Lee Wetherington - indianabankers.org · machines are better at ... landscape of customer...
Transcript of Lee Wetherington - indianabankers.org · machines are better at ... landscape of customer...
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© 2017 Jack Henry & Associates, Inc.
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@leewetherington
Future techs with near-term impacts
Director of Strategic InsightJack Henry & Associates, Inc.®
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© 2017 Jack Henry & Associates, Inc.
@leewetherington
Lee WetheringtonDirector of Strategic Insight
• Develops actionable insight and strategy
for the financial services industry at large
• Delivers keynotes nationwide
• Technology Faculty Chair for
several graduate schools of finance
• Authors articles for industry trades
• Degrees in Economics and English
from Duke University
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Machine Learning
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SOURCE: https://www.geospatialworld.net
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© 2017 Jack Henry & Associates, Inc.
• Artificial intelligence is intelligence exhibited by machines. In
computer science, an ideal "intelligent" machine is a flexible
rational agent that perceives its environment and takes actions
that maximize its chance of success at some goal.
• Machine learning is programming computers to optimize a
performance objective using example data or past experience.
• Today’s so-called A.I. systems are merely advanced machine
learning software with extensive behavioral algorithms that
adapt themselves to our likes and dislikes
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A.I. vs. Machine Learning: Definitions & Distinctions
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Machines are better at doing math, all the time, without fail or error.
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Math is neutral. Algorithms aren’t.
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“We live in the age of the algorithm.
Increasingly, the decisions that affect
our lives—where we go to school,
whether we get a car loan, how much
we pay for health insurance—are
being made not by humans, but by
mathematical models. In theory, this
should lead to greater fairness…but the
opposite is true.”
--Cathy O’Neil, Author of Weapons of Math Destruction
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© 2017 Jack Henry & Associates, Inc.
• Chatbots are computer programs that simulate conversation with human users, especially over the Internet.
• Popularity of messaging platforms and advancements in artificial intelligence (A.I.) make chatbots viable in limited settings, but…
• …they generally aren’t ready for primetime.
• Most bots aren’t ready to handle the complexities of conversation, and they still depend on human intervention to succeed.
• The best chatbots rely on natural language processing, A.I., and machine learning, aided by thousands of iterations to drive learning…which is why it will take time for chatbots to evolve and why technologists are beginning those iterations now.
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Chatbots
SOURCE: “Chatbots 101: Building Conversational Interfaces: Take A Progressive Approach To Building Chatbots
That Aligns With Your App+ Mobile Strategy”; by Michael Facemire, Julie A. Ask, and Andrew Hogan; 12/12,/16
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“Alexa, ask my bank to pay my gas bill.”
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© 2017 Jack Henry & Associates, Inc.
What makes for a successful chatbot?
Natural language processing is a form of artificial intelligence concerned with the
interactions between computers and human (natural) languages.
processing
Computer systems able to perform tasks that normally require human
intelligence, such as visual perception, speech recognition, decision making,
and language translation.
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Beware the “Uncanny Valley.”
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“Anything that sounds or appears almost, but not quite,
human elicits feelings of eeriness and revulsion.”
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BTW, how should community banks deploy chatbots differently than BigTechs and BigBanks?
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The Chatbot Continuum: Differentiation
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Ded
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BigBanks
BigTech
Community
BanksWealth
Advisor
Full Robo
Advisory
Robo +
Advisor
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Why is video chat not preferable for live support at the limits of self-service?
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Where do most people spend most time on mobile phones?
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When chatbots work…
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© 2017 Jack Henry & Associates, Inc.
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PersoneticsWhite-label banking chatbot
Text-based/Voice-based
▪ Includes functionality such as standard
banking features (check balances,
move money, etc.). However, also
includes a machine-learning component
that leverages predictive messaging
and behavioral insights.
▪ The system can recognize input errors
and even out-of-context inputs.
▪ Currently working with 4 of the top 10
FIs in North America.
Intelligent alerts
Artificial Intelligence
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A chat-based personal finance coach.
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“Here’s how your July is looking…”
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A.I.
Machine Learning
DATA26
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Data Rich, Insight Poor
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GOOD NEWS:Banks have the best data
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BAD NEWS:Banks don’t have enough data.
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Dynamic
Segmentation
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• Persistent vigilance for life-cycle transitions
• Checking account transactions (e.g., tuition check)
• Debit card transactions (e.g., marital license fee)
• Credit card transactions (e.g., baby furniture purchase)
• Social media updates (e.g., new jobs, relationships, moves, etc.)
• END GOAL: humanize digital interactions
• Anticipate needs
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Dynamic Segmentation: Data Analytics Trends
SOURCE: Celent’s “CHANGING THE LANDSCAPE OF CUSTOMER EXPERIENCE WITH ADVANCED ANALYTICS APPLICATIONS
IN BANKING, WEALTH MANAGEMENT, AND INSURANCE”; Karlyn Carnahan, Dan Latimore, & William Trout; October 17, 2016
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• Delivering relevant levels of communications and engagement at scale means activating pertinent customer insights in real time
• Requires gathering, managing, and analyzing massive amounts of data.
• Machine learning
• Artificial intelligence
• Happily, the financial services industry is awash with data; few industries are so fortunate.
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Data Analytics Trends
SOURCE: Celent’s “CHANGING THE LANDSCAPE OF CUSTOMER EXPERIENCE WITH ADVANCED ANALYTICS APPLICATIONS
IN BANKING, WEALTH MANAGEMENT, AND INSURANCE”; Karlyn Carnahan, Dan Latimore, & William Trout; October 17, 2016
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Platforms
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SOURCE: “Beyond Fintech: A Pragmatic Assessment of Disruptive Potential in Financial Services”; World Economic Forum; August 2017
“Platforms…become the dominant model
for delivery of financial services.”
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• Platform
• “A plug-and-play business model that allows multiple
participants (producers and consumers) to connect to it,
interact with each other, and create and exchange value.”
• Successful platforms attract both producers and
consumers, match producers with consumers, and
provide seamless integration among participants.
• Platform Service Providers (PSPs)
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The “Platformification” of Banking (Ron Shevlin)
SOURCE: “The Platformification of Banking” by Cornerstone Advisors; 2017
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So why are platforms a threat to U.S. financial institutions?
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Platforms control distribution,UX, marketing and data.
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“Once a P2P payment is sent, the cash balance will be stored on the Apple Pay virtual card in the Wallet App. That means users have instant access to the cash instead of waiting for it to clear in a traditional bank account.” – www.pymnts.com
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The BigTech Threat Vector
P2PStored Value
Deposit Accounts
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• P2P stored value
• Venmo ($2B)
• Square ($2B?)
• ApplePay ($1B?)
• Retail mobile apps
• Starbucks ($2B)
• HSAs ($45B)
• Robo-advisory ($1.1T)
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Deposit Displacement(in trillions of dollars)
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Digital Personas
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“…we trained everyone to use passwords that were hard for humans to remember but easy for computers to guess.”
--Randall Munroe
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What’s the biggest fraud trend in retail?
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“External shrinkage” is industry speak for self-checkout shoplifting.
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Most Common Types of Fraud Loss for Banks
SO
UR
CE
:”D
igitalC
hannel F
raud M
itig
atio
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Evolv
ing t
o M
obile
-First”
; A
ite
Gro
up;
Novem
ber
2017
Majority of attacks causing FI losses now target consumer accounts.
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• Associating specific devices with
particular customers
• Has customer used device previously for
undisputed transaction?
• 85% of big banks using/implementing
device fingerprinting…
• …but few have linked device fingerprints
to customer’s identity to create digital
personas for authentication across
channels
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Solutions: Device Fingerprinting
SOURCE:”Digital Channel Fraud Mitigation: Evolving to Mobile-First”; Aite Group; November 2017
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Sensor Fusion
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Global Connected Devices
56 SOURCE: Aite Group; “The Internet of Things: An Information Explosion”; May 2017
SOURCE: Ericsson Mobility Report 2016, Aite Group analysis
Global Connected Devices Forecast (in billions), 2015 to e2021
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Internet Data Volumes Grow 21% Annually
57 SOURCE: Aite Group; “The Internet of Things: An Information Explosion”; May 2017
SOURCE: Cisco Visual Networking Index
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Sensors: Costs Go Down; Deployments Go Up
58 SOURCE: Aite Group; “The Internet of Things: An Information Explosion”; May 2017
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Amazon Go store leverages sensors, machine learning, and AI to allow customers to walk in and walk out without going through a checkout line.
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Internet of Things (IoT), Data, Analytics & Decisioning
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sensors data analysis decision
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• More payments for lower amounts
• Shift from periodic (monthly) to real-time, on-demand, a la carte payments…will challenge authorization capacity
• Machine-to-Machine (M2M) payments between machine-based accounts or value stores?
• Unsecured IoT devices amplify power/scale of botnets
• As payments automate, “top of wallet” card becomes defacto “only card” in wallet
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IoT Impacts on Payments and Security
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Emotion A.I.
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Affectiva monitors your face to track your feelings.
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UI UX
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the totality of emotions evoked in a customer when
he/she connects with the bank—and not just the interface.
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UI
UX
engagement
trust
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Doubt
Anxiety
Guilt
Inferiority
Incompetence
Addiction
Helplessness
Despair
Rage
Negative Emotions of Money
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Positive Emotions of Money
Trust
Control
Confidence
Empowerment
Competence
Security
Safety
Peace
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engagement
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