Big data in fintech ecosystem

Big Data
in the Fintech ecosystem
INFOGRAPHIC
Big Data, present
and future
06
The Big Data revolution
in banking01
Artificial Intelligence
& Big Data applied to
the banking business
02
INTERVIEW
BBVA PayStats03
The best-known banking
data aggregation APIs04
Data in the new
mobile era05

01 The Big Data revolution
in banking
Financial institutions use big data and data mining to collect and analyze
data for a variety of purposes: to attract customers and build their loyalty,
to know how they feel, and to adapt what they offer to their needs.
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Network searches and data consumption,
telephones, smart watches and bracelets, mobile
devices such as tablets and GPS, the Internet of
Things, social networks... The number of everyday
recipients of personal data have grown
exponentially in recent years. Today, users and
companies have more unstructured data available
than ever in the history of humanity. And they
have Data Science, the most efficient, quickest and
cheapest way of ordering and analyzing them to
extract conclusions that are useful for business.
In the present and future scenario for banks
operating as Platforms as a Service (PaaS), their
reception, management, structuring and analysis
of big data will become a competitive advantage
with respect to others. It is not only the revenues
from the use of APIs by third-party startups, there
are also end products (applications) that
accumulate personal data, consumer habits and
day-to-day operations that are a paradise of
opportunities. To a large extent it will make it
possible for banks to reinvent their business.
01.The Big Data revolution within banking
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Customers, at the heart of Big Data
Big data management has a number of objectives
in what is obviously the focus of all banking
operations: their customers. The primary target of
the mass collection of data, their structuring and
analysis is the identification of customer profiles. It
is possible to discover what customers consume,
their interests, their needs... And with this it is
easier to adapt marketing campaigns to the
different customer profiles and improve services to
the extent that they can be personalized. In the
end the banks, like any company, want to improve
their brand image.
The other two major objectives are to understand
how customers relate to the financial products and
what their real commitment is to what their bank
offers them, whether it is a personal loan, relations
with employees or a cell-phone app. Third, and
not least, the aim is to detect tired or unhappy
customers, and in the last resort those with a high
likelihood of abandoning one bank for another
financial institution. The use of machine learning,
an effective mix of big data and artificial
intelligence, allows banks to prevent customers
from leaving them.
Today banks use numerous types of algorithms to
predict conduct related to customers, and even to
their own employees: decision-making trees,
clusters, neuronal networks, text analysis, links and
searches or survival analysis are methods used to
improve the experience of consumers or retain
them.
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In this case, survival analysis is the method used by
banks to establish the moment when a user can
leave a bank. It is able to analyze millions of the
bank's user data and establish the customer life
cycle. The analysis normally has two elements, one
on a scale of 1 to 0 that measures commitment and
another that establishes the duration of the
relationship between the financial
institution and consumers.
Some of the elements that survival analysis can
provide an answer to are:
• When a specific customer could leave a bank.
• When the customer should be moved to a new
segment with new services and benefits.
• Effects that facilitate a better or worse
relationship between the bank and users.
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Big Data and customer segmentation
Customer segmentation is the method by which a
financial institution can create groups of
consumers who share needs and interests.
This is the path towards personalized banking.
Trying to adjust financial products to the needs of
each individual means chasing a mirage, but
segmentation by groups can bring the banks
closer to the goal of adapting the financial offer.
A normal example of this for banks is the
collection of data on the use of credit cards and
analysis of consumption habits based on this use.
Banks can adapt their offer using this work with
Big Data, but they also establish price scales by
financial product according to the type of user, for
example for segmentation of applicants for loans
(each is prepared to pay the right price for the
offer).
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The most commonly used Big Data method in customer segmentation is K-means clustering, a clustering
method used in data mining to make subdivisions of a set using different observations, leaving clusters
around the nearest mean. It is the most effective form of creating typologies of users or customers around
market trends.
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Analysis of feeling
Social networks have become an ideal scenario for
searching, informing, offering and understanding
how consumers feel. But there are millions of
users giving millions of opinions at the same time
on numerous platforms (Twitter, Facebook,
LinkedIn, etc. ), not only in the social networks,
but also through comments in forums or news
aggregators, among others.
Data collection and analysis methods are essential
for measuring the temperature of this
environment and for taking specific measures to
build customer loyalty or solve reputational
crises.
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There are two perfect algorithms for analysis of
feeling:
● Naive Bayes classifier: it is a probabilistic
classifier based on the Bayes theorem and
simplifying hypotheses. What does this mean?
There is a saying that sums up perfectly how the
naive Bayes algorithm works: if it looks like a duck,
swims like a duck and quacks like a duck, then it
probably is a duck. This naive classifier establishes
how each of these characteristics contributes
independently to the probability of the final
premise.
● Support Vector Machines or SVM: a set of
supervised learning algorithms developed by
Vladimir Vapnik in AT&T. He is now working in the
artificial intelligence team in Facebook. It is a very
commonly used data mining method in machine
learning: based on a set of sample data a support
vector machine can be trained to predict the
classes of a another set of data. This big data
method allows, for example, a forecast of
customer defaults within risk management.

02 Artificial Intelligence & Big Data
applied to the banking business
APIs specializing in technologies like deep learning and machine learning
allow financial entities to define products and segment customers,
efficiently manage risk and detect fraud.
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A large part of the industry, with years of experience
training their teams, designing their strategies and
operating their business niches, either voluntarily or
under obligation, are having to adapt to new market
conditions.
One of the most frequent shifts in this industry,
including retail and investment banking, is how artificial
intelligence can be used as a competitive edge to earn
money old -and new- style.
Methods like machine learning and deep learning are
helping entities in many different operational fields.
Logically, APIs specializing in machine learning and
deep learning are the starting point for any
transformation. They allow banks to create finalist
products that create value for the entity and its
customers: they allow extracting important information
from Big Data, searching for patterns to tailor offers,
price corrections and detecting bank fraud processes.
02. Artifiicial Intelligence and Big Data

These days there are application development
interfaces that feature natural language processing
or image and voice recognition (deep learning)
and predictive modeling to make estimates
(machine learning).
This can be applied in practice: product and
customer definition (knowing which services are of
interest to each user through customer
segmentation); risk management (lending always
associated with the possible default); and anti-
fraud techniques.
All of this is possible due to the natural evolution
of data equipment in banks: from business
intelligence (SAS Add-ins, Excel and PowerPoint) to
data science machines (language programming,
for example R, Python and Scala); data
visualization with JavaScript libraries such as D3
and dashboard software such as Tableau; the
open source distributed computing platform
Apache Spark; or the data storage system Apache
Hive, with Apache Hadoop, to view and analyze
data using HiveQL.
Product definition
The three key questions in using machine learning
for product and service definition and the
necessary customer segmentation is where are
banking users coming from, where are they now
and where are they going.
A predictive model must be built which can be
interpreted by the operations teams, with the
customer at the core of the business logic, and
which leads to specific actions.
The idea is to define services that are adapted to
customer needs and interests, by studying
consumer habits and the channels where banking
users show the most commitment.
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The 2007 global crisis had far-reaching consequences on how financial and investment entities and retailers
calculated the risk involved in their business transactions. A recent report by MacKinsey&Company
establishes an interesting change in concept: while these days only 15% of bank risk control falls with
analytics, by 2025 that percentage will rise to 40%. These changes are always progressive and, as the
analysis shows, banks do not need to wait, they can already apply machine learning processes.
This shift of resources in risk management is shown clearly in the following chart, which explains how banks
will change structures to assume the new challenges of the new model, based on Big Data technology as
machine learning:
Risk management
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Today
2025
New areas Analytics Central functions Reporting Operational
processes
0 15
40 15
15
15
20
5
50
25
Profiles of risk-management staff

Not only will more resources be allocated to early
risk detection and not so much to problem
solving. This is a strategic decision with a huge
impact. Teams will also receive training or external
talent will be sought to combat the new forms of
bank risk, mainly cyber attacks. Cybersecurity has
become a strategic goal for companies, and within
the financial sector it is a department of great
value.
Anti-fraud techniques
The use of machine learning to prevent fraud is
based around methods that can be divided into
two general groups: supervised learning and non-
supervised learning.
In machine learning methods, the machine learns
to detect abnormal behavior using a random data
subset, which is classified as fraudulent or not. By
successively repeating this information processing,
the machine improves its predictive capacity and
can prevent possible fraud.
The most commonly-used supervised learning
methods in this case are supervised neural
networks and fuzzy neural networks to prevent
both over-the-phone fraud and credit and debit
card fraud.
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Non-supervised learning, unlike supervised
learning, does not include a sample data set that
allows machine learning, instead the method aims
to identify patterns or similar characteristics to
create subgroups for the total data volume.
There are common methods like Bayes networks
and Markov Hidden Models to establish
probabilities and reduce the uncertainty over
whether financial fraud has actually been
committed.
This is important because, these days, most banks
around the world focus their fight against anti-
fraud on creating pattern models from subsets of
past transactions. Therefore, banks have a low
capacity to prevent fraud committed for the first
time and in real time. Also, those historical models
are not properly up-to-date due to cost reasons.
Another important factor is weighing up customer
satisfaction: financial entities always carefully
consider canceling supposedly fraudulent
transactions due to fear of upsetting the customer
who, unlike what the predictive model says,
performed a legal transaction.
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All transactions
Subsets of transactions for model building Fraud
Legacy fraud solutions

Some financial entities have specialized in solving
such problems. Brighterion is one of the fintechs
that currently stands out due to its machine
learning services to prevent credit card fraud, for
example. The company's products combine up to
10 artificial intelligence technologies, allowing the
machine to learn, predict and take decisions in
real time. It is a cognitive computing platform.
Brighterion includes four anti-fraud products:
● iPrevent: the platform can register and learn the
behavioral and consumer habits of the owner of
any credit cards issued by a bank. The objective:
establish red lines which detect possible abnormal
behavior when using those cards.
● iDetect: this can detect the violation of personal
or security data related to credit cards and
irregular transactions.
● iPredict: risk prevention tool for bank credits.
● iComply: uses non-supervised learning
processes to detect international money-
laundering. The platform receives data from
different sources, always in real time, analyzes the
data and monitors the money flow between
customers and organizations to prevent the
laundering.
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03 BBVA PayStats
data at the service of customers
One of the aims for PayStats API, from BBVA, is to provide information to
third parties, which can then develop quality apps and services to offer
added value to end customers. Juan Murillo, Head of Urban Analysis at
BBVA Data&Analytics, explains how it works.
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INTERVIEW

Marketing companies, developers of market insight
apps, real estate operators, tourism firms... these are
just a few examples of the kinds of companies that
could benefit from information and data extracted
from the PayStats API, developed by BBVA and made
available to third parties.
Juan Murillo, Head of Urban Analysis at BBVA
Data&Analytics, emphasizes that the key strength of
API technology is the automation of data usage
processes. He encourages developers to try out the
data that can be invoked via PayStats to improve a
given business and associated decision-making.
Specifically, PayStats provides usage statistics based
on BBVA credit and debit card activity data. This data
is used to describe economic flows and commercial
dynamics in a given zone, to thus build consumption
patterns, visitor trends, and it allows developing micro
market studies high frequency and is able to find gaps
in supply and areas of opportunity.
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03. BBVA PayStats

Therefore, it is interesting in the area of
geomarketing also, because these data can
measure the sufficiency of neighborhoods; in the
area of tourism, to describe what visitors are at a
certain destination; or in the property, since they
can obtain new valuation metrics commercial.
These are just some examples.
The API represents a large scale evolution of the
initiative set up by BBVA in 2013, when it first
opened a proportion of its data as part of the
Innova Challenge. The development is part of the
bank's digital transformation and helps to drive the
evolution toward an open and collaborative culture.
If you are interested in open financial APIs, you can
try out BBVA's here.
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03. BBVA PayStats

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03. BBVA PayStats
PayStats
BBVA PayStats offers anonymized and aggregated statistical data from
millions of transactions performed with BBVA cards, creating a virtual
map comprised of consumers' habits, demographics and origins. With
this information, updated on a weekly basis, you will be able to gain
knowledge and value for your business.

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03. BBVA PayStats
Sandbox dataset
available, with mock
data to freely test the
API capabilities.
Database includes all
BBVA card transactions in
physical stores in Spain
from 2014 to present day
with monthly, weekly and
daily data
5 main dimensions to
improve customer
behaviour analysis:
Territory, Time, Merchant
Category, Gender and Age.
Information available at
geographical 2 leves:
Zipcodes and 500m x
500m tiles.
PayStats

04 The best-known banking
data aggregation APIs
There are companies that collect and store bank data: information on
accounts, transactions, credit-card operations, loans, investments... These
companies' APIs give entities access to new markets and new customers.
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The aggregation of financial data has become a
great business for some companies in the Fintech
market.
The idea is simple: all the personal and banking
data of a customer is available in a single space,
which makes it much easier to consult information
and operations of all kinds. These great volumes of
data, grouped together into a kind of single
portfolio, can serve as a launch pad for companies
and banks to generate income.
There are many practical reasons why the
aggregation of financial data is a formula for
generating benefits, largely because it is useful for
customers (whether individuals or companies) that
have a diversified investment portfolio.
04. Aggregation APIs

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• It provides a general overview: when you have an
extensive investment portfolio it can become
difficult to get a comprehensive idea of your own
financial situation, unless the whole picture is
available in a single space. This gives a
comprehensive vision and has advantages for
management.
• It provides real knowledge of investment and
consumption habits: the aggregation of financial
data brings gives customers a more accurate
knowledge of how they invest and manage their
money. It includes all the movements of their
accounts and the final destination of their money.
In allows them to manage their expenses better.
• Control of cash assets: some entities facilitate
financial products for their customers dedicated
exclusively to savings. Aggregation of banking
data increases the control people have over the
portfolio dedicated to saving this cash.
• Process automation: when Fintech data
aggregation companies act as suppliers, they
supply data in real time that are as up to date as
possible. They do mechanical work and facilitate
the analysis.

There are three key elements that any bank data
aggregation product or service must provide:
information must be updated as precise as
possible, operating in real time is key when we talk
of monitoring financial data and managing
investment portfolios; second, data aggregation
makes sense if each provider brings together a
broad list of entities; and third, a correct
management of customer credentials.
As of today there are various banking data
aggregators with application programming
interfaces that are very well known by banks and
by their development teams and operations:
Yodlee, Plaid and Kontomatik are three particularly
interesting examples in this sector.
• Yodlee: its API gives access to the financial data
of thousands of financial institutions or
international sources of banking data (a total of
14,500). The API offers authentication features,
user registration (start of the session as
customer), receipt of information related to the
provider's platform, management and
elimination of accounts, transfers and access to
information, invoices, cards, investments,
loans...
Banking data aggregators: APIs
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• Plaid: it is a platform that offers fundamentally
two services, aggregation of bank transactions
and management of financial movements. Plaid
collects and stores large volumes of high quality
real-time bank and financial data. This is a
complex job because there are hundreds of
entities and millions of customers who carry out
transactions every day. In addition, it has a
simple integration platform for creating products
and services thanks to its API.
• Kontomatik: this platform allows banks to
create products and services for their customers
with a more elegant and practical finish.
Services related to bank data aggregation,
transaction information, data treatment and data
analysis. Basically, what Kontomatik offers banks
is access to data on activity and consumption of
financial products by third-party customers, who
operate with other organizations. Therefore, it
aims at gaining new customers, largely because
the Kontomatik API is read-only for banks. The
platform allows the integration of a widget in
HTML5 in any website, where bank users can
access and operate with their entities.

05 Data and banking services
in the new mobile era
Banks must adapt to the new scenario of consuming information and
services in the mobile era: smartphones and smart watches, tablets, etc.
Mobile banking and mobile payments are the markets that will condition all
activity in the new legal framework.
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The new ways of consuming information and the
related products have experienced an incredible
turnaround through the impact of the mobile era:
smartphones and smart watches and tablets have
transformed the way in which users, who are also
customers, relate to the offering surrounding
them. This has entailed a significant impact on the
banking business: products are being transformed,
consumer habits are changing, business is
evolving. And no bank can escape that mutation.
The mobile era is the reforming seed with two key
elements: the future of banks lies in their evolution
to a Platform as a Service (PaaS), based on a
strong commitment to application development
interfaces for designing products adapted to new
consumption and the opening of a new business
space in their relationship with third-party
suppliers; and secondly, the arrival of the
European PSD2 legislation (Revised Directive on
Payment Services), amending the entire financial
scenario in the EU because it forces banks to
provide mandatory access to data and payment
services to other companies.
05. The new mobile era
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Who are these other companies? Companies that
are known as fintechs and concentrate their
business in two key sectors: payment initiation
services (PIS) and account information services
(AIS). In both cases, two businesses that base their
consumption habits on digital processes, apps and
the use of smart mobile devices.
Today most people have a smartphone or another
mobile device where they can download banking
and financial apps to consult account transactions
and cards, make transfers and savings plans,
request information on complex financial products,
etc. There are reports that analze this new
environment for the banking business:
● The value of the economy linked to sectors
with mobile products and services will continue to
grow in the coming years. The study ‘The Mobile
Economy 2016’ by GSMA makes a forecast until
2020. It is clear that there is a fairly juicy pie for
companies that decrypt the keys generating
revenue through mobile devices. Banks are
another player in this area. Here is a chart with the
economic trend:
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The economic value added generated by the mobile sector
The economic value added generated by the mobile sector will
continue to grow in the next five years. ($ trillion)
3
3.1
2
1
2015
3.2 3.4
3.5
3.6 3.7
2016 2017 2018 2019 2020
Source: The Mobile Economy 2016, GSMA

• The report ‘Consumers and Mobile Financial
Services 2016’ is a fairly recent in-depth study of
what the mobile market is like in the US. Some of
the figures are revealing: 87% of Americans have
a cell phone, data similar to 2014 and 2013;
77% of those cell phones are smartphones, up
from 71% in 2014 and 61% in 2013.
• The same analysis provides some figures on the
adoption of financial services in the mobile age:
43% of Americans had used a bank account
through their phone in the previous 12 months,
compared with 39% in 2014 and 33% in 2013;
that data rises to 53% in smartphones compared
to 52% in 2014; 28% of smartphone users made
use of mobile payments, especially to pay bills,
purchase digital content and finally purchase a
product in an e-commerce store . This chart
summarizes the trend in all these figures between
2011 and 2015
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Usage of mobile banking an payments by mobile phone type (%)
Evolution of consumer habits of mobile banking and mobile payments by the type of mobile
device used by customers from 2011 to 2015.
2011 2012 2013 2014 2015*
Mobile banking (all mobile phones)
Mobile banking (smartphones)
Mobile payments (all mobile phones)
Mobile payments (smartphones)
22 29 33 39 43
43 50 51 52 53
12 15 17 22 24
23 24 24 28 28
*No directly comparable to prior years due to question change in 2015.

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Mobile-enabled products and services in the developing world Salud Formación Dinero Otros
• The report ‘The Mobile Economy 2015’ by GSMA provides some interesting data and forecasts about new
mobile business for international banking. Credit services through a mobile wallet, which have been
expanded thanks to agreements between operators and banks; mobile insurance, which is in decline; and
financial services related to mobile devices, on the rise thanks to the new mobile era . It is certainly true that
nowadays there are more mobile products and services related to the health sector, but the financial ones
are very much on the rise. .
1.600
1.400
1.200
1.000
800
600
400
200
0
PRE 2009 2009 2010 2011 2012 2013 2014
Note: That ‘others’ includes Disaster response, Energy Access. Green networks, NFC, Smart cities…

Mobile banking and mobile payments are being
taken up rapidly among the population for many
reasons: the burden of physical products such as
cards, coins or banknotes is removed; it is a fairly
flexible method linked to any bank account,
online payment systems like PayPal or
cryptocurrencies such as Bitcoin; and also a more
agile way thanks to the use of communication
technologies and payments such as NFC (Near
Field Communication).
However, customers still prefer other methods of
relationship with banks, whether it be a branch, an
ATM or online banking, to the detriment of other
options such as mobile banking or telephone
banking.
Access to banking services in the mobile era
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Usage of different means of accessing banking services (%)
A significant fraction of mobile banking users have only recently adopted the technology.
ATM
Online banking
Mobile banking
Telephone banking
75
Bank branch 84
71
38
30

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Mobile banking users tend to use their
smartphones to make all kinds of financial
arrangements from their devices: more than 80%
have downloaded their bank's app to make
transactions (balance inquiries, transfers between
their own accounts or accounts of other users,
receiving banking information through or email
notifications, etc.) In some cases, customers have
also made use of a technology known as remote
deposit capture: using the phone's camera to take
a picture of the amount of a check to pay in and
make the deposit.
Normally, when you ask a mobile banking user the
reasons why they use these services, or the same
is done with other users who prefer other types of
access to financial services, the answer always
revolves around three important concepts: ease,
speed and flexibility. The reasons given are always
related to how the bank made those services
available, there are no branches or ATMs near
their home or work or they believe that mobile
banking offers secure services and the ease to
check possible fraud.

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Reasons why you started using mobile banking (%)
I become comfortable with the
security of mobile banking
There is no bank branch or
ATM near my home or work
To receive fraud alert or check my
account for fraudulent transactions
Other
7
My bank started offering
the service
19
3
3
3
I got a smartphone 26
I liked the convenience of
mobile banking
39

The implementation of mobile payments is taking
place progressively throughout the world. Bill
payments, purchasing physical goods or payment
of subscriptions of any kind of content (media or
services like Netflix) are the most common uses by
users, but the habit of buying at mobile sale points
(MPOS) is increasing in customers with a smartphone,
they are associated with a personal account, a debit
card, a credit card or a PayPal-type account.
There are commercial banking customers who do not
make use of mobile payments yet for several reasons:
some believe it is easier to pay with cash or use a
card, while others still do not trust the security of the
method, they do not see real use or understand the
different types of mobile payments.
Mobile payments and security
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Reasons for not using mobile payments
I don’t trust the technology
I don’t really understand all the
different mobile payment options
I don’t have the necessary
featured on my phone
The places I shop don’t accept
mobile payments
I don’t see any benelt from
using mobile payments
I’m concerned about the
security of mobile payments
It’s easier to pay with cash
or a credit/debit card
80
It’s difficult or time consuming to
set up or use mobile payments
I don’t need to make any payments
or someone else pay the bills
67
65
47
36
36
34
25
22

06 Big Data,
present & future
The data are flooding the world at a rate of 40% per year. Here are some
predictions and trends for Big Data to 2023.
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INFOGRAPHIC

06. Big Data, present & future
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Timetable
X 3 X 3
Visualization
The market tools will
grow 2.5 times
faster than the rest
of Business
Intelligence.
Rich content
The analysis of video,
audio and images will
triple this year. Key sector
to the investment.
Clean Data
The companies look for
clean, correct and
quality data.
Cloud data
Over the next five
years, the sector
will grow three
times faster than
the hosted services
in the office.
X 2.5
2015

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Hadoop & NoSql
These technologies will be
a management standard of
Big Data.
Fast Data
Increase of the real time
analysis platforms.
Machine learning
Acceleration from 2016:
growth rate of 65% faster tan
the applications without
predictions.
Internet
of Things
The technology of
sensors analytics will grow
by about 30% in the
companies.
+ 65.5%
+ 30%
2016

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Unity of
architectures
In 2017 the information
management, data analysis
and search technology will
get unify.
Structured
data
Unstructured
data
Big Data
Services
Databases
Sales management
Company resources
Sensors
Finances
Web blogs
Social Media
Audio, video
Excel, CSV
Online
2017

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Shortage of professionals
in 2018, only in the US there will be
180,000 jobs for analysts in depth and
five times more in management data
and interpretation.
50%
Artificial Intelligence
In 2018, half of all users will
interact with cognitive computing
services.
2018

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Population
connnected
Estimated penetration
on Internet:
Market data
Nowadays, the 70% of companies buy
external data. In 2019 this figure will be
100%. They will be monetizing their data
through its sale or adding value.
57%
16%
2005 2015 2019
38%
70%
100%
2015
2019
Decision
management
Management platforms will
grow to an annual rate of 60%
until 2019.
+ 60%
2019

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Data traffic
Big Data grow to an annual rate
of 40%.
High performance
computing
Annual growth of 8.3% to reach $44
billion in 2020. There will be
generated incomes of $220 billion
between 2015 and 2020.
1,2
zettabytes
2012 2020
100.2
zettabytes
100.2 billions
terabytes
2020

www.bbvaopen4u.com
Predictive
analysis
It’s an essential tool to
analyze the viability of
enterprises.
Deep Learning
Allows to analyze personal habits and
realtionships between data, speech and image
recognition and the customized market.
0.38
1.3
2005 2015 2020
0.60
Personalization
Sensors
prices
Estimated average
in dollars
Deep Learnig
Big
Data
Artificial
Intelligence
2021

www.bbvaopen4u.com
Mass personalization
The machines will process all
information and provide
products to indicated people, the
right time and in the appropiate
place.
Augmented Humanity
Data provided by the Smart technology will be
established in the Company and will revolutionize
the transport sectors, the storage and the
manufacturing.
Marketing
future
Most important áreas
in the US
33%
Mobiles
22%
21%
14%
11%
Real time
Social
Big Data
Personalization
2022

www.bbvaopen4u.com
Smart Cities
Over 26 cities will be Smart
in 2025.
Convergence
of industries
The connectivity information will
accelarate the convergence of products,
tecnhologies and competencies.
50% of Smart Cities in
Europe an North America
50%
2023

Ebook: APIs, key in the
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SHAMIR KARKAL
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