Payments Revolution Part 1: Technology...systems with the goal of enabling the best automated outcome Machine Learning •Automated systems which react to human input in a manner which

©2019 PTap Advisory, LLC; All Rights Reserved1

The Payments InstituteJuly 21-24, 2019 • Emory University, Atlanta GA

Payments Revolution Part 1:Technology

Peter TaplingManaging Director, PTap Advisory, LLC

1©2019 PTap Advisory, LLC; All Rights Reserved


Agenda

• Framing the conversation about Payments Revolution

• Identify “new” technologies

• Choosing a few to explore

• Categorize them

• Business considerations

• Putting the pieces together


A Revolution – From What to What?

• What are “traditional” forms of payment?

– Cash

– Check

– Credit Card

– Debit Card

• Is “chip and sig”/”chip and PIN” traditional?


• Most “revolutionary” payments technologies do not actually affect the flow of a payment through the bank

• Understanding where these technologies fit is key to helping understand their strategic value

• Many of them work together throughout a payment experience

Demystifying “Revolutionary”


Banks Are Technology Companies

• Do they change the way your FI processes an ACH file? Wire?

• Does the FI even know they exist in a payment?

• Do they change the behavior or requirements of your payment systems?

• Do they change user (consumer/commercial) experience?



What non-traditional payment means do you personally have at your disposal

today?

WORKSHOP


Here’s What I Have…• ApplePay

– iPhone

– iWatch

– MacBook

• Visa “tap to pay”

• BofA mobile ATM

• BofA Erica

• Zelle

• Coinbase (crypto)

• What about Uber/Lyft/SpotHero?

– Are those payments “technologies”

How often do you use these?


REVOLUTIONARY PAYMENT TECHNOLOGIES



In 3 minutes – write down 3 technologies you’d like to discuss.

DISCUSSION


Let’s Start with These…

NFC

Bluetooth

QR

IoT

Tokenization

API

AI/ML

Chatbot

Blockchain

ISO 20022

Faster

10

And nothing has gone away!“More than 70% of banking corporate data still resides on the mainframe.”

https://www.allerin.com/blog/why-do-banks-still-use-mainframes

https://www.allerin.com/blog/why-do-banks-still-use-mainframes


Technology Elements

•Near Field Communication

•A radio communication protocol which allows devices to communicate when within very close range (~4mm max.)

•NFC devices can have self-contained computational capability

•Communication protocol behind contactless payments

NFC

•A networking protocol which allows devices to communicate over a range of 30 feet

•Part of what powers Amazon Go stores

•BLE – Bluetooth Low Energy – Limits range to ~3 feet

Bluetooth


Technology Elements (cont’d)

• Quick Response codes

• 2D BAR CODE which encode information

• Used for crypto addresses, bill payment, some ATMs

QR

• A process by which sensitive data is substituted with information which is otherwise meaningless

• Used in payments to protect card information in certain applications

Tokenization



• Internet of Things

• The connection of everyday objects to the Internet

• Many applications in data collection driving masses of data for further analytics

• Events triggered by IoT devices could initiate payment activities

IoT

• Application Programing Interface

• In computer systems the definition of a function to be performed, the data required as input and the expected output

• When used in the context of banking and payments, these are not functions the bank will consume, but rather functions the bank will publish to other parties

• ALSO: PSD2; Open Banking UK; Open Banking Europe

API



• Computer systems which can imitate cognitive decision making processes

• AI systems evaluate a given transaction against a history of similar transactions and the context of the current situation to make a decision that is intended to mimic a human response

Artificial Intelligence

• Systems which use training data to ascertain from a variety of outcomes which is most likely to be the “best” outcome

• Machine Learning systems are used in conjunction with Artificial Intelligence systems with the goal of enabling the best automated outcome

Machine Learning

• Automated systems which react to human input in a manner which mimics a human response

• Could be via voice (telephone) or online (chat/email)Chatbot



• Blockchain is a term used generically to refer to:

• Any form of distributed ledger technology (DLT); and,

• Specific implementations of DLT which enable a public exchange of value (e.g. Bitcoin; Ethereum); and,

• [Mistakenly] to any form of cryptocurrency

• Blockchain enables the storage of information in an immutable ledger with agreement among a collection of different parties about the “correctness” of the data

• Blockchain implementations can be private, permissioned or public.

• Blockchain implementations may or may not involve the exchange of value (cryptocurrency)

Blockchain



• International Organization for Standards

• International standard for the exchange of electronic information between financial institutions or for financial transactions

• ISO 20022 specifies a meta-data repository which contains message descriptions agreed to between a collection of participants for a given information exchange purpose

• Given domains can define their own message descriptions (e.g., RTP messages are different than SWIFT gpi messages and different from other domains)

• Same concept as ISO 8583

ISO 20022



• Any mechanism or system that enables the exchange of funds between banks on behalf of bank customers where the availability of funds is “faster” than through traditional methods.

• “Instant” or “real time” payments are faster payments where funds are deducted from the senders account and and reflected in the receivers account in [near-]real time

• RTGS – Real Time Gross Settlement

• A system where funds transfers are cleared and settled in real time on a transaction by transaction basis

Faster


Did We Get Them All?

• OF COURSE NOT!

• What others would you like to discuss?


HOW ARE THEY GROUPED?


All These Technologies Fit in the “4-Corners”

FI A

ReceiverSender

FI B



How would your team group the technologies we have discussed?

How do they work together?

WORKSHOP


BUSINESS CONSIDERATIONS


Using This Information

• An understanding of underlying technologies can inform your product decisions

– Features and capabilities

– Risk management

– Regulatory compliance

• Even things you don’t “do” will affect you

– Think of customer service experiences


Features and Capabilities

• Using new technologies creates a web of considerations– Some things *must* be done

• e.g., Tokenization with Apple Pay (NFC)

– Some things *can’t* be done • e.g., Faster with IoT

• Look at the technologies with a “product” lens– How can you improve the customer experience?

– Can you improve operational efficiency?


Risk Management

• Anything “new” has unintended consequences

– Apple Pay (NFC) with Tokenization is safe, right?

• But what if I use Apple Pay from my Mac Book?

• Beware of “combination risk” – something alone is well understood, but in combination becomes a different animal

• Look at all risks, not just fraud

– E.g., AI for fraud against multiple card users


Regulatory Compliance

• Any solution should maintain or improve your ability to stay in compliance

• Novel technologies risk not being understood by regulators

• There is no “do over”!

• (Come see the Intersection of Innovation session on Wednesday!)



Using the “4-corner” model an end to end process and the technologies used.

WORKSHOPConsiderations


PUTTING THE PIECES TOGETHER


Where Do These Technologies Fit?

• Now that we understand a bit about what these technologies do:

– How do they fit together?

– Where do they fit in a payments architecture?

– How can you derive value?


• Wondering how all of this will change the overall Payments Experience?

• Stick around for Part II

Technologies are Building Blocks


THANK YOU!

Peter Tapling, Managing DirectorPTap Advisory, LLC

[email protected]

Documents

Payments Revolution Part 1: Technology...systems with the goal of enabling the best automated outcome Machine Learning •Automated systems which react to human input in a manner which