It was a period that „started with steam-powered ships as the most sophisticated means of transport and ended with the space shuttle.” The incredible progress made between 1901 and 2000 was astronomical, especially on the economic front.

So what can we call the Era that started in 2001? After all, we’re still striving to modernize the world and improve our lives. But it’s happening on a different level now.

Big Data technology is undoubtedly revolutionizing our world in a new way. And it’s clearly visible in the financial industry.

What is “Big Data”?

To make the long story short, Big Data is a complex “numbers game”. In other words, it is a series of very large sets of information whose processing defies the traditional methods and tools used for extracting valuable conclusions. This new technology can be applied to many different industries.

The idea is that, soon, more people and companies will be privy to that “knowledge” as well. For now, however, crunching those vast amounts of numbers is extremely labor-intensive and time-consuming. But the insights you gain from cracking the code and data science solutions derived from them are worth every effort and resource deployed to it.

So what does Big Data have to do with science?

When we think about science, we tend to imagine labs full of chemical substances, where people in white robes conduct dangerous, life-changing experiments that will save humanity from a deadly disease in the future.

Although Big Data also means ”a methodical study of a part of the material world”, the traditional perception of science doesn’t exactly reflect the core of this type of predictive analytics.

It’s a new type of science, being developed and perfected as you’re reading this article.

Analytics in the financial markets – the story so far

For instance, it might be a mundane exercise, but it’s not rocket science to review a financial report and convert its content into an informative and concise presentation.

There are plenty of visual tools out there that can help “sell” that data in an attractive and palatable way, like the popular Tableau.

But what if that report has thousands of pages, is released every single day, and your CEO’s business and trading decisions partially depend on the accuracy of the conclusions you draw from it?

Traditional business analytics are a thing of the past

Statistics show that “every day, 306.4 billion emails are sent, and 500 million Tweets are made”. So chances are you’ll start losing track of the critical data from the financial report you’re in charge of very soon.

And we truly doubt you’ll have the enthusiasm, time, and patience to build that presentation from scratch every day. Even if it only meant replacing the numbers with the current ones.

The difference between BI & DS

Another aspect that renders the usual analytics tools outdated is the direction of the arrow on a time axis. Business intelligence often refers to past events and historical data. What we need today, however, is to focus on the way forward to scientifically predict future financial trends.

How Big Data science is changing the finance industry

The reality of the 21st century is that the legacy systems in the financial world are out. Big Data is in. Today, if a financial institution wants to stand out, it needs to dig much deeper, much quicker, and pay much more attention to some socio-economic aspects that have been underestimated until now.

Interestingly, not every business sector knows yet how to extract that valuable information and interpret it to its advantage.

The pioneering sector

The good news is, however, that the financial services industry is one of the first to have embraced the advantages of Big Data solutions. Innovative fintech companies, traditional banks, and the stock exchange market have been at the forefront of innovation in this field.

Financial firms have been championing practical applications and allowing their analysts to make smart decisions about their clients’ future for some time now.

A lot has already been written about the advantages of using data solutions in the financial sector. So we’d like to focus on three aspects where Big Data has the potential to change the financial markets’ game for good. They are hedge fund investments, banking personalization, and fraud prevention.

To invest or not to invest? – that is the question

Hedge funds are a rewarding yet challenging occupation for portfolio managers. They deal with a fairly limited group of high-profile clients who invest huge money expecting relatively fast returns. It’s a financial sector in which common regulatory requirements do not always apply. Thus, portfolio construction and risk management techniques differ from traditional forms of investing.

This level of mathematical accuracy is impossible for a data analyst to achieve manually. Big Data science is the way to go.

Unstructured data in hedge funds

Nowadays, hedge funds managers also work with non-traditional methods to make informed decisions. Apart from analyzing the financial aspects of all transactions, they often need to focus on unstructured data. Many times, their job requires reviewing other sources of information about their clients’ sentiments to understand the market they’re investing in.

Being present on social media is an important factor, for instance. We’ve only just witnessed the Twitter storm related to Elon Musk’s announced takeover of the app and then, subsequently, the offer withdrawal. Such events can have a huge impact on the stock market and call for a prompt reaction.

And it would be a good indication for hedge funds managers whether it is time to sell shares or wait.

We’re selling – are you buying?

You’ll probably agree that it’s a nice touch to receive a $50 “Happy B-day” shopping voucher from your bank on the actual day you were born. You might also appreciate being looked after by a financial advisor who greets you by your first name.

But do you remember that in the (not so distant) past banks used to bombard their clients with massive, generic offers? So if you and your neighbor used the same financial institution, you would receive the same message, i.e. about being able to take a loan for $100K. Regardless of whether your income levels were the same or completely different.

Back then, it was more about “selling” things to anybody interested in buying rather than thinking about whether the offer made sense to the client at all.

Your personal(ized) bank

In the era of Big Data analytics in the financial industry, it is a far more researched process.

When you receive a loan proposal today, that communication is specifically tailored to your current earnings, estimated financial purchasing capabilities, or carefully calculated credit score. And it is very different from the offer made to your neighbor.

Big Data science, such as artificial intelligence tools, machine learning algorithms, or predictive models, has made decision-making easier on this front. With big data and natural language processing, progressive financial institutions can gain the upper hand in tailoring offers to their customers’ actual needs.

Alibaba and fraud prevention

Apart from improved consumer analytics, a more accurate risk assessment, or smarter stock market investments, Big Data has also been instrumental in targeting security threats. Some examples include credit card fraud detection, filtering out email scams, or stopping entire criminal organizations.

One of the biggest global retailers, Alibaba, has turned to Big Data to build its fraud risk management system. Considering that “in the fiscal year ending March 31, 2022, the Chinese e-commerce corporation (…) recorded a revenue of around 592.71 billion yuan in Chinese online sales [the equivalent of approximately 93.5 billion U.S. dollars]”, the group has every reason to invest in fighting illegal activities.

Its framework aimed at preventing fraud is a multi-layered one. It includes five (!!!) steps in the process: account check, device check, activity check, risk strategy, and a manual review at the end. How is that for a thorough verification?

Don’t become another hacking statistic

Nevertheless, as in many other innovative life disciplines, Big Data can be a double-edged sword.

And they start using more sophisticated methods to scam people or steal vital information, including social engineering, DDoS attacks, or cyberstalking.

It shouldn’t come as a surprise that most fraud cases pertain to money. In the US alone, “consumers lost $5.8 billion to fraud” in 2021. That’s 70% more than the year before.

Unfortunately, cybercriminals can get the same access to Big Data as the “good guys” if the valuable databases are not properly safeguarded. And it’s not always “the human factor” that’s at fault.

So it’s essential to ensure that, apart from investing in this new technology, you’re also paying attention to making it bullet-proof secure.

The value of implementing a Big Data strategy for your business

You’re probably asking yourself these questions right now, “Is my business Big Data-ready?” and “Can I afford to pivot to using Big Data analytics?”

Every financial start-up and every fintech company is different. So, instead of giving you a definitive answer, let’s look at it from a different perspective.

Although nobody in the whole wide world talks about such unimaginable amounts every day, that number does exist in mathematics.

But your average calculator won’t be able to properly process this many zeros. And that lack of capacity speaks to the challenge of working with Big Data as well. Although this new technology presents undeniable benefits, it comes with its specific challenges, as it is still a developing discipline.

After all, not every financial organization always needs to process large sets of data. Many smaller ones don’t even have the right tools, resources, or infrastructure to do so. And sometimes, regular business analytics will do the job just fine.

But if you want to stay competitive in the fintech or insurtech industries in the future, you should start thinking of having a Big Data strategy in place already today.

Be a part of the banking revolution

We do understand that investing in Big Data solutions in-house might be a costly venture for some businesses. Or you might simply not be ready to implement it in your company yet.

That’s why outsourcing your Big Data analytics to specialists, like our data scientists, makes sense. That way, we can help you form part of the Big Data user community today, so your company can keep up with the challenges of the future.

Conclusion

If we were to give the 21st century a nickname, we would call it ”The Era of Big Data Science”. The banking and financial services sector is one of the best examples of its practical realization.

This revolution in finance, powered by numbers, analytics, and the latest technology, is already here. It’s up to you to decide if you’re ready to join it.