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.

If you want to learn more about customer analytics and many other data science topics, we've

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We've created 'The 365 Data Science Program' to help people enter the field of data science,

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We have trained more than 350,000 people around the world and we're committed to continuing

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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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