These technologies are gradually reshaping the financial services industry: - Artificial intelligence, deep learning, analytics, blockchain, and robotic process automation are emerging technologies applied in finance. - Analytics has been a top technology trend for over a decade and is going through four stages from basic business intelligence to real-time streaming analytics. - Blockchain uses distributed ledger technologies and consensus algorithms to securely record transactions in a decentralized manner, having applications for cryptocurrency, smart contracts, and identity management.

1. AND FINANCE
EMERGING
TECHNOLOGIES

2. T E C H N O L O G Y
When something is important
enough, you do it even if the
odds are not in your favour –
Elon Musk
B Y
V I K R A M S I N G H
S A N K H A L A
F I N A N C E
For the past few years, finance
has been shaken by
technological innovation like
never before – The Economist
E M E R G I N G T E C H N O L O G I E S A N D F I N A N C E
.
A PRESENTATION ON

3. A R T I F I C I A L
I N T E L L I G E N C E
Simulation of human
intelligence processes by
machines
D E E P L E A R N I N G
Concerned with algorithms
inspired by the structure and
function of the brain called
artificial neural networks
A N A L Y T I C S
The discovery, interpretation,
and communication of
meaningful patterns in data
R P A
Robotic process automation (RPA) is
the practice of automating routine
business practices with "software
robots" that perform tasks
automatically
B L O C K C H A I N
A digitized, decentralized,
public ledger
EMERGING TECHNOLOGIES
C O N V E R G E N C E
These are forces that are gradually reshaping the financial services industry today .

4. Technology priorities for 2017 and beyond
Rank Technology Trend
1 BI/Analytics
2 Cloud
3 Digitalization / Digital Marketing
4 Infrastructure & Data Center
5 Mobile
6 Cyber and information security
7 Industry-Specific Applications
8 ERP
9 Networking, Voice, and Data Comms
ANALYTICS
Ten out of
Twelve years
2006-2017
Source: Gartner
4

5. A N A L Y T I C S
Four Stages
E X P E R I E N C E
Intuition, domain knowledge
B U S I N E S S I N T E L L I G E N C E
Visualizations, KPI’s, OLAP,
Dashboards
P R E D I C T I V E A N A L Y T I C S
Modeling, Experimental
verification
R E A L T I M E
S T R E A M I N G A N A L Y T I C S
Deep learning, A.I.

6. B U S I N E S S T R E N D S
T E C H N O L O G Y T R E N D S
T R E N D S
Massively scalable data and processing clouds for data
aggregation, storage, and analysis
Next generation tools, portals, and visualization for data analysis
and presentation
Companies look to leverage investments in ERP and legacy systems
Existing data warehouse and reporting systems have limitations
THROUGH THE TIME
T H E B U S I N E S S A N A L Y T I C S M A R K E T
T h e B A m a r ke t i s d y n a m i c , ra p i d l y e x p a n d i n g a n d p o i s e d fo r h i g h g ro w t h a n d a d o p t i o n b e y o n d e a r l y a d o p t e rs

7. S U P E R V I S E D L E A R N I N G
The data set is split into two parts
– the training set and the testing
set.
L E A R N I N G A L G O R I T H M S
Learning algorithms start with an
initial guess and continuously train
themselves to improve the guess
till they can find the error is
minimal
U N S U P E R V I S E D L E A R N I N G
Draw inferences from datasets
consisting of input data without
labelled responses
MACHINE LEARNING
T H E B R A I N
N E I T H E R M A N N O R M A C H I N E C A N R E P L A C E I T S C R E ATO R . - TA PA N G H O S H

8. T R A I N I N G
Optimizes the model parameters
by using the items in the Training
dataset
+ T E S T
Ensures that the model obtained
during the Training phase is
effective on another dataset
(called Test dataset)
= L E A R N I N G
Iterative process
SUPERVISED LEARNING
T H E B R A I N
T H E F U T U R E I S O U RS TO S H A P E . - M A X T E G M A R K

9. 2 S E L E C T F E A T U R E S E T
1 C H O O S E A N M L M E T H O D
A N D A L G O R I T H M
0 S T A R T
PROCESS
M A C H I N E L E A R N I N G
T h e B A m a r ke t i s d y n a m i c , ra p i d l y e x p a n d i n g a n d p o i s e d fo r h i g h g ro w t h a n d a d o p t i o n b e y o n d e a r l y a d o p t e rs

10. 3 S P L I T D A T A I N T O
T R A I N I N G S E T A N D
T E S T S E T
4 A D J U S T T H E M O D E L T I L L Y O U
G E T A C C E P T A B L E
E F F E C T I V E N E S S I N D I C A T O R S
5 A p p l y t h e m o d e l t o t h e
t e s t s e t , c h e c k t h e
e f f e c t i v e n e s s i n d i c a t o r s

11. ARTIFICIAL NEURAL
NETWORKS
D E E P L E A R N I N G
Neural networks follow a dynamic
computational structure, and do not abide
by a simple process to derive a desired
output. The basis for these networks
originated from the biological neuron and
neural structures – every neuron takes in
multiple unique inputs and produces one
output.

12. The very heart of the first Deep Learning
algorithms is a neural network with multiple
hidden layers and a final output layer (usually
logistic regression). In lieu of standard training,
there are 2 steps:
1. Individually trains every single hidden layer,
starting from the one after the input layer and so
that the output of a trained layer becomes the
input of the following layer.
2. Supervised training of the network as a whole,
or just of the output layer.
DEEP LEARNING
H O W I T W O R K S

13. I N F O R M AT I O N W A N T S T O
B E F R E E
Data mining is the process of discovering patterns in
large data sets involving methods at the intersection of
machine learning, statistics, and database systems. It is
an essential process where intelligent methods are
applied to extract data patterns.
DATA MINING
I F W E H A V E D A T A L E T S G O B Y D A T A . I F A L L W E H A V E I S O P I N I O N S , L E T S G O W I T H M I N E

14. Measures of Central Tendency and Dispersion
▪ Mean, Median, Mode, Quartiles, Percentiles, Standard
deviation and Variance
▪ Range, Mean /median absolute deviation
▪ Coefficient of variation, Frequency Distribution
Sampling Techniques
▪ Simple random sampling, Stratified sampling, Cluster
sampling, Systematic sampling
Hypothesis Testing
▪ z test, t test, F test, Chi-square test
Correlation
Regression
▪ Simple Linear regression, Multiple regression
▪ Non-linear regression, Logistic regression
Forecasting
▪ Time series: Moving average, Auto regressive method,
Exponential smoothing
Optimization
▪ Linear programming, Integer programming
Data Mining/Machine Learning/Text Mining
▪ Decision tree, Random Forest
▪ Support vector machine, Stochastic gradient descent, Neural
network, Cluster analysis
▪ Naïve Bayes, Apriori, k-Nearest Neighbors (kNN),
Expectation Maximization (EM), AdaBoost
Simulation
Commonly used Statistical/Machine Learning Functions
▪ Allows businesses a better understanding of data.
▪ Allows businesses to deal with the uncertainties of
the business.
▪ Allows managers to make sound judgments,
knowing their decisions are based on data and not
on assumptions.
▪ Statistical functions help businesses to plan better
and make predictions about the road ahead.
▪ Marketing is an important part of any business and
statistical functions help to market products and
services effectively
▪ Predictive Models based on appropriate Machine
Learning models provide deep insights and help
gain a competitive advantage.
▪ Market Sentiments is provided using the Brand
Sentiment Analysis Tool that mine the social network
and provide signals that otherwise gets lost in a
traditional communication environment.

15. B L O C K C H A I N
Algorithms that enable the creation of distributed
ledgers are powerful, disruptive innovations that could
transform the delivery of public and private services
and enhance productivity through a wide range of
applications.
C R Y P T O C U R R E N C Y
Cryptocurrencies are applications of Blockchain. Digital
currencies such as Bitcoin rely on the underlying
technology called a block chain. This records every
transaction made in identical copies of a digital ledger
that is shared among users
DISTRIBUTED LEDGER
TECHNOLOGIES
T H E N E X T R E V O L U T I O N

16. R E C O N C I L I AT I O N
Sharing and Verification
through Cryptography
R E P L I C AT I O N
if one ledger is
compromised, the
remainder are not.
G R A N U L A R A C C E S S
C O N T R O L
‘Keys’ and Signatures
to control who can do
what inside the shared
ledger.
G R A N U L A R
T R A N S PA R E N C Y
A N D P R I VA C Y
Many parties can verify
every record.
S H A R E D L E D G E R S H A R E D L E D G E R S H A R E D L E D G E R S H A R E D L E D G E R

17. Blockchain is a system
designed for optimal fault
tolerance in Distributed
Systems. Blockchain
technology offers a
solution to many digital
identity issues, where
identity can be uniquely
authenticated in an
irrefutable, immutable,
and secure manner
Financial institutions,
regulators, central banks
and governments are now
exploring the possibilities
of using this ‘shared
ledger’ approach to
streamline a plethora of
different services, both in
government and the
wider economy.
Blockchain technologies
makes tracking and
managing digital identities
both secure and efficient,
resulting in seamless sign-
ons and reduced fraud.
Blockchain technologies
makes tracking and
managing digital identities
both secure and efficient,
resulting in seamless sign-
ons and reduced fraud.
BLOCKCHAIN
W H AT A R E T H E A P P L I C AT I O N S
O F

18. W H E N
We achieve state replication if all the nodes of a
distributed system agree on a sequence of
Transactions. That is – They record the same set of
transactions in the same order.
C O N S E N S U S
Participants must reach a consensus on a unique block
of transactions to be appended to the chain. This
achieved by means of a consensus algorithm.
STATE REPLICATION
W H A T I S S T A T E R E P L I C A T I O N

19. BLOCKCHAIN
T H R E E G E N E R A T I O N S O F B L O C K C H A I N

20. D ATA S T R U C T U R E C O N S E N S U S M E C H A N I S M E X A M P L E
THREE GENERATIONS
G E N E R A T I O N S O F B L O C K C H A I N
1. BLOCKS
2. DIRECTED ACYCLIC GRAPHS
3. DIRECTED ACYCLIC GRAPHS
1. PROOF OF WORK
2. PROOF OF STAKE
3. PROOF OF STAKE.
1. BITCOIN
2. ETHEREUM
3. CASPER, TANGLE.

21. THE FINANCIAL DISRUPTION
B L O C K C H A I N

22. C R Y P T O C U R R E N C Y S H A R E T R A D I N G S M A R T C O N T R A C T S I D E N T I T Y
M A N A G E M E N T

23. C R Y P T O
A DIGITAL ASSET
CRYPTOCURRENCY
B L O C K C H A I N

24. P O S I T I O N
The Universe is all of time
and space and its
contents. It includes
planets.
D I R E C T I O N
The Universe is all of time
and space and its
contents. It includes
planets.
M A P
The Universe is all of time
and space and its
contents. It includes
planets.
L O C AT I O N
The Universe is all of time
and space and its
contents. It includes
planets.
L E A R N M O R E L E A R N M O R E L E A R N M O R E L E A R N M O R E

25. B E G I N N I N G
Announcing the first release of Bitcoin, a new
electronic cash system that uses a peer-to-peer
network to prevent double-spending. It’s
completely decentralized with no server or
central authority. – Satoshi Nakamoto, 09
January 2009, announcing Bitcoin on
SourceForge
T H E C O N C E P T
If you take away all the noise around
cryptocurrencies and reduce it to a simple
definition, you find it to be just limited entries
in a database no one can change without
fulfilling specific conditions. A cryptocurrency
like Bitcoin consists of a network of peers.
Every peer has a record of the complete history
of all transactions and thus of the balance of
every account.
SATOSHI NAKAMOTO
B I T C O I N

27. S M A R T C O N T R A C T S
THE NEXT BIG
THING IS
HERE!

28. T H E N E X T G E N E R AT I O N
I N T E R N E T
When you send and share information on the Internet,
you’re not sending an original but a copy. There may be
distributed applications/social networks where people
don’t push their data through a centralized
intermediary but control, move and allocate it to
certain situations
G O V E R N M E N T
It can help build accountable governments through
transparency, smart contracts and revitalized models of
democracy. Within the decade, every single financial
asset, which is really just a contract ‒ a stock is a
contract, a bond is a contract of paper ‒ those
contracts will all move to a blockchain-based format.
ABOUT US AND OUR
FUTURE
B L O C K C H A I N

29. R P A
ROBOTIC PROCESS
AUTOMATION
Robotic process automation (RPA) is the use
of software with artificial intelligence (AI)
and machine learning capabilities to handle
high-volume, repeatable tasks that
previously required humans to perform.

30. W H AT R PA D O E S
RPA speeds up and executes with perfect
accuracy processes in the fields of
banking & finance, insurance, healthcare,
manufacturing, telecom and many more.
Typically, one software robot can replace
and outperform 3 workers.
B E N E F I T S
RPA introduces a highly flexible and scalable
virtual workforce with reduced induction
time. Every robot’s activity can be logged and
interpreted through customized reporting
tools. Improved governance and compliance
can be easily achieved as requirements are
set in the automation rules.
THE FUTURE OF JOBS
A U T O M AT I O N

31. Algorithmic trading (automated trading, black-box trading, or
simply algo-trading) is the process of using computers programmed
to follow a defined set of instructions for placing a trade in order to
generate profits at a speed and frequency that is impossible for a
human trader.
ALGORITHMIC
TRADING.
T H E S T O C K M A R K E T
H OW T R A D I N G I N T H E E XC H A N G E S I S C H A N G I N G
A B E T T E R T O M O R R O W

32. C O M P L E X A L G O R I T H M S
Algorithmic utilizes advanced and complex
mathematical models and formulas to make high-speed
decisions and transactions in the financial markets.
Algorithmic trading involves the use of fast computer
programs and complex algorithms to create and
determine trading strategies for optimal returns.
P O P U L A R T R A D I N G
S T R AT E G I E S
The use of algorithmic trading is most commonly used
by large institutional investors due to the large amount
of shares they purchase every day.
ABOUT US AND OUR
FUTURE
A L G O R I T H M I C T R A D I N G

33. Arbitrage is the difference of market prices
between two different entities. When this
occurs, the stocks traded on the markets
either lag behind or get ahead of the futures,
providing an opportunity for arbitrage. High-
speed algorithmic trading can track these
movements and profit from the price
differences.
ARBITRAGE
A L G O R I T H M I C T R A D I N G

34. The index funds of mutual and Pension funds are regularly
adjusted to match the new prices of the fund's underlying
assets. Before this happens, preprogramed trading
instructions are triggered by algorithmic trading-supported
strategies, which can transfer profits from investors to
algorithmic traders.
TRADING BEFORE INDEX
FUND REBALANCING
A L G O R I T H M I C T R A D I N G

35. Mean reversion is mathematical method that computes the
average of a security's temporary high and low prices. Algorithmic
trading computes this average and the potential profit from the
movement of the security's price as it either goes away from or
goes toward the mean price.
MEAN REVERSION
A L G O R I T H M I C T R A D I N G
S T O C H A S T I C P R O C E S S E S

36. SCALPING
Scalpers profit from trading the bid-ask spread as
fast as possible numerous times a day. Price
movements must be less than the security's spread.
These movements happen within minutes or less,
thus the need for quick decisions, which can be
optimized by algorithmic trading formulas.
A L G O R I T H M I C T R A D I N G

37. H I G H
FREQUENCY
TRADING
High frequency trading is an automated
trading platform used by large
investment banks, hedge funds and
institutional investors which utilizes
powerful computers to transact a large
number of orders at extremely high
speeds.

38. HFT IS A KIND OF ALGORITHMIC TRADING
High Frequency Trading (HFT) involves algorithmic
trading on much shorter timescales, where signals
are processed and orders are executed within
microseconds.
D I F F E R E N C E B E T W E E N
HFT AND ALGO
TRADING

39. THANK YOU
V I K R A M S I N G H S A N K H A L A
+91 9819543261
VIKRAMSANKHALA@GMAIL.COM