Data Science in Finance: 5 Ways It Changed the Industry

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What’s the Role of Data Science in Finance?

It’s true that financial analysts have relied on data to extract valuable insights for decades.

The article originally appeared at https://365datascience.com/data-science-finance/

Ever since its genesis, data science has helped transform many industries.

However, 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.

So, to get you up-to-speed with the latest developments, in this post, we’ll explore the importance of data science in Finance. More specifically, we’ll walk you through the top 5 ways data science is reinventing the industry:

  • Fraud Prevention
  • Anomaly Detection
  • Customer Analytics
  • Risk Management
  • and Algorithmic Trading

Let’s discover how financial institutions use these methods to their advantage, shall we?

Data Science in Finance: What Are the Top 5 Ways Data Science Is Reinventing Finance?

Fraud Prevention

Fraud prevention is a part of financial security that deals with fraudulent activities, such as identity theft and credit card schemes.

How Do Financial Institutions Prevent Fraud?

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. Then, 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.

What’s the Role of Data Science in Fraud Prevention?

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. And they 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 machine learning algorithms. That said, data science has substantially improved fraud prevention in more ways than one.

Anomaly Detection

When we talk about data science in Finance, we can’t possibly skip 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.

How Does Anomaly Detection Work in Finance?

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 algorithms can spot whenever somebody’s trading history is well-above the norm, both for them as an entity, and the market as a whole.

Here’s how it works.

Algorithms 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.

Customer Analytics

On another front, we can find a great example of data science in Finance in the Customer Analytics field.

How Do Financial Institutions Use 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 machine learning techniques, the company splits consumers into distinct groups based on certain characteristics, such as ageincomeaddress, 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.

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 in finance has provided great help in developing that part of the industry.

So, let’s explore it in more detail.

What is Risk?

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.

How Is Data Science Utilized in Risk Management?

Depending on what type it is, there are 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. What comes next is monitoring them going forward, prioritizing and addressing the ones that make the 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 (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 how through machine learning, the banking industry has evolved and effectively put a soft brake to prevent a potential repeat of the crisis.

Still, the main application of data science in Finance is in Algorithmic Trading.

Algorithmic Trading

We have Algorithmic Trading when a machine makes trades on the market based on an algorithm. They can happen multiple times every second with various degrees of volume. Plus, they don’t need to be approved by a stand-by analyst. Such 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. Or, 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, that is, they find inconsistencies and make trades which lead to certain profits.

The huge upside of algorithmic trading is that it can be high frequency.

So, 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.

How Does Algorithmic Trading Work?

The way it works is the following: the algorithm develops 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, we can say 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.

So, what makes algorithmic trading so successful is that it’s not trigger-happy and can wait out to make sure the moment is correct.

A downside these algorithms used to have in the past, 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 high volatility of the prices of crude oil all around the world. These events cannot be predicted, regardless of how well-trained the model is. That’s why 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. Especially 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. In fact, that’s 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. It’s the data itself.

So, this is how data science in Finance 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 in Finance. And, more than ever before, data is the resource everybody is fighting over.

If you want to learn more about the various ways data can be processed, read out our blog post on Techniques for Processing Traditional and Big Data.

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