This document discusses innovation in banking and challenges to innovation. It summarizes that innovation in banking should mainly focus on customer experience, analytics, cloud technology, APIs, machine learning, and robotic process automation. It also notes that barriers to innovation include lack of leadership buy-in, budget issues, risk aversion in company culture, and company politics. Open innovation and design thinking can help break down silos and bring together diverse expertise to drive innovation. Data-driven innovation is also challenging as data is often siloed within organizations without a single source of truth or ability to track customers across systems.

1. -1-
Innovation in today's Bank: Challenges,
Trends and Prospects
Kostis Chlouverakis, Group Chief Digital Officer @ Eurobank
Innovation Forum, Athens, June 19 2018

2. One reason analytics remains a hot
topic is that the amount and variety
of data available has skyrocketed,
constantly creating new analytic
challenges.
But even more importantly, analytics
has become an essential part of
digital transformation and therefore
innovation.
Mobile first requires a new approach to
planning, UX design, and development
that puts handheld devices at the
forefront of both strategy and
implementation.
The digital landscape has changed, and
companies have realized that
consumers are now accessing more
content on their mobile devices than
anywhere else.
MAP:MobileFirst–AnalyticsFirst–PeopleFirst
“When you’re trying to design and
implement a new way
of working, you really need a feedback
loop. And that
feedback needs to come from all the
people involved, and
not just what senior management
thinks is the right thing
to do. This helps to foster a culture of
innovation.”
Eddie Copeland, Director of
Government Innovation, Nesta

3. ➢79% increase in POS machines
➢84% increase of debit and credit card transactions
and transfers (+ 24%)
➢47% increase in the value of direct debits for fixed payments
➢29 % increase in the value of internet and mobile banking
transactions
http://greece.greekreporter.com/2017/06/27/how-two-years-of-capital-controls-hurt-greek-trade/
Significantriseofelectronictransactionsin3years
ofcapitalcontrols…….

4. 91%
88%
86% 85%
83%
79%
71%
64% 64%
62%
59%
54% 53% 53% 52% 51%
49% 49% 49%
46% 45%
43%
39% 38%
35% 35%
29% 29% 28%
19%
9%
5% 4%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
90.0%
100.0%
Shareofindividuals
Onlinebankingpenetrationis19%comparedto49%
inEU(average)

5. 76,5% internet penetration
among Greek population
92% Social media
usage among ages 18-29
95% of transactions are
still performed in Cash
85% Social Media
usage from mobile devices
2mileBanking users
are estimated across
Banks
90%penetration among
13-54 at urban places
3/5 of Greeks have a
Smarthpone
0.5 mil mobile
banking users
45%of Greeks go
online via mobile
Yet…….Greeceisonline!

6. DIGITAL
TRANSFORMATION
“It’s no longer the big beating the small, but the fast
beating the slow.”
Eric Pearson, CIO, International Hotel Group (IHG)

7. ThepaceofDTwillaccelerateinthenext5yearsandis
progressingin3mainphasesthatdriveinnovation
• Internet of Things
• 100% penetration of
smartphones/tablets
• Cloud explosion
• AI / Machine Learning
• Younger people start entering
the banking ecosystem
• Mobile net use increase
• Digital culture is spreading
across different industries
• Branches activity decreasing,
but…
• …they get larger and more
digital
• Mobile traffic gets #1
• IT agility
• Reduced tech cost
• Reduced OpEx & CapEx
• Faster response times to
customer needs
• New uses experiences
• Innovative products
• Data connectivity
• Innovation
• New growth
• AI and ML applications

8. How should a “digitized” innovative retail bank
look like
8
Todo
• Mobile payments
• Product innovation
• Digital marketing, ads
• Customer-centric services
• Cost savings
• New customers acquisition
• Retain high-worth customers
• Financial reporting
• Digital signature
• Time to Market
OutcomeTodoOutcomeTodoOutcome
Innovation
Asset Utilization
Sustainability
Todo
• Voice in IoT
• AI chat bots
• E-authorization
• Cross/upsell
• Increase of NPS
• Reduce churn
• New customers acquisition
OutcomeTodoOutcomeTodoOutcome
Customer
Experience
Employee
Productivity
Internal
Efficiencies
• Increase market share
• Increase customer experience
• Improve product development
• Compliance
• Internal efficiencies
• Cost Reduction
• Mobile banking
• Personalized incentives
• Increase staffing
efficiencies
• Reduce dependency on
local branches
• Digital signature
• Reduce time to market
• Compliance
• Cost Reduction
Digitalvalue(fromhighesttolowest)

9. Priorities for abank toget digital: Innovation
opportunities
• Cost reduction and increasing sales (cross-selling, up-selling, acquisition).
• Customer focus: optimizing the customer experience and enhancing customer service in a consistent way: from
cross-channel and multichannel to omnichannel.
• Single customer view & segmentation: connecting systems and processes to achieve the above with an increasing
role for digital channels and (connected) CRM.
• Developing new offerings, products and services that are adapted to the evolving customer reality, competitive
landscape and area of activities.
• Branch consolidation (and innovation), among others for cost reduction purposes, but also to introduce new – cost-
effective and customer-adapted – ways to digitize specific processes and tasks (with experiments to see what works).
• Analytics (AI & ML) and ways to combine all interactions, information and – predominantly unstructured – data to
optimize customer service and touchpoints, while having dashboards.
• Identify silos that are omnipresent in all business functions and processes, and find ways to build cases that enable
improved efficiency horizontally.
• The use of digital marketing and customer service strategies to acquire, retain and service customers, adapting
to the digital consumer but also promoting digital services to reduce costs, both in services and marketing spend.

10. Innovation @ state-of-the-art US largest banks
What’s new?Priority
AI Chat bots
Natural Language Processing
Predictive analytics
AI Chat bots / virtual assistants
Fraud
Vision for AI adoption
Bank
• Invested in technology and recently introduced a Contract Intelligence (COiN) platform designed to “analyze legal
documents and extract important data points and clauses.”
• The Emerging Opportunities Engine, uses automated analysis to help “identify clients best positioned for follow-
on equity offerings.”
• Wells Fargo announced the establishment of a new Artificial Intelligence Enterprise Solutions team in February
• In April, the company began piloting an AI-driven chatbot through the Facebook Messenger platform. This
virtual assistant communicates with users to provide account information and helps customers reset their
passwords.
• Launched AI technology with the debut of an intelligent virtual assistant named Erica
• erica is a chatbot leveraging “predictive analytics and cognitive messaging” to provide financial guidance to the
company’s over 45 million customers
• Citibank has recently established a succession of innovative partnerships with cutting edge tech companies to
expand and improve its services
• Particular attention has been given to ecommerce and cybersecurity
• Announced the creation of a new managerial position in connection with U.S. Bank’s Innovation Group: Artificial
Intelligence Innovation Leader.
• Potential application of AI for helping bank associates answer infrequent questions more quickly for it’s customers
IT upgrades for AI adoption
AI bots
• The company’s infrastructure upgrades will help them leverage data and implement artificial intelligence and
machine learning
• The company’s initial focus has been on the consolidation of its data centers and a major shift to an “internal
cloud environment.”
• They are banking on “bots,” specifically robotic process automation (RPA), to improve the efficiency of its
operations and to reduce costs
JPMorgan Chase
Wells Fargo
Bank of America
CitiBank
U.S. Bank
PNC
Bank of NY
Mellon Corp.
“Incorporating artificial intelligence into our mobile banking offering will help customers manage their simple banking needs more efficiently and consistently, which
then allows our specialists in our financial centers to spend more time with customers to understand their more complex needs and help them improve their financial
lives”
Thong Nguyen, president of Retail Banking, Bank of America

11. Value DriverBusiness Objective Key Business Benefits
Warranty Analytics
Customer Support Analytics
Acquisition Analytics
Customer Retention Analytics
Next Best Action
Product Portfolio Optimization
Marketing Mix Optimization
Digital Channel Optimization
Advanced Social Analytics
Pricing Analytics
Human Capital Analytics
Financial Analytics
Fraud Analytics
Analytics Solution
Omni-channel Analytics
Social Monitoring & Listening
B2CValueTree
Voice of the Customer Analytics
Product Design Analytics
Adv. Content & Placement
Analytics Advertising ROI
Multi-Channel Attribution
Gross margins
1.5 – 2.0%
Opportunity win rate 2-4%
Renewal rate
Sales lift/close rate
Revenue increase
1.5 – 2.0%
Sales lift
Marketing spend ROI 5 – 10%
Revenue increase
NPS , Satisfaction
Multiple KPIs 15-20%
Engagement scores 500 bp
Warranty costs 10 – 30 %
Cost of service calls 10-15%
Grow
Revenues
Optimize
Cost and Cash
Business Value
Improve Customer
Acquisition & Retention
Increase Profit Per
Customer
Improve Sales
Effectiveness
Reduce After-Market/
After-Sales Costs
Reduce service ops costs
Drive new customer acquisition
Grow customer wallet share
Improve customer retention
Improve Asset
Utilization
Optimize Resource
Management
Improve talent perform & engagement
Better management of finance
Improve demand generation ROI
Minimize variance on warranty costs
Improve product revenue &
profitability
Grow Customer
Satisfaction
Improve customer experience
Optimize advertising revenue
Improve costs/privacy/security/risk
Cost of service calls 10-15%
Marketing Mix Optimization Marketing spend 5 – 10%
Improve Sales
Effectiveness
Keep sales the same by minimizing
investments

12. Cloud
Big Data Analytics
Robotic Process
Automation
APIs
Blockchain
Machine
Learning
Tosummarize,innovationintomorrow’sBankshould
mainlyfocuson:

13. For the future Bank: a customer-centric AI
innovation strategy should be followed:
Insights
generation
• “You should renew your subscription…”
• “This is your last x-months spend-allocation pie chart (groceries, gas, subscriptions, rent etc” and “here is what you
should do”
Useability
Useability is very important: How easy is it to perform a function. For example, in an app for your savings you have to put
income, name, etc. But you have already given that info. Should that be a core focus? Centralized profiles is an opportunity
(this is a major problem in the US with banks)
Social media
• Gather insights such as complaints, praises, suggestions for improvement and if possible to collect automatically this data on a
monthly basis and try to connect it with internal usage data.
• Produce extremely detailed tailored promotions and insights on the individual level. Nevertheless, social media scraping could lead
to an “Engagement Index” similar to the Net Promoter Score.
Chat bots/virtual
helpers
• This is the holy-grail in state-of-the-art banks that would increase significantly employee performance and efficiency and
also customer satisfaction
e-authorization
• A new “product” that just started lately at Morgan Stanley for VERY EASILY and quickly authorizing transactions from a
customer standpoint
Voice-first
• By 2020, 50% of all searches will be voice searches. Amazon’s Alexa, Google Assistant, Apple’s Siri, and Microsoft’s
Cortana are becoming an increasing part of how consumers manage their lives, and the competition is becoming more
intense as forward-thinking banking organizations start to introduce voice-first solutions. The advanced data capabilities
of voice AI can enhance customer insights with each interaction

14. Challenges

15. Barriers to Innovation: Infusing the culture with
a risk-taking attitude is extremely challenging
Budget / Vision
• Lack of strategy
• No leadership buy-in
• No money
• “wrong” budget allocation
Not acting on signals
• Slow reactions
• Non-expertise to
understand customer
needs or competition
Culture
• Risk averse
• Not able to show shorter-
term results
• Quick wins are the
foundation for innovation
• Too busy to test new things
• Unwillingness to share
ideas/data/results
• Too busy on urgent issues
Politics
• Company politics can
hinder innovation
• People/teams conflicts
Impact on
Innovation
Leadership support is the number one enabler of innovation success.
The second most important enabler is the “ability to test, learn and iterate,
and NOT afraid to fail.”

16. More-inclusiveoperatingmodels,suchasopeninnovation,
designthinking,andco-creationwithpartners,customers,and
suppliers,arenowallembracedaheadoftraditionalR&D
“As our vehicles become smart products, we’ll start to see more of that real-time
customer engagement, where we can really mash up what’s happening in the vehicle
in real time with big data”
- Rachel Nguyen, executive director of Nissan Future Lab Senior Director Buyer
Customer engagement
The embracing of open innovation and design thinking helps break down walls and
brings together people from across the company’s various areas of expertise, spanning
business strategy to technology.
Open Innovation & Design Thinking
“I’ll take amazing talent and a mediocre idea any day over an amazing idea and
mediocre talent, because innovation is more about the who than the what.”
- Bonny Simi, President, JetBlue Technology Ventures
The human factor
Design
Thinking
Open
Innovation
Ways to Innovate
Collaborative Traditional
R&D

17. In anyfirm, digital innovation is driven within silos
toaddress localized problems
• Multiple data sources and 100s of data marts with no standardization
• Inability to track customers through the value chain and poor tracking across platforms and
services
No Single Source of Truth
• Third-party data are not leveraged that could produce valuable insights
• Lack of transparency as several departments collect data about end users but do not share
Lack of Customer-related Data
• Failure to draw out insights from data to address strategic objectives and larger goals and as a
result achieve targeted business outcomes
Data-Centric Perspective on
Implementing Analytics
• Clear ownership of problems does not extend beyond silos
• Segmented approach to Analytics for large integrated business problems disregards the bigger
picture
Small Portions of Large Integrated
Problems
Limited Standardization • Key metrics and data elements lack standard definitions and criteria

18. Questions?
kchlouverakis@eurobank.gr