The 10 most trusted fraud detection solution providers 2019

+
ThetaRayRendering Exemplary Fraud
Detection Services
Vol. 10 Issue. 03,
2019
The 10 Most Trusted Fraud Detection Solution Providers, 2019
Mark Gazit
CEO
ThetaRay
Secured Vision
Key POS Trends Reshaping
the Retail Sector
Tech Infrastructure
Disruptive Technology and
Changing Trends
Inﬂuencing Business
Mentor’s Standpoint
Beyond Automation:ai
Powered Autonomous
Factories Of The Future
Knowledge Keys
Leadership Skills
Essential For
Organizational Growth
Industry Intel
Eminence at
the edge

E
Man vs Machine
ince its inception Machine Learning has played an
Sinstrumental role in solving few of the critical
business problems, which include accurate
medical diagnosis, email spam, etc. Thanks to the
increasing processing power, advancement in Big Data
and various statistical modeling, the adoption rate of
Machine Learning has grown by leaps and bounds.
With the increased adoption rate of ML, the number of
payment channels, and transactions have also increased,
which have made Fraud Management a painful process
for the Banking, Insurance and Commerce Industries.
Additionally, machine learning models tend to amplify
some elements of risk. However, most of the banks that
operate under stringent regulatory requirements have the
needed framework in places to minimize the risk
associated with the traditional models.
By keeping these in mind, banks nowadays are
proceeding carefully, and restricting the use of ML
models to various low-risk applications including digital
marketing, which is quite fair due the potential
regulatory, ﬁnancial and reputational impacts that can
happen from frauds.
Who is More Eﬀective
Detecting a Fraud?

Also as humans, we have the tendency to find a face
behind any decision, whom we can ask questions and
understand how decisions are being made. On the other
hand, we often have hard time to trust the decision of an
algorithm mainly because of the less transparency. Also
before using any new algorithm, organizations have to
review and check them for any errors. Additionally,
organizations also have to make sure that the data which
are being fed is completely ethical, immune to any
manipulation and accurate. So, there’re many conditions
that one needs to keep in mind before trusting the results.
Another point is, when people manage algorithms, they
have to think very carefully regarding how they are
setting up their risk assessment levels. If it is not
sensitive enough then it can create a massive loophole,
and risk will increase. Otherwise if the alarm is too
sensitive then it will raise too many alarms, which will
lead to manual sorting through all the risks on a case by
case basis, reducing the effectiveness of the algorithm.
So, with great enthusiasm Insights Success has
shortlisted The 10 Most Trusted Fraud Detection
Solution Providers, 2019, who are working round the
clock to help is clients detect fraud, faster!
Featuring our Cover Story is ThetaRay, with the goal of
transforming the way the world benefits from data. The
organization is dedicated to helping clients at large
financial organizations and Industrial Internet of Things
(IIoT), companies that have become more resilient
against threats.
Also, while flipping the pages don’t forget to go through
the articles and CXOs written by our in-house editorial
team and industry experts respectively.
In the long run ML is going to be
an inseparable partner for
humans. If we misuse it, then it
can be disastrous. If we use it
right, it can be our partner.
Kaustav Roy

08
TABLE OF
CONTENTS
ThetaRay
Rendering
Exemplary Fraud
Detection Services
Cover Story

22 30
2618
34 2038
Fraud Detector in
BFSI Market
Making KYC an
Easy Aﬀair
Emre Sayin
CEO
Victor Fredung
CEO
BEYOND AUTOMATION:
AI powered Autonomous
Factories of the Future
Knowledge Keys
Leadership skills
essential for
organizational growth
Industry Intel
Eminence at
the edge
Secured Vision
Key POS Trends
Reshaping the
Retail Sector
Tech Infrastructure
Disruptive
Technology and
Changing Trends
Inﬂuencing
Business
Emre Sayin
CEO
Victor Fredung
CEO
Emre Sayin
CEO

sales@insightssuccess.com
October , 2019
Editor-in-Chief
Co-designerSenior Sales Manager Business Development Manager
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Technical Head
Technical Specialist Digital Marketing Manager
Research Analyst
Database Management Technology Consultant
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Managing Editor
Anish Miller
Executive Editor
Kaustav Roy
Assistant Editors
Jenny Fernandes
Rohit Chaturvedi
Visualizer
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Associate Designer
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Business Development Executives
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Rendering Exemplary Fraud Detection Services
Our advanced platform for inancial crime
detection provides focus on irregular activities
and prioritizes transaction patterns that require
further examination.
““
The 10 Most Trusted Fraud Detection Solution Providers 2019

Mark Gazit
CEO
ThetaRay
Fighting Financial Crime

ThetaRay protects banks against inancial,
operational and reputational damage by
immediately detecting sophisticated fraud, money
laundering and ATM hacking threats.
“ “
In an interview with 'Insights Success', Mark Gazit,
CEO of ThetaRay shares insights on how the
company helps clients at large financial
organiza ons, cyber security divisions and cri cal
infrastructure become more resilient and seize
opportuni es. Also, he broadly discusses the company's
core competencies and the services it offers.
Below are the highlights of the interview conducted
between Mark and Insights Success:
Give a brief overview of the company and its vision.
ThetaRay was founded in 2013 by acclaimed
mathema cians Prof. Ronald Coifman and Prof. Amir
Averbuch with the goal of transforming the way the
world benefits from data. We are dedicated to helping
clients at large financial organiza ons and Industrial
Internet of Things (IIoT), companies that have become
more resilient against threats.
Our advanced analy cal solu ons are based on AI and
machine learning technologies, built on proprietary
algorithms developed by Coifman and Averbuch
throughout 15 years of research. Their breakthrough
technology, hyper-dimensional mul -domain big data
analy cs, has the dis nc ve ability to fuse and analyze
massive amounts of heterogeneous data from diverse
sources like network traffic, financial transac ons and

database records. This holis c, all-seeing technology
provides automa c, unsupervised, real me discovery of
the "unknown unknowns" -- threats and risks that are
not detected by exis ng rule-based solu ons.
ThetaRay lets math discover meaning in the data
without making any assump ons. It has no need for any
seman c or contextual understanding, predetermined
pa erns, rules or other known elements. The
technology operates with unprecedented speed,
accuracy and scale, enabling clients to manage risk,
detect money laundering schemes, uncover fraud,
expose bad loans, iden fy ATM hacking, and more. With
offices in Israel, NY, London and Singapore, ThetaRay is
privately backed and has raised over $65M in
investment. The company is led by cyber expert CEO
Mark Gazit.
How do you diversify your products and solu ons in
order to benefit your customers?
Our pla orm is industry-agnos c, so it can be used in
any sector. However, we currently focus on the financial
and IIoT sectors. Our three financial services solu ons
are:
An -Money Laundering: Today's criminal organiza ons
are intelligent enough to bypass exis ng AML rules and
knowledge. When they launder money, they use small
transac ons that look legi mate. However, ThetaRay
iden fies anomalous pa erns of behavior that suggest
money laundering is taking place, allowing banks to
intercept the crime in its ini al phases.
Fraud detec on: With the transi on to digital, banks
must manage a acks exploi ng all their new and
exis ng channels and products. With unsupervised
machine learning, they can detect fraud without
predefined thresholds or assump ons.
ATM Security: We are seeing organized a acks on ATM
control networks. Instead of crea ng skimming devices
and trying to fool the machines themselves, these
criminals are engaging in massive a acks that penetrate
the management and control networks of ATMs and
make them distribute large amounts of money. When
this occurs, it is a large-scale event that is almost
impossible to iden fy in real- me. However, ThetaRay
can detect it in its earliest stages.
Describe the experiences, achievements or lessons
learnt that have shaped the journey of ThetaRay.
We ini ally launched the company as a cybersecurity
provider for cri cal infrastructure but, instead of hun ng
viruses, our technology looked for slight anomalies in
everyday processes. This allowed it to detect both
cyber-a acks and general equipment malfunc ons. We
later realized that financial organiza ons face similar
risks as cri cal infrastructure, but with greater financial
losses at stake. As a result, we shi ed our focus to the
financial sector.
What are the evident challenges in the Fraud detec on
Solu ons industry?
The key issue is that the banking industry's tradi onal
rule based fraud detec on methods do not work
anymore, because most fraud now takes place online
and most criminals know the rule thresholds. Add to
that the fact that financial organiza ons are genera ng
massive amounts of data, and you can understand why
they might feel helpless against new types of threats.
Describe the significance of machine learning in fraud
detec on space.
Machine learning is an important trend in fraud
detec on, since tradi onal solu ons are incapable of
detec ng today's complex threats. Even the industry's
regulatory bodies have begun sugges ng that banks use
new technologies such as AI. However, not all machine
learning solu ons are created equal, and we are seeing
industry confusion over three types:
Supervised machine learning: This is what most of the
vendors are using, and it's essen ally the same thing as
the old rules-based systems: you tell the machine what
to look for and it finds it. Unfortunately, new and
unfamiliar schemes are missed en rely.
Unsupervised machine learning: A few companies are
offering unsupervised machine learning solu ons, which
can detect unknown threats based on anomalous
behavior. Unfortunately, this all takes place in a 'black
box,’ so banks cannot submit SARs based on these
conclusions.

Intui ve machine learning: This is what we call our form
of AI, which is unsupervised yet transparent. It is the
only machine learning-based solu on on the market
that completely explains every decision it makes, and
thus enables banks to submit suspicious ac vity reports
(SARs), which is a cri cal point for regulators.
What are the current trends that are driving the
industry?
We see two key trends driving AML and fraud detec on
today:
Ongoing and escala ng penal es: Even though banks are
using fraud detec on and an -money laundering
solu ons, they con nue to get fined – and even indicted
– for AML viola ons. A big part of the reason for this is
because criminal enterprises are using extremely
sophis cated techniques to funnel and cleanse money
that is used to finance human trafficking, drug
trafficking, terror financing and other illegal opera ons.
These groups know the rules and thresholds that banks'
detec on systems look for, and subvert them in some
very clever ways.
New regulatory support for AI: The Financial Crimes
Enforcement Network (FinCEN) and all four U.S. Federal
regulatory bodies recently released a Joint Statement on
Innova ve Efforts to Combat Money Laundering and
Terrorist Financing that not only recommends that banks
try AI-based approaches to AML; it essen ally
guarantees financial ins tu ons that they won't face
regulatory ac on if AI finds money laundering events
that their exis ng systems were unable to detect!
Where does ThetaRay envision itself in the long run
and/or what are its future goals?
ThetaRay's groundbreaking unsupervised machine
learning technology is the result of over two decades of
academic research led by world-renowned
mathema cians, Professor Ronald Coifman (Yale
University) and Professor Amir Averbuch (Tel Aviv
University). Their patented approach has been designed
from the ground up to automa cally detect meaningful
anomalies from massive data volumes with
unprecedented accuracy and performance. ThetaRay
was launched to transform how the world benefits from
big data tackling the most difficult challenges. Originally
founded out of Israel, ThetaRay has seen tremendous
demand for its solu ons on a global scale. Thus,
ThetaRay currently has four offices in the US, Israel, UK
& Singapore and is providing its solu ons and support
for each region. Today we are dedicated to helping
clients at great financial ins tu ons make giant strides in
managing risk. Detec ng money laundering schemes,
uncovering fraud, exposing loans that are likely to fail,
and revealing valuable new customers whose credit
scores only tell part of the story. But on the horizon, we
see almost limitless poten al: to support professionals
of every kind, in every industry, make their organiza ons
as resilient as these mes require, safeguarding assets,
recovering from setbacks, and capturing future growth
amid con nuous and wrenching change.
Ÿ Considering the rising number of fraud detec on
solu on providers, how does ThetaRay stand out
from its compe tors?
We have four key differen ators:
Ÿ Full transparency: ThetaRay's machine learning
algorithms were inten onally designed from their
incep on to produce output that is fully supported
by easily accessible forensic evidence. To understand
why ThetaRay has iden fied an ac vity or customer
as suspicious, users can click through the full data
lineage and see every single transforma on that was
made to the raw data -- including how each anomaly
was iden fied through sta s cal comparisons to pre-
established 'normal behavior.’
Ÿ High detec on rate: Because our technology
analyzes massive amounts of data, our detec on rate
is 5-10x greater than compe ng solu ons.
Ÿ Low false posi ves: The biggest problem with
today's threat detec on systems is their staggering
false posi ve rates. In some cases, 99.5% of the
alarms generated are false. This creates 'detec on
fa gue' for companies, making it easy for them to
overlook real a acks when they take place. Our false
posi ve rate is 10 to 100 mes lower than that of
compe ve solu ons.

Ÿ Always up to date: Our system
doesn't rely on any rules, so it's
always up to date. It educates
itself via deep machine learning,
and updates automa cally.
Clientele Assessments
A mul na onal bank that had
previously failed to detect its
exposure to the Russian Laundromat
money-laundering scheme
contracted ThetaRay to conduct a
review of its correspondent banking
ac vity over the previous six years.
The bank was pleasantly surprised
by the results of a project they
ini ally believed to be "out-of-
reach." However, ThetaRay
algorithms were able to seamlessly
parse through 200-million SWIFT
messages, which are notorious for
their data quality fric ons, to
illuminate the client's
correspondent-banking black hole.
Using SWIFT messages alone,
ThetaRay uncovered three new
money-laundering pa erns
contamina ng the bank's business,
which they had previously thought
they caught all of the money
laundering schemes occurring at the
bank.
A er-1 bank engaged ThetaRay to
analyze over 12 months of data,
including 45-million transac ons and
over 100,000 business customers,
and was jolted by the discovery of
ﬁve new conﬁrmed money
laundering pa erns. While we not
only detected unknown events, in
this Pilot, ThetaRay's analy cs
pla orm detected 100% of the true
posi ves that the bank's legacy
system detected.

Our transformational technology.
Your unique challenges.
“ “
About the CEO
Mark Gazit CEO ThetaRay is the founding of and has played a crucial role
in growing and guiding the business since its inception. He is one of the
top cyber security experts in Israel, with a longstanding reputation dating
back to his cyber security service in the Israeli Air Force. Mark is a
prominent senior executive with 20 years of experience in Israeli and
international high-tech companies. Prior to ThetaRay, he served as
Managing Director of Nice Track, which provides software and hardware
solutions to government agencies worldwide in the areas of information
intelligence and cyber. He was also the Group President & CEO of
SkyVision, which he took from the start-up stage to an international
company serving over 50 countries worldwide. Mark has held additional
pivotal roles in leading companies.

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BEYOND
AUTOMATION:
AI powered Autonomous
Factories of the Future
Rise of the industrial robots in 1980s led to a
major evolution of Henry Ford’s assembly line
concept. Routine tasks that were being handled
by factory workers started to be performed by robots with
greater accuracy and efficiency. With the spread of
device-to-device communication and IoT protocols,
robots started taking a serious amount of responsibility
from factory workers. This movement was initially led by
th
German high tech firms that branded this shift as the 4
industrial revolution. On the opposite side, there was
China, taking advantage of their access to low cost
human labour as an alternative to automation. Very
recently, a few years ago, China flipped their strategy 180
degrees, becoming the biggest advocate of automation by
starting to invest aggressively on buying robots and
developing their own robotics know-how led by
government initiatives. So the technology won. There is
no question about the need for automation anymore and
major manufacturing companies, including the ones in
China, Germany and rest of the world are racing to
automate their industrial processes to increase
productivity.
Everything that can be automated will be
automated, according to Zuboff’s law.
However, some things are more difficult
to be automated than other things.
Those are non-repetitive tasks which
require higher level of cognition and
ability to adapt into unknown
conditions. These tasks can be
defined as the last mile in factory
automation. Tesla, the electric car
manufacturer, was recently
criticized by analysts for
automating their assembly line
more than necessary. The
reason of the criticism was the
fact that the cost of
automation exceeded the cost
of a human led assembly
Daghan Cam
Co-founder & CEO
Ai Build
18 | October 2019 www.insightssuccess.com

line. This may be a valid argument for
today but the solution should be found
in increasing the bar for commercially
viable automation and not by going
back to lower levels of automation.
So if we are moving towards full
automation and lights-out
manufacturing, how can the last mile
in factories be automated without
overspending and over engineering?
Autonomy seems to be the answer,
therefore Artificial Intelligence…
There is a slight but important
difference between Automation and
Autonomy. Autonomy is the state of
being able to make independent
decisions in situations that were not
experienced before. By definition,
autonomous systems are always
automated, but the other way around is
not always the case. A system may be
fully automated but not autonomous.
“Blind Automation” is the term we use
for this category.
Systems that are blindly automated are
based on hard coded rules, as in expert
systems, and they tend to be very
difficult to be reconfigured if the
conditions or requirements change over
time. Autonomous systems, in contrast,
generalize the rules for decision
making in different scenarios by
allowing a higher level of abstraction
in their programming.
For example, in the blind automation
scenario, a robot may be programmed
to go to coordinates x, y, z in order to
pick up an object. This will work
without problems as long as the object
is found precisely at coordinates x, y, z.
However, if for any reason the object is
positioned at a slightly different
location, the robot will still go to x, y,
z, fail to pick the object up and
continue executing the rest of its
commands without noticing this
problem. Eventually every step
subsequent to this failure will also fail.
A better strategy for performing the
same task, is using a robot with vision
sensors, and programming it to go to
the object’s position (as a variable) to
pick it up. Regardless of where the
object is positioned, robot will localize
it using its sensors and successfully
pick it up. This difference between the
two programming paradigms; “go to x,
y, z” and “go to an object’s position”
makes a big difference in making the
system less prone to making mistakes
in unexpected conditions which is
crucial for last mile factory
automation.
Robots making hundreds of decisions
every second and receiving hundreds
of measurements from the environment
and from other devices also require
massive computation and storage
capabilities. Despite the popularity of
cloud computing in most applications
today, a decentralized compute power
is necessary for most robotic
applications and for IoT devices in
general. The throughput between the
edge and the cloud through internet
connectivity is simply not enough to
process all data generated by sensors
and make sensible decisions in real-
time. So edge computing, or fog
computing - dedicated servers on
premise as a layer between individual
devices and the cloud - will likely be
the norm in autonomous factories of
the future unless we experience a
breakthrough in science that leads to
infinitely fast and reliable internet
connectivity. However unlike compute
power, storage of important data, that
is filtered on the edge, should be
centralized in order to achieve the best
decision making models at every
location.
As a summary, a few conclusions and
predictions about the future are:
Ÿ We are moving towards an
autonomous black-box factory
model where the factory robots are
operated by themselves without
human intervention.
Ÿ These factories will be powered by
the data that is generated by
themselves and their productivity
will increase over time exceeding
human performance in every
process involved in manufacturing.
Ÿ Factories will compute on-premise /
on-edge and they will push
important data to cloud for
benefiting from a shared pool of
data with other factories.
www.ai-build.com
In this scenario where robots make
autonomous decisions by using real-
time sensor data, a feedback loop is
established between physical and
digital environments. The constant
flow of information from physical to
digital and vice versa create immense
amounts of structured data which is the
fuel for AI powered autonomous
factories of the future. Such action-
measurement-action strategies allow
the systems to self-improve over time,
creating a data network effect in
manufacturing. The more one process
is executed, the more efficient it will
get. This allows super-human
performance in almost every domain
given enough data.
Daghan Cam is the Co-founder
and CEO of Ai Build, a London
based startup developing
Artificial Intelligence and
Robotics technologies for the
construction industry. He is also
a visiting lecturer at University
College London doing research
on robotic fabrication, large scale
3d printing and parallel
algorithms with GPU computing.
His work focuses on developing
intelligence for automating
complex tasks in design and
manufacturing by using
computer vision and machine
learning techniques.
About the Author
19|October 2019www.insightssuccess.com

In recent times, the retail industry hasn’t seen a more
exciting invention since the invention of cash register.
With new and innovative technologies helping shape
both online and offline experiences for consumers, the
landscape is continuously changing in a way which was
unimaginable even few years back. The best part is that
there seems to be no end of the innovation, which only
influencing the purchase decision of the consumers.
Nowadays the main focus of retailers is to create a safe,
engaging, and unique shopping experience for its
consumers, it’s very important for the retailers to
understand the importance of Big Data and in-store
analytics and adapting to the cloud. With the retail industry
at the verge of massive transformation, we are listing out
few key trends that everyone needs to know to be
successful in the ecosystem that is transforming quickly.
Multi-system Integration
Multi-system integration with various applications gets the
utmost priority from top retailers. Most of the retailers list
out POS integration with other applications as a key priority
alongside the implementation of dynamic marketing content
through mobile devices. This is mostly due to the retailer’s
interest to store all the customer information and purchase
history in a database, which is completely centralized that
could be easily integrated with multiple applications.
However, in order to do that, a retailer needs to use an ERP
database that can handle all these.
Speed
People always look for quick solutions for everything. A
clock starts ticking the moment a customer enters, no matter
how good the product is, if the process is slow and the
attention to details are missing, then customers will leave
disappointed. As a retailer, one cannot please everyone, but
with a modern and efficient POS, the service can be
improved. A modern POS simplifies the communication
between various departments and can save a lot of time for
both the retailer and the customer respectively.
Managing Stocks
Keeping and managing inventory is a nightmare for most of
the retailers, and it’s quite natural. Managing inventory is a
never-ending task and takes a lot of effort, time, and
manpower. However, it is quite important to manage
inventories when it comes to long-time survival. An
efficient POS system always makes the process of
managing the inventory much easier. The best part of a POS
is, one can monitor the status of stocked items, shipped
products, and new orders anytime. This is a huge time saver
for a cumbersome and a tedious process, and eventually
helps retailers to focus on other important aspects of
running the business.
Customized Experience
With POS systems, retailers just need to provide
personalization that scoops out every shopper. Every
passing year, retailers are adapting to personalized
technology solutions that allow an interactive user
experience. Thanks to the emergence of all new mobile
POS technology, now retailers can offer its customers more
choices to accommodate their shopping habits by letting
them to complete transactions anywhere in the store. Now
with the invention of improved POS marketers and
customer service teams can contact the buyer at each point
of their purchase decision. With so much data retailers and
consumers can have better customer service, quicker
payment processes and access to better offers and real-time
personalization.
POS Trends
Reshaping
the Reatil Sector

Promotions and Marketing at its Best
Nowadays with the advent of digital technology, marketing
involves maintaining a digital presence as well. A POS can
integrate all the advertised offers with transactions, making
it easier to keep track of all the campaigns. Additionally, it
can integrate with CRM and track customer behavior. When
an offer gets popular among the masses, then the retailer
will see it in his transaction data.
Usage of Big Data analytics
In order to compete with e-commerce, retailers are now
taking the help of Big-Data and in store analytics just to
have a better idea about what’s happening inside the store.
Big-Data analytics helps retailers to track how frequently a
specific item moves from shelf to shopping cart allows
retailers to know the trends that are dominant in the market.
Analytics helps the retail industry in a big way to better
understand consumer purchase pattern and behaviors.
Keeping Track of Employees
To run a business smoothly a retailer, need few people. A
POS system enables to manage them with great accuracy.
With a Point of Sale system in place, employees can sign on
or off easily and the system will automatically log their
work hours and break hours.
Security
Above all, a POS system offers great security protections
that help keeping customer data safe. Retail stores and
businesses are always prime targets for Cyber Criminals,
and a data breach is not good for a business. So, by using
standard encryption and firewall, businesses can be secured
from cyber-attacks and customers can swipe their cards
with a peace of mind.
So, here we have listed out few of the POS trends that will
shape the future of the retail industry. As we look ahead,
these trends will be on focus for both retailers and
customers. The main advantage of an advanced POS system
is greater efficiency and optimization, it links all the
departments together which eventually allows to have better
control over the inventory, better profitability, and to
manage processes in an efficient way.
Secured Vision

I
n the digital age, financial
corrup on against banks,
building socie es, credit
card holders, and other
financial services businesses
are accelera ng rapidly.
Today's financial services
industry is challenged with
increasingly innova ve ways
of commi ng fraud and cybercrime.
From malicious malware to
sophis cated phishing schemes, they
are under constant mul -channel
threat. While there are a number of
fraud detec on so ware solu ons
available for individual internet
banking, mobile banking, and credit
card pla orms, each has its own case
management interface, the majority of
which fail to meet the needs of a
financial service provider.
So, born FCASE, which can improve the
opera onal efficiency by 300% and
close fraud cases ten mes faster. The
company helps Fraud Opera on
Centers to orchestrate their
"Resources", "Data" and "Fraud
Systems".
FCase's next-genera on technologies
integrate different data pla orms,
collec ng informa on from mul ples
fraud detec on sources to manage
fraudulent ac vity in real- me, using
adap ve analy c. The so ware
provides a cross-channel fraud analysis
which empowers fraud-screening
teams to make quicker decisions based
on reliable informa on, overcome
data fric on, and achieves the velocity
of DataOps demanded by modern
financial ins tu ons. The company's
highly skilled team has extensive
exper se in fraud detec on and
preven on technologies which help
banks, building socie es, and credit
companies transform their fraud
management capabili es.
About FCase
Founded in 2007, FCase is situated in
the city of London. With a company
size of more than 50 employees, it is
an Informa on Technology & Services
private held industry. The company is
an end-to-end Fraud Orchestrator
which drives fraud management
systems from basic, standalone
detec on to an enterprise-focused
approach. This holis c view of fraud
data allows standardizing fraud case
interac on, fraud management
processes, governance models, and
performance and quality indicators.
The company's next-genera on
technologies, aggregates dis nct data
pla orms, collec ng informa on from
mul ple fraud detec on sources to
manage fraudulent ac vity in real- me
by using adap ve analy cs. The
organiza on spans the en re financial
crime, risk compliance, and customer
care systems, centralizing alerts and
events into one enterprise-wide
inves ga on pla orm for all your
fraud inves ga on, and fraud
compliance repor ng needs. The
industry consolidates mul -channel
fraud data, simplifies fraud
management, centralizes data
analy cs, and significantly improves
efficiency.
Fraud- Global Problem
Fraud is a global nuisance, and it has
Fraud Detector
in BFSI MarketFcase
been around since the dawn of
commerce. The earliest recorded case
of fraud goes way back to 300 BC, a
Greek merchant named Hegestratos
took out a massive insurance policy on
a shipment of cargo, corn to be exact.
Hegestratos a empted to sink his
boat, sell the corn, and keep the
insurance proceeds but to no avail. In
the end, the plan fell through,
becoming the first fraud example on
record. From this incident, fraudsters
have only evolved in their methods
and schemes to defraud the global
financial system. It robs from the
financial ins tu ons; adversely
impac ng merchants, banks, insurance
companies, and everyday individuals.
Current Challenges to FCase
A few challenges the financial services
industry is facing when it comes to
increased digital fraud includes mul -
channel banking op ons, mobile
dominant customers, and synthe c
iden ty the .
Mul -Channel Banking- Mul -channel
banking refers to the array of services
financial ins tu ons provide their
customers to manage their finances.
Mobile is quickly becoming the
dominant channel, but customers can
also access ATMs, physical branch
loca ons, and telephone to service
their banking needs. Providing more
channels means providing more value
for customers, however, unfortunately,
it also creates a silo effect where
fraudulent ac vi es can be hard to
manage across different channels.
Mobile Dominant Customers- Recent

studies are showing banking customers are going mobile
more than ever. A report by Fiserv shows that mobile is
now the most heavily used banking channel, with
customers accessing mobile banking an average of 8.4
mes within a 30-day period. Financial ins tu ons are
pushing for more their customers to use mobile banking
services as it is by far the most cost-effec ve method. For
example, in 2018 a retail bank spent roughly $4 every me
a customer calls or visits a physical branch.
Synthe c Iden ty The - Synthe c iden ty the is defined
as a type of fraud where criminals parse together real and
fake personal informa on to create a new iden ty, which is
then used to open fraudulent banking accounts or make
fraudulent purchases. This type of fraud has quickly
become the fastest growing and hardest to detect a form
of iden ty the to date. This the is the most common
type of iden ty fraud and is becoming a major source of
losses for financial ins tu ons. Financial ins tu ons must
be diligent in finding ways to prevent synthe c iden ty
the , which is es mated to be the source of 80% of credit
card losses in the industry.
Key to Solu on-
Fraud orchestra on can be the answer to the many
challenges facing financial ins tu ons in this digital age.
Fraud orchestra on creates a centralized pla orm where
fraud ac vity can be viewed across the en re enterprise,
no ma er how many banking channels exist. It creates
ul mate transparency where bank fraud divisions can view
alerts across any channel in real- me. There are several
advantages to fraud orchestra on, which includes:
Ÿ Increases Opera onal Efficiency- A centralized, an -silo
fraud management pla orm which improves
communica on between banking channels increasing
fraud preven on op miza on.
Ÿ Creates enterprise wide transparency- Fraud
orchestra on creates a mission control where fraud
ac vi es are made transparent across the en re
enterprise leading to real- me ac on to catch fraud in
the act.
Ÿ
No need for addi onalDecreases opera onal costs-
staff or training monitoring countless fraud
management systems, fraud orchestra on brings all
fraud systems onto one pla orm.
Ÿ Reduces customer fric on- Faster fraud response
equals less customer fric on, for financial ins tu ons, it
is that simple.
Responsive Leader in Tech Industry
To lead the field of fraud detec on requires vision. With
one such vision, Emre Sayin is the Founder and CEO of
FCase. Finished his educa on from Sabanci University Emre
has a vast experience in the field of technology. He has
been leader in many organiza ons like Pakolina, Inoven,
Abonesepe , Paygilant, Pubinno, and IHS Technology. He
leads his management team and makes quick decisions to
achieve success. He is a skilled leader in web applica on
security, opera ng systems, cryptography, and networking.
At FCASE we help Fraud Operation
Centers to orchestrate their
"Resources", Data And
FDP Systems.
Emre Sayin
CEO
" "
""

Chief Operating Oﬃcer
Mark rees
Secucloud GmbH

C
an one learn leadership? Every winter semester
I pull my students through the whole spectrum
of “Leadership Theory Parts I and II”:
Taylorism; French & Raven; Blanchard; McGregor;
Maslow et al. At the end of the semester I have them test
their knowledge in a tricky exercise involving a climbing
rope and blindfolds. They have to – whilst blindfolded –
form, storm, norm and perform as a team and lay the
rope (which they are not allowed to let go of) in a
predetermined shape on the ground. And yes, they
always achieve the objective – one way or another. In
contrast I have experienced this exercise as a team
member of highly paid and experienced managers and
witnessed first-hand the utter failure to even manage a
plan, let alone achieve even half of the objective.
Although to be fair, what the managers did achieve (as
opposed to the students), was a whopping great conflict
of personal differences, with which the rest of the
seminar was used to sort out. So what was the difference
between these two groups?
A business acquaintance of mine once introduced me to
the “power of trust” as a basic concept of leadership. He
argued that if there is no trust between the leader and the
led, then the possibilities for sustainable leadership are
extremely limited and indeed most probably restricted to
a short period. Without trust in human relationships there
is however a form of leadership and to reference French
Leadership skills
essential for
organizational growth
and Raven, it is the positional power of coerciveness and
reward – the proverbial carrot and stick. That works for a
while as long as the (micro) manager exclusively owns
the processes and information as a means of control. I
believe in the long run, this strategy fails. No (high
performing) employee deliberately hangs around for
very long in such a situation. I´ve seen this happen
numerous times.
Again considering the group of students and the
experienced managers with their climbing ropes and
blindfolds, what I perceived was extreme differences in
levels of trust. The students – in their seventh semester –
were close-nit. They were just entering their final thesis,
they have common objectives and after 3 and half years
spent on campus, an intrinsic trust to one another. In
contrast the managers came from varying business units
of a large company and were “thrown” together to
improve their leadership skills and develop as a
leadership group within the company. Each manager had
his (the group was all male) own agenda and personal
career objectives etc. Each was competing for power and
influence before the board. Instead of collaborating to
achieve the climbing rope objective, all the micro-
politics came out in the exercise and trashed any trust
that might have been there at the outset. The evening log
fire at the beach was a flop.
Trust is the key for sustainable development of organizations
Knowledge Keys

Trust as a subject is rationally difficult to grasp. It is one
of those things we intuitively feel as being present (or
not) in human relationships. To get a grip on the term, let
us start with Peter Drucker´s definition of management
“turning resources into production”. Now consider that
what leadership does in a company is to apply strategies
(resources) to achieve objectives (production). On the
one hand the objectives should be such that they inspire
people, bringing out their best qualities both in skills and
collaboration. Secondly and as a rule, it is the leader who
drives the strategy in order to achieve those objectives
and this is the key area where trust within the team and
across teams can emerge. It is the means utilized to
achieve the ends that define so much about an enterprise.
There are of course coercive means, non-compliant
means, even illegal means etc., thus the old excuse for
bad behavior “the ends justify the means”. In such an
environment sub groups form, secrecy becomes endemic
and there can only be mistrust and suspicion between
people.
Approaching the skill of leadership whith consideration
of social and ecological factors allows decisions to be
made under the reflection for example, of waste
reduction, for the good of the many, for sustainable
growth etc. In utilizing social and ecological factors in
decision-making processes, our unique human form of
emotional intelligence is an influential force that
significantly influences behavior both of oneself as a
leader, and as a member of the collective (the business
unit being led). Over time common values and
understanding emerge in the group, which in turn
develop (forms and storms) and fortifies the culture (the
norms), thus producing the high performance unit that
every person blessed with the opportunity to lead can
aspire to. If you get there it’s the best job in the world!
Trust is a human trait that is available to us all. You can´t
simply buy it by attending a leadership seminar, doing an
MBA or reading a book. Trust has to be earned and
shared unconditionally as a gift. It can be rejected,
withheld, it is breakable and can be destroyed in a
second. Yet for all its non-tangibility and fragility, it has
more power to achieve than anything else in the
leadership toolbox.
About the Author
Mark Rees is the Chief Operating
recfO at Secucloud GmbH,
Hamburg. In his last position Mark
Rees was Managing Director of E-POST
Development GmbH in Berlin, a
subsidiary of Deutsche Post. There, he
led an international team comprised
of several hundred employees in agile
DevOps development teams, and was
responsible for employee direction, as
well as the entire budget in the areas
of IT security, DevOps, quality
assurance, operations and user
experience. In his work as COO at
Secucloud, Rees applies his expertise
in agile leadership and his many
years of experience in interdisciplinary
IT organizations in the Media and IT
security industries.

Shu i Pro, a global AI-based
iden ty verifica on service
provider, delivers seamless
KYC/AML solu ons to its diverse
clientele. With clients from every
corner of the world, it offers a range of
services like document verifica on,
consent verifica on, ID verifica on,
face verifica on, address verifica on,
and supports 150+ languages. It
provides quick results with 98.67%
accuracy using a hybrid of Human
Intelligence and Ar ficial Intelligence.
Shu i Pro envisions to become the
leader in the global iden ty
verifica on industry and contribute
towards making cyberspaces fraud-
free. It provides low-priced high-
quality services and con nuously
updates its databases and services to
achieve its long-term objec ves.
Programs that are Constantly Being
Updated
Shu i Pro remains updated with
changes in global due diligence,
KYC/AML, and data protec on
regula ons. It stays in touch with
regulators to deal with unprecedented
issues in global compliance. Its
exhaus ve databases are updated
every 15 minutes, with global sanc on
lists, watch lists, PEPs lists, etc.
Moreover, Shu i Pro constantly
improves its services through
technological advancements. For
instance, in 2019, it enhanced its API
integra on by providing Auto Code
Generator for swi and seamless
integra on.
The Skipper
Victor Fredung, the CEO of Shu i Pro,
is a fintech innovator with significant
experience in the industry. He knows
the financial domain inside out.
The organiza on flourished under his
supervision and became a remarkable
name in iden ty verifica on and fraud
preven on. Victor enhanced the
customer base of Shu i Pro and
developed a team of experienced and
dedicated professionals.
Making KYC an
Easy Affair
The Great Journey
The company gained global acclaim in
a short period, increasing the value of
the company manifolds.
Shu i Pro prac cally bridged the gaps
between global economies by serving
businesses in developed countries like
the USA and un-recognized countries,
such as Sealand. It served clients by
verifying people in more than 230
countries and territories within two
years of its founda on.
Technological advancements and
product enhancement is a con nuous
process at Shu i Pro. In 2018, the
company started u lizing OCR
technology for data extrac on and
expanded its services to AML/PEP
screening. The industrial challenges
mo vated Shu i Pro to deliver
services more vigilantly. By serving a
diverse clientele, the organiza on also
learned new ways to secure the cyber
world. Shu i Pro has been represented
at several global expos and developed
good rela onships with businesses
and global organiza ons.
Tackling Challenges and Preparing for
Be er Future
According to the organiza on,
cybercrime is the biggest challenge,
and it aims to provide an excep onal
fraud preven on solu on to tackle it.
For Shu i Pro, understanding the
regula ons is not enough, and
so ware must be enhanced with

regard to the latest technologies and techniques used
in cybercrime.
Another challenge for the company is the lack of
awareness among businesses regarding the need for
KYC and AML compliance. It is vital to improve
apprehension among the masses regarding the
significance of digital KYC/AML compliance and fraud
preven on to achieve its long-term goals.
The best part is, Shu i Pro stands out among its
compe tors due to its unparalleled services given at
compe ve prices. It delivers quality services at more
affordable prices as compared to its compe tors. The
low price does not affect the quality of Shu i Pro's
results, it delivers them in real- me with 98.67%
precision.
The Middle East Market and Arabic OCR
The Middle East offers a plethora of opportuni es for
banks and fintech companies but Arabic is one of the
most difficult languages for op cal character
recogni on (OCR). But Shu i Pro loves challenges, and
provides state of the art Arabic OCR with incredible
accuracy. Only a handful of iden ty verifica on services
cater to The Middle Eastern Market, and Shu i Pro is
among them.
Shu i Pro envisions itself as a leader in the global
iden ty verifica on and fraud preven on industry,
playing a remarkable role in making cyberspace fraud-
free.
It aims at enhancing services to an extent where no
compe tor could match its quality. Reducing the me
span of verifica on process and securing a bigger share
in the global market are its two major goals.
Sa sfied Clientele
We have a diverse clientele. Kindly visit the 'Press
Release' page on our website Shu iPro(.)com to read
the tes monials of our sa sfied clients.
Shufti Pro is a global identity
verification and KYC/AML
service provider. It uses Hybrid
(HI & AI) approach for achieving
its vision of becoming a leader
in the industry.
Victor Fredung
CEO
“
“

xecutive summary
ESince Internet of Things technology
started to gain mainstream traction,
multiple platforms, solutions and
strategies have been developed. At the
moment there are more than 450
‘platforms’ commercially available.
Yet, realistically speaking, most of
these have been designed for a very
specific function on out-dated
technology and mostly down a vertical
application path.
Similarly, gateway players have
developed powerful gateway
technology with a portion that
generically aggregates data to the
cloud.
Why? Well, historically, technology
companies argued that the best way to
quickly create commercial value was
to develop a strong vertically
integrated application encompassing an
ecosystem of partners.
The quickest way to show value was to
focus on a vertical and go after it. We
have a different view.
once mass adoption took place. This
was followed by an implosion which
saw a huge number of concepts, ideas
and investments disappear. A similar
trend is developing in the adoption of
IoT and in digitalisation in general.
Part of the demise of this is because
they were too early on the initial curve
and either ran out of cash, were unable
to build what they said they could, or
saw new, sexier, more agile technology
drive competitors closer to adoption.
The .com bomb was a rationalisation
and a reality for companies and their
investors resulting in fortunes being
made and lost in the hype. Timing is
key in driving Big Tech. If you’re too
soon, you are potentially busy
developing a concept that will not only
age quickly but give competitors
plenty to learn from and piggy back off
The true power and differentiator
in IoT.nxt resides in our full IoT
stack capability encompassing the
edge and the cloud.
Background
Our thinking from the outset has been
that we wanted to adopt thinking and
develop tech that creates horizontal
interoperability between multiple
systems and platforms in a technology
agnostic manner.
In 2002, it was all about the cloud.
Amazon Web Services was launched
and, when OPC Unified Architecture
was released in 2006 enabling secure
communication between devices, data
sources and applications, adoption of
IoT began to rise. The early adopters
developed their projects with the cloud
in mind. The thinking being a simple
connected mindset where billions of
sensors will be deployed and easily
spin up supercomputers at low cost in
the cloud to process all of this valuable
Big Data… how could they go wrong?
During the .com bomb era, people ran
around with amazing ideas that they
thought would take over the world
minence

CXO 2
allowing them to develop better tech that is more relevant
and value driven. Often a cool idea is exactly that - a cool
idea, but without real substance it doesn’t get wide
commercial adoption. The commercial viability ultimately
sits with the ability of a product to produce ‘real value’,
whether quantitative or qualitative.
And then there are the guys who make it.
Amazon, Alibaba, Google. They were unprofitable for a
number of years before they started to bear fruit simply
because they played the long game. They saw past the hype
and created products of real value. They made sure they
will be relevant in future economies.
The importance of timing
Timing is everything, and tech is hard to time
We entered this market at the perfect time. Two years in,
our solution is strong and businesses at enterprise level are
rallying to adopt Big Data technology. They’re embracing
VR, AR, AI, cognitive, algorithmic machine learning
technologies as they become a reality.
As irrelevant solutions are being seeded out, the IoT.nxt
approach to the problem of IoT is making us a major
contender; cementing our position in the market.
If we look at the solutions currently available, we
understand more than most of these ‘platforms’ have all
been built in the cloud. Five years ago everything was in the
cloud, it is therefore unsurprising that it is still dominating
IT discussions.
Anyone who has, up until this point, embarked on an IoT
initiative, has probably
1. built a solution that resides in the cloud;
2. leverages the power of the cloud and its ability to
centralise and leverage processing power from the
supercomputers that exist there;
3. adopted a top down approach incorporating the
cloud as the central power behind the application.
The competitive landscape
Looking at the IoT industry and where the ‘competition’
and ‘incumbents’ are in the current IoT cycle, it is evident
that IoT development are in a perfect bubble that I believe
is not far from rationalisation. I think it will be less severe
than 2000 as I think investors have been more calculated;
but there certainly will be a correction in the not so distant
future. Driving my belief in this is that you need this type of
event for eminence to be created. People need to start
understanding where the true value lies. The companies that
have the ability to lock into this IoT business value
proposition and convert that into investor value will survive
and will gain eminence. There are a number of great
technologies and concepts available but only the ones that
are able to truly unlock value will remain.
What sets us apart
The IoT.nxt approach has been somewhat different, defying
the norm and, to date, it is my firm belief that ours is the
only company that has this unique approach. Addressing the
problems of interconnectivity from the bottom up, our
solution acknowledges the power of the cloud and Big data,
but also acknowledges that power is greatly diminished or
even nullified if the edge layer is not correctly managed.
Our definition of interoperability and data orchestration is,
at times, diluted by platform players claiming to provide the
same. They don’t.
The general platform interoperability discussion talks to
cloud interoperability. This is a hugely complex play that
causes massive headaches for some of the most influential
players as they try to fathom how to seamlessly integrate
multiple platforms. API’s are the talk of the day, with the
current solution to solving this dilemma, but it is simply not
sustainable or practical. On a whiteboard it might look great
having several platforms integrated via API and then
plugging into some ESB via microservices, but I challenge
to you to construct all of that and take into consideration the
small part all of these guys initially did not deem necessary
– the edge.
This methodology is hugely reliant on smart sensor
technology that has the ability to push data into the cloud.
There’s a heavy reliance on networks and, as a result,
‘platforms’ are struggling to grapple with edge technology,
all the while hopeful that a 5G, no, 20G network will
resolve this problem.
At almost all the international conferences we have
attended in the last 24 months the major discussion has
been Big Data and smart sensors, so most of the more
mature platforms have been designed around the premise of
them being able to receive data directly from the sensor.
Industry Intel

The problem now is how to talk back
to the sensor or machine and, more
importantly, how to do this cross
platform. An even bigger issue
creeping to the forefront of discussions
are regarding ecosystems in which near
real-time data feeds are crucial.
Yet still, the focus is on the cloud and
understandably so, especially if you
have invested millions into a
technology that is reliant on the cloud.
We do not believe this.
For some time now we’ve been saying
that the edge is eating the cloud.
We’re not implying that the cloud will
lose relevance. What we’re saying is
that a true IoT ecosystem will become
less and less reliant on the cloud and,
in fact, that ecosystem design will rely
heavily on edge capabilities.
A natural oversight, but a crucial detail
destined to form an integral part of this
industry’s ability to commercialise in
the near future. The IoT industry is
inhibited by an inability to create
interconnectivity and
interoperability at the edge.
Retrofit and decrease the barrier to
entry and sweat the assets.
Correctly designed and engineered,
edge technology enables edge
interoperability and, more importantly,
the ability to retrofit into legacy
systems. Legacy systems, to a large
extent, were disregarded, with current
players relying on the ‘rip and replace
‘mentality that has governed and, to a
degree, plagued the IT industry since
the beginning, befuddling brands that
have become household names.
This mentality of winner takes all is
not congruent with the ideation of a
connected world and certainly does not
embrace the concept of true scalability.
Having to rip out and replace existing
technology and infrastructure on your
journey towards digitalisation
introduces a huge amount of additional
complexity, disruption and a cost, all of
which makes it a difficult sale to the
business, contributing to the slow
adoption rate of the 4th Industrial
Revolution.
So whilst the ‘big dogs’ are all trying
to figure how they can develop and
ensure technology lock-in to secure
future revenue, they’re contributing
towards the mixed message that is
being sent out to the market, diluting
the value of IoT technology as a tool to
unlocking real business value.
Value is a simple exercise for any
business leader – Look at expenditure,
then ROI. Satisfied? Great. Here’s the
next question - is it relevant to my
business?
All data is not the answer.
When we enter into discussions with
big companies, the issue of legacy
investments in technology at the edge
comes up without fail. Remember that
everyone is selling some type of cloud
platform that is going to ‘change the
business’, but that cloud engine is
reliant on edge data i.e. devices,
sensors, machines, protocols, PLCs,
SCADAs, CCTV, access control
systems - the list goes on and on.
Clients start considering negotiating
with each vendor and realising that,
much like when our 1000 piece holiday
puzzles has 1 missing piece can ruin
the picture and make the whole
exercise seem futile. It’s the same with
many of the algorithms and predictive
applications - the true power of these
platforms lie in their ability to provide
companies with insights. For this they
are 100% dependent on having the
correct, filtered, aggregated, curated,
secure, real-time data from the edge,
and they need all the pieces of the data
puzzle to build the Big Data picture.
In every environment, on every piece
of the puzzle there is information that
is critical to the task at hand, and then
there’s other information that isn’t
needed in real-time. Things like
whether a device needs to be serviced
in a weeks’ time, whether stock is
going to be depleted by the end of the
month, etc. Now consider a sensor
having a fixed normal range, and only
recording exceptions rather than all
data all the time - you’re able to reduce
the amount of data passed by around
60%- 90% in real time monitoring
environments, as a basic statistic.
We are throwing away the rule book.
While the rest of the industry
scrambles to figure out how to
showcase the exponential value of IoT
whilst also attempting to lock clients in
to their technology stack, we’re taking
the IoT rule book and throwing it out
of the window.
We don’t care what technology our
clients have now, and what technology
they will have in five years’ time. We
don’t talk about vendors, we talk
protocols. We’re driving our clients to
get to Big Data quicker, using what
they have, thanks to our trademarked
Raptor.
Raptor technology is the missing link
in most of the discussions around
digitalisation. A normalised, edge
layer of physical and virtual
intelligence that can be retrofitted,
deployed and connected seamlessly
into an ecosystem of existing
technologies and things, radically
reducing the cost and time of having
to develop multiple edge integrations
into disparate cloud applications.
The IoT.nxt Power-play
Being able to retrofit onto all deployed
devices, whether analog-, or IP-based
has a huge benefit.

Ÿ It reduces disruption to business processes,
Ÿ cost of implementation,
Ÿ cost of training,
Ÿ cost and impact of enterprise-wide change management,
Ÿ reduces vulnerability and cyber risk because of less
technology disparity at the edge,
Ÿ reduces data moving across the network that
Ÿ reduces the cost of the network and network congestion,
Ÿ reduces processing required at the cloud platform level as
the data has already been curated at the edge,
Ÿ reduces the cost associated with maintenance of edge
integrated gateways,
Ÿ has less attack surface at the edge as the gateways are
rationalised and
Ÿ simplifies real-time subsystem integration
All of this allows us to better leverage the power of our
cloud platform as we can now understand the up-, and down-
stream effects of an event-triggered occurrence and effect
dynamic and seamless recalibration and interoperability
throughout ALL edge connected devices.
We ensure that all the pieces of the puzzle are in the box, and
ready to be pieced together to create the big picture.
Conclusion
Edge normalisation of data at the edge gateway layer form
the foundation for rapid digitalisation and digital
transformation. The disruption that everyone talks about is
vested in the ability for an organisation to continue its
business but iteratively and rapidly start to address the core
issues within its business through digitalisation. This leads to
more visibility on a real-time basis allowing for dynamic
recalibration back into the business ecosystem to achieve
optimised levels of production and efficiency that bring
about change and new ways of doing the same thing, better.
Peer-to-peer intelligence and learning will further drive this
thinking – Raptor thinking - making us even more relevant
as the necessity to drive edge analytics and decision making
in critical business environments nullifies the cloud. Are you
with me?
If you control the edge you unlock the cloud, a bottom
up approach.

Disruptive Technology
and Changing Trends
Influencing Business
Let us see how trends in technology are changing
businesses.
IOT has begun to change the world around us. It allows the
businesses to access their information virtually, creating a
flexible and global way of accessing data, any place, and
any time. It reduces the cost by maintaining IT system,
rather than purchasing expensive systems and equipment. It
also allows employees to be more flexible in work
practices. Let us see some fields where IOT must be
adapted.
Hospitals and other healthcare facilities are largely paper-
based industry. The pen and paper approach is still followed
largely around the world. Patient’s record sharing is still
done in the traditional way which is time-consuming.
Whereas, real-time monitoring via connected devices can
save lives in an event of a medical emergency. IOT devices
collect and transfer health data and are stores in the cloud.
These data can be shared with a physician or a health firm,
in order to allow them to look at it, regardless of their place,
time or device. Therefore, in an event of an emergency,
patients can contact a doctor who is many kilometers away
with a simple smartphone.
Fleet operators spend a large amount of time, money and
resource in maintaining the safety standards and resource in
maintaining the safety standards and operate at the desired
performance levels. Through various sensors, fleet
companies have access to a vast amount of data. This
information can help the company to make real-time quick
decisions for instant improvements. In fact, these insights
can help in effectively managing the overall supply chain.
Undoubtedly, IOT has set to become the backbone of the
fleet management industry.
Few decades ago, reaching the moon was beyond
imagination. But not today, because technology has
completely changed the world and made it possible.
th
Technological inventions were revolutionized in the 18 and
th
19 century with the steam engine, the telegraph, fiber
optics, typewriter, sewing machine, etc. Later, it changed
the way we communicate on a real-time basis with
telephone, radio, and internet.
Innovations in nanotechnology, biotechnology, and
information technology are already helping to solve
challenges that occur in these sectors. Through the
breakthrough innovations in health services, technology has
been able to improve the lives of poor people in developing
countries.
Manufacturing field is increasingly being automated and
technology driven. Advanced technology and systems such
as automation, nanotechnology, cloud computing, the
Internet of Things, and others are changing the face of
manufacturing to improve business technologies. So, the
adaption of technologies in work will revolutionize the way
it was in the past in the field of manufacturing as well.
Internet of Things
Healthcare
Fleet Management

75%
75%
75%
In today’s major cities at rush hour, getting
to and from work is a nightmare. Imagine a
world where not only the cars are smart, but
also the street and traffic lights. Public
transportation systems like trains and buses
are connected to individual’s smartphones.
This will help to know the exact time to
leave the houses accordingly. In smart cities,
passengers are already enjoying Wi-Fi and
USB charging stations on public
transportation. Overall, IOT already started
affecting the aspects of our life.
Public Transport Management
Renewable Energy
Will you like earning money on reducing the
use of electricity? Thanks to IOT energy-
saving tools, you can significantly decrease
the numbers in your bills. IOT energy
solutions are sensor-based technology. It
analyses weather and environment
condition, helps automate the management
of wind farms, optimizes maintenance and
thus reduces the cost dramatically. People
(both households and companies) get a
better understanding of their usage habits
and adjusts them accordingly. These system
collects data on electricity consumption in
real-time and helps generate important
insights for environmentalists, researchers,
and conservation strategists. Thus, installing
IOT smart energy device can join the
environmental initiative, cut down on
energy consumption and lessen the
greenhouse effect.
Agriculture
The global population is set to touch 9.6
billion by 2050. So, to feed this much
population, the farming industry must
embrace IOT. Smart farming based on IOT
technology will reduce waste and enhance
productivity. Ranging from the quantity of
fertilizer utilized to the number of journeys
the farm vehicles have made. In IOT based
smart farming a system is built for
monitoring the crop field with the help of
sensors and automating the irrigation
systems. It is highly efficient when
compared with the conventional approach.
Thus, with the population growing rapidly,
the demand can be successfully met, if the
farmers implement agricultural IOT
solutions in a prosperous manner.
Blockchain technology is changing the way we do our day to day
businesses. Companies are starting to work with Blockchain technology
because it gives you privacy along with it is transparent. Let’s see how
blockchain can help to deal with business.
Blockchain Technology
Smart Contracts
Contract is where consent of the parties is involved to agree and interact
with each other. Blockchain technology helps to guarantee the validity of a
transaction through a secure validation mechanism. Industries and
institutions are heavily reliant on contracts, such as insurance, financial
institutions, real estate, construction, entertainment and, law. A smart
contract helps formalize the relationships between people, institutions and
the assets they own. They eliminate the need for trusted third parties and are
self-verifying, self-executing and Tamper resistant.
Blockchain will be an important part of our financial and technological
digital future. It is one of the incredibly creative inventions that technology
has ever seen. So how we use it is up to us, it could indeed transform the
global scenario.
Technology and changing trends in businesses is not something which is
going to happen in the future, it is happening right now. It has already
started affecting a lot of businesses. So businesses have tremendous
opportunity to benefit from such technological advancement. There is no
doubt that technological innovations are largely followed all over the world
and it will revolutionize the businesses.
Tech Infrastructure

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