5 Ways Data Science Changed Finance

Data has been used by financial analysts for decades to extract useful insights, but the rise of Data Science and Machine Learning has ushered in a new age in the sector.

Automated algorithms and complicated analytical tools are being employed together more than ever before to stay ahead of the curve.

But, before we get started, let’s go over some of the terms we’ll be utilising.

Machine Learning (M-L) and Deep Learning (D-L) are two parts of data science that employ modelling algorithms to uncover connections between data, extract insights, and make future predictions.

Let’s take a look at the top 5 ways financial institutions employ these techniques to their advantage.

1. Preventing Fraud Financial security includes fraud prevention.

Credit card fraud is generally detected by unusually high transactions from cautious spenders or purchases made outside of the country.

When such activity is found, the cards are normally automatically blocked, and the owner is notified.

Banks can safeguard their customers, as well as themselves and insurance providers, from large financial losses in a short period of time in this way.

The opportunity costs greatly surpass the minor annoyance of having to call or re-issue a card.

Data science use random forests and other algorithms to determine whether there are enough elements to trigger suspicion.

Face and fingerprint recognition, for example, have clearly offered degrees of authentication, reducing the risk of identity theft.

The adoption of 3D passwords, text message confirmation, and PINT codes has also improved the security of online transactions.

However, the initial security measures we stated are what we’re most interested in.

These pattern recognitions also necessitate the use of machine learning algorithms, therefore data science has helped fraud prevention in multiple ways.

2. Detection of Anomalies In contrast to Fraud Prevention, the purpose here is to detect rather than prevent the problem.

The reason for this is that we can’t describe an occurrence as “abnormal” while it’s happening; we can only do so afterward.

The most common application of anomaly detection in finance is uncovering unlawful insider trading.

Trading patterns are not always easy to discern with the naked eye in today’s financial environment.

Of course, every trader can hit gold and correctly predict the rise or fall of a certain equity stock once in a while, but there are ways to spot what is out of the ordinary.

Data scientists can construct anomaly-detection algorithms using a combination of Recurrent Neural Networks and Long Short-Term Memory models.

Such an algorithm may detect whether a person’s trading history deviates significantly from the norm, both for them individually and for the market as a whole.

They analyse trade trends before and after internal announcements of non-public information, such as the debut of a new product or a planned merger.

The model can then determine whether someone is exploiting the market and taking advantage of innocent investors based on the volume and frequency of transactions.

As a result, data science has had a significant impact on the industry’s ability to detect and penalise unlawful trading.

3. Customer Analytics.

Financial firms can forecast how each customer will behave based on previous behaviour patterns.

They can divide customers into clusters based on socioeconomic criteria and estimate how much money they expect to make from each client in the future.

They can then select which ones to cater to and how to better appeal to them.

They can also cut their losses on customers who will only make them a small amount of money.

In other words, it enables them to divide their savings in the most effective manner possible.

Insurance firms, for example, frequently utilise this method to assign lifetime evaluations to each customer.

While this isn’t the most accurate method, it has proven to be extremely reliable in practise.

So, where does Data Science fit in?

The company divides customers into various groups depending on certain factors such as age, income, address, and so on, using unsupervised M-L approaches.

Then, using predictive models, they figure out which of these characteristics are most important for each group.

They assign each client’s predicted worth based on this information.

They can select who is worth keeping and who isn’t after quantifying the value or range of values of each consumer, which helps them effectively utilise their savings.

4. Risk Assessment and Management Stability, often known as risk management, is another crucial component in finance.

When it comes to significant transactions, investors and executives don’t like uncertainty, therefore risk must be measured, analysed, and predicted.

Of course, the short phrase for this is “risk analytics,” and data science has aided in the development of this sector of the financial business greatly.

So, let’s take a closer look at it.

Risk can take various forms: it can be market uncertainty, an invasion of competition, or a lack of customer trustworthiness.

We model and handle it in a variety of ways depending on the type.

Overall, risk management is a complicated field that necessitates knowledge of finance, arithmetic, statistics, and other subjects.

You may be familiar with the terms ‘risk management analysts’ and ‘quantitative analysts.’

A modern data scientist, on the other hand, possesses the skills required for both professions.

As a result, financial institutions use data science to reduce the risk of human mistake during the process.

But how does one go about doing this in practise?

According to the main strategy, the first step is to identify and rank all of the unknown interactions.

Then we keep track of them in the future, prioritising and addressing the ones that put our investments at jeopardy at any given time.

Banks typically construct adaptive real-time scoring models using customer transaction data and other accessible information.

These are updated on a regular basis to determine how “risky” each customer is and if they are eligible for a credit loan or a mortgage.

Indeed, since the Great Recession of 2008, banks have been hesitant to make the infamous NINJA loans.

NINJA stands for No Income, No Job, and No Assets, for those who are unfamiliar with the acronym.

Instead, they’ve elected to employ data science to develop more reliable risk score algorithms to assess potential clients’ creditworthiness.

This merely goes to demonstrate how the banking industry has matured and successfully put a soft brake on a potential repetition of the crisis using machine learning.

However, neither of the areas we’ve covered thus far represent the most significant contributions data science has made to the financial industry.

5. Algorithmic Trading.

To put it another way, a machine uses an algorithm to make market trades.

These trades can occur numerous times per second with varying volumes and do not require the approval of a stand-by analyst.

These trades can be made in whatever market we desire, or even in numerous markets at the same time.

As a result, algorithmic trading has reduced many of the opportunity costs associated with missing a trading opportunity due to reluctance or other human errors.

These algorithms are built on a set of principles that guide traders’ judgments on whether or not to trade.

In addition, we frequently observe a reinforced learning model, in which errors are harshly penalised.

It modifies the hyper parameters based on how well the model works in order to generate better predictions in the future.

The model modifies the settings for each rule based on performance, in layman’s words.

We observe algorithms that discover and exploit arbitrage possibilities most prominently.

To put it another way, they look for irregularities and make transactions that result in a profit.

The fact that algorithmic trading can be done at a high frequency is a big plus.

In other words, the algorithm will profit as soon as it detects a profit chance.

These algorithms, on the other hand, do not have to trade all of the time.

They operate in the following manner: they create conditions that make up a “signal.”

This signal is delivered to the algorithm, and it executes a trade once they are met.

The requirements for these parameters are so well-defined that the time between the signal and the trade is fractions of a second, making the operation effectively instantaneous.

However, these requirements aren’t always met for months at a time.

The algorithm does not twitch when all the fluctuations of the equity stock or security are simply noise.

This is what makes algorithmic trading so successful: it isn’t trigger-happy and can wait for the right moment.

One disadvantage of these algorithms was that if they were inaccurate, it may result in massive losses owing to the lack of human supervision.

In February 2018, for example, the Dow Jones stock price collapsed when many trading algorithms misinterpreted a false signal.

As other algorithms followed following, a devastatingly swift snowball effect occurred, and the stock price dropped by $80 in minutes.

To avoid the market from going into freefall, several algo-trading models were developed far more complicated after that.

However, every now and again, something unexpected occurs, necessitating human intervention to bring the models to a halt.

In September 2019, for example, a drone strike in Saudi Arabia set fire to the world’s largest oil refinery.

This created a lot of uncertainty in the market, as well as a lot of volatility in crude oil prices all over the world.

Many investors suspend their trading algorithms since these events cannot be expected, regardless of how well-trained the model is.

Massive gains can be made, but so can massive loses. CEOs, as previously stated, are risk cautious and desire stability.

The playing field is significantly more levelled when competitors have equal access to information, thanks to the huge and rapid development of such trading algorithms.

As a result, arbitrage chances are uncommon, as they are frequently taken advantage of right away.

As a result, the market has become extremely efficient, forcing hedge funds and investment banks to hunt for a competitive advantage elsewhere.

The most recent shift data science has brought to the finance industry can be found here.

Nowadays, data is the hottest commodity for gaining an advantage over the competitors.

Large sums of money are being spent by financial firms to get exclusive data rights.

They can build better models and get ahead with more knowledge.

As a result, the most precious commodities are no longer the analysts or the quants who assist in the development of these algorithms, but rather the data itself.

As a result, data science’s entrance has genuinely changed the financial business.

We’ve entered a new age for the sector, with advancements in security and loss prevention, as well as automated trading models that reduce human error.

Data is the resource that everyone is vying for now more than ever.