Ordering

Thermostats

Lighting

Heart Monitors

BMI Monitors

Mobile

Shopper

Why is it important for FIs?


New Digital Channel

Desktop, Mobile and now Smart Watches,

Amazon Echo etc

Increased Engagement

Personalized experience yields 20x engagement

Offer more personalized products

Car insurance by the mile

Variable risk based mortgage

Edge Devices Branch Transformation Smart Gateway Analytics

NCR investing in the full ecosystem



Demo – Smart Piggy LightLow Balance


THANK YOUGirish Narang

Chief Architect, Digital Banking



COMPUTER VISION Shuki LichtChief Enterprise Architect, Software Solutions, NCR

Please use theInnovation ConferenceEvent App to check-in to this session S687


FUTURE FRICTIONLESS CHECKOUT CONCEPT

Ann is identified as she enters the store to pick up a her lunch.

As she picks up items from the shelf they are automatically added to her virtual basket.

Using her phone she can see the price of the items in her basket and be reminded that the store is offering a personalized meal deal promotion.

She picks up the rest of her meal and sees that the discount has already been applied.

As she exits the store, Ann is charged on her preferred debit card by simply walking out.

A receipt of the transaction is sent to her digital wallet to give her a record of the purchase.

check-in

check-out

Real-Time Recommendati

on

ItemDetection

ShopperLocation

NCR Confidential

ShopperLocation

ItemLocation

Shopper

TrackingShelf

VisionShelf

Sensors

Shoppers ItemsVirtualBasket

Store HUB Controller

InventoryCheck-inCheck-out

Retail One

Order Service

TDMService

PromotionService

CustomerService

LocationService

InventoryService

Store IoT

Restful / real-time

90 DAY BLUE PRINT

NCR Confidential

Mobile

ShopperStore

Manager

BeaconsBiometric

38

Face Recognition

Behavior Biometrics

QR Code

Voice Biometrics

Additional Sensors

RFID

BLE

BIOMETRIC EDGE TECHNOLOGY

NCR STORE VISION

REAL-TIME - SHOPPER LOCATION, BASKET AND PHYSICAL INVENTORY

Shopper Tracking

Shopper Check-in

Shelf

VisionShelf

Sensors

https://www.dropbox.com/s/cbo06gqeeodjhhu/Synergy%20Vision%20Store%202017.mp4?dl=0

https://www.dropbox.com/s/7scb99eim9elzyy/synergy2017_2.mp4?dl=0

NCR Confidential 40

SYNERGY 2017 - VIDEOS

https://www.dropbox.com/s/cbo06gqeeodjhhu/Synergy Vision Store 2017.mp4?dl=0

https://www.dropbox.com/s/7scb99eim9elzyy/synergy2017_2.mp4?dl=0

NCR Confidential10/8

/201

41

DI INSIGHTS WITH VISION


THANK YOU


machine learning - ncr...innovation conference 2017 machine learning raghu rajah vice president,...

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