Welcome to this 365 Data Science special where
we’ll explore the top 5 ways data science
is reinventing Finance!
Ever since its genesis, Data Science has helped
transform many industries.
For decades financial analysts have relied
on data to extract valuable insights, but
the rise of Data Science and Machine Learning
has brought upon a new era in the field.
Now, more than ever, automated algorithms
and complex analytical tools are being used
hand-in-hand to get ahead of the curve.
But before we proceed, we need to very briefly
explain some of the terminology we’ll be
using.
Machine Learning, or M-L, and Deep Learning,
or D-L, are different aspects of data science
that use modelling algorithms to find links
between data, extract insights and draw predictions
for the future.
They are an important part of Data Science
and allow us to construct algorithms that
evolve on their own, given enough time and
information.
Okay, now that this is out of the way, let’s
explore the top 5 ways in which financial
institutions use these methods to their advantage,
shall we?
Number 5: Fraud Prevention
Fraud prevention is a part of financial security
that deals with fraudulent activities, such
as identity theft and credit card schemes.
Abnormally high transactions from conservative
spenders, or out of region purchases often
signal credit card fraud.
Whenever such are detected, the cards are
usually automatically blocked, and a notification
is sent out to the owner.
That way, banks can protect their clients,
as well as themselves and even insurance companies,
from huge financial losses in a short period
of time.
The opportunity costs far outweigh the small
inconvenience of having to make a phone call
or issue another card.
The role data science plays here comes in
the form of random forests and other methods
that determine whether there are sufficient
factors to indicate suspicion.
Surely, security advancements with facial
or fingerprint recognition have added layers
of authentication which have lowered the chances
of identity theft, as well.
3D passwords, text messages confirmation and
PINT codes have also massively backed the
safety of online transactions.
However, we’re more interested in the initial
security measurements we mentioned.
Those pattern recognitions also require the
use of ML algorithms, so data science has
substantially improved fraud prevention in
more ways than one.
Number 4: Anomaly Detection
Unlike Fraud Prevention, the goal here is
to detect the problem, rather than prevent
it.
The reason is that we can’t classify an
event “anomalous” as it happens but can
only do so in the aftermath.
The main application of this anomaly detection
in finance comes in the form of catching illegal
insider trading.
In today’s financial world it isn’t always
easy to spot trading patterns with a naked
eye.
Of course, any trader can strike gold and
accurately predict the boom or collapse of
a given equity stock occasionally, but there
exist ways of determining what is out of the
norm.
Enter, deep learning.
Through a mix of Recurrent Neural Networks
and Long Short-Term Memory models, data scientists
can create anomaly-detection algorithms.
Such an algorithm can spot whenever somebody’s
trading history is well-above the norm, both
for them as an entity, and the market as a
whole.
The way it works is, they analyse the trading
patterns before and after the internal announcement
of non-public information like the release
of a new product or an upcoming merger.
Then, based on the volume and frequency of
the transactions, the model can decide if
somebody is using non-public information to
exploit the market and take advantage of innocent
investors.
Thus, data science has had a huge impact on
catching and punishing illegal trading in
the industry.
Moving on to Number 3: Customer Analytics.
Based on past behavioral trends, financial
institutions can make predictions on how each
consumer is likely to act.
With the help of socio-economic characteristics,
they’re able to split consumers into clusters
and make estimations on how much money they
expect to gain from each client in the future.
Knowing this, they can decide which ones to
cater to and how to appeal to them more.
Similarly, they can cut their losses short
on consumers who will make them little or
no money.
In short, it allows them to distribute their
savings in the most efficient way.
For example, insurance companies often use
this technique to assign lifetime evaluations
to each consumer.
And while this is not the most precise technique,
it does prove to be very solid in practice.
So how does Data Science fit into this?
Using unsupervised M-L techniques, the company
splits consumers into distinct groups based
on certain characteristics, such as age, income,
address, etc.
Then, by constructing predictive models, they
determine which of these features are most
relevant for each group.
Depending on this information, they assign
expected worth of each client.
Having quantified the value or the range of
values of each consumer, they can decide who
is worth keeping and who isn’t, which helps
them allocate their savings best.
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and many other data science topics, we’ve
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We have trained more than 350,000 people around
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If you are interested to learn more about
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that will also give you 20% off all plans.
Now, back to our countdown with…
Number 2: Risk Management
Another important factor in finance is stability,
a.k.a. risk management.
Investors and higher-ups don’t like uncertainty
when it comes to major deals, so there exists
a need to measure, analyse and predict risk.
Of course, the short term for that is “risk
analytics”, and data science has provided
great help in developing that part of the
financial industry.
So, let’s explore it in more detail.
Risk can be many things – it can be uncertainty
about the market, it can be an influx of competition,
or it can be some customer trustworthy-ness.
Depending on what type it is, we use different
ways to model and manage it.
Overall, risk management is a complex field
requiring knowledge across finance, math,
statistics and more.
You may have heard of positions called ‘risk
management analysts’ or ‘quantitative
analysts’.
However, a current-day data scientist has
the necessary skills for both previous positions.
Therefore, financial institutions utilize
data science to minimize the probability of
human error in the process.
But how is that achieved in practice?
The main approach dictates that the first
step is identifying and ranking all the uncertain
interactions.
Then, we monitor them going forward, and prioritize
and address the ones that make our investments
most vulnerable at a given time.
Banks tend to use customer transactions data
and other available information to create
adaptive real-time scoring models.
Those frequently update how “risky” each
consumer is and whether they are suitable
for a credit loan or mortgage.
In fact, since the Great Recession of 2008,
banks have shied away from giving out the
infamous NINJA loans.
For anybody unfamiliar with the term, NINJA
stands for: No Income, No Job or Assets.
Instead, they’ve opted to use data science
and create more reliable risk score models
to determine the creditworthiness of potential
clients.
This just goes to show you how through machine
learning, the banking industry has evolved
and effectively put a soft brake to prevent
a potential repeat of the crisis.
That being said, neither of the topics we
discussed so far are the main contribution
data science has had on the financial industry.
That accolade belongs to number one on our
list: Algorithmic Trading.
To explain it briefly, a machine makes trades
on the market based on an algorithm.
These trades can happen multiple times every
second with various degrees of volume and
do not need to be approved by a stand-by analyst.
These trades can be in whatever market we
want, or even multiple markets simultaneously.
Thus, algorithmic trading has mitigated many
of the opportunity costs that come from missing
a trading opportunity by hesitation, as well
as other human errors.
In their foundation, these algorithms consist
of a set of rules, which steer the decisions
to trade or not.
On top of that, we usually see a reinforced
learning model, where mistakes are heavily
penalized.
Based on how well the model performs, it adjusts
the hyper parameters to make better estimations
going forward.
In layman’s terms, the model adjusts the
values for each rule, based on performance.
Most notably, we see algorithms that find
and exploit arbitrage opportunities.
In other words, they find inconsistencies
and make trades which lead to certain profits.
The huge upside of algorithmic trading is
that it can be high frequency.
In other words, the moment the algorithm finds
an opportunity to make a profit, it will.
However, these algorithms don’t always have
to trade all the time.
The way they work is the following: they develop
conditions that make up a “signal”.
Once they are met, this signal is sent out
to the algorithm, and it makes a trade.
The requirements for these conditions are
so well-established that it takes fractions
of a second between the signal and the trade
to occur, so the process is essentially instantaneous.
However, sometimes these conditions aren’t
met for months on end.
Sometimes, all the movements of the equity
stock or security are simply noise, so the
algorithm doesn’t twitch.
This makes algorithmic trading so successful
because it’s not trigger-happy and can wait
out to make sure the moment is correct.
A downside these algorithms used to have,
was that if they were imprecise, it could
lead to huge losses due to the lack of human
supervision.
For instance, in February 2018, the price
of Dow Jones plummeted after several trading
algorithms interpreted a false signal.
A devastatingly quick snowball effect emerged
as other algorithms followed suit and the
stock price fell by $80 in mere minutes.
After that, many algo-trading models were
made much more complex in order to prevent
the market from going into freefall.
Sometimes though, something unprecedented
happens, and human intervention is needed
to suspend the models.
For example, in September 2019 a drone strike
in Saudi Arabia set ablaze the world’s largest
oil refinery.
This caused huge uncertainty in the market
and a high volatility of the prices of crude
oil all around the world.
Since these events cannot be predicted, regardless
of how well-trained the model is, many investors
tend to pause their trading algorithms.
Even though huge gains can be made, so too
can huge losses.
As we already mentioned, CEOs are risk-averse
and prefer stability.
Since the vast and fast development of such
trading algorithms, the playing field is very
much evened out when competitors have the
same access to information.
This makes arbitrage opportunities very scarce,
since they are often exploited immediately.
In turn, this has led to great efficiency
in the market, so hedge funds and investment
banks need to look for an edge over the competition
elsewhere.
Here lies the latest change data science has
brought onto the finance industry.
Nowadays, data has become the hottest commodity
that results in getting an edge over the competition.
Financial institutions are spending huge amounts
of money to get exclusive rights to data.
By having more information, they can construct
better models and get ahead.
Thus, the most valuable commodities are no
longer the analysts themselves or the quants
that help design these algorithms, but the
data itself.
So, this is how the introduction of data science
has truly revolutionized the financial industry.
From leaps in security and loss prevention
to automated trading models that decrease
human error, we’ve certainly entered a new
era for the industry.
More than ever before, data is the resource
everybody is fighting over.
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