The 20th century was hailed as ”The Age of Modernity”. Groundbreaking innovations were observed in those 100 years.
- What is “Big Data”?
- The difference between BI & DS
- How Big Data science is changing the finance industry
- We’re selling – are you buying?
- Your personal(ized) bank
- Don’t become another hacking statistic
- Be a part of the banking revolution
- Conclusion
The space shuttle marked the end of a time period that “began with steam-powered ships as the most advanced means of transportation.” Between 1901 and 2000, there was astronomical development made, particularly in terms of the economy.
What then should we name the era that began in 2001? After all, we’re still working to make the world more contemporary and our lives better. But now it’s taking place at a new level.
Undoubtedly, big data technology is bringing about a new kind of change in our world. And the financial sector can easily see it.
Just what is “Big Data”?
To cut a long tale short, big data is a sophisticated “numbers game”. In other words, it is a collection of very huge data sets whose processing defies conventional techniques. and instruments for drawing useful findings. Many different sectors can use this breakthrough technology.
The hope is that more people and businesses will soon have access to that “knowledge” as well. But for now, it takes a lot of time and effort to crunch such enormous volumes of data. However, the data science solutions you develop as a result of breaking the code and the insights you receive from doing so are worth all the time and effort put into it.
So what is Big Data’s relationship to science?
When we think about science, we frequently picture laboratories filled with chemical compounds, where scientists in white robes carry out risky, life-altering operations that will one day protect humanity from a fatal disease.
Although “a methodical study of a part of the material world” is another description of “Big Data,” the conventional idea of science doesn’t really capture the core of the field.of predictive analytics of this kind.
As you read this post, a brand-new branch of science is being created and refined.
The current state of analytics in the financial markets
Reviewing a financial report and turning its information into a clear and straightforward presentation, for instance, is not a very difficult activity.
Many visual tools, like as the well-known Tableau, are available that may assist “sell” such data in an appealing and digestible manner.
But what if that report includes hundreds of pages, is published every day, and your CEO bases some of his trading and business choices on the veracity of the inferences you make from it?
The era of conventional business analytics is over.
According to statistics, “306.4 billion emails are sent daily, and 500 million Tweets are posted.” Therefore, it’s likely that you’ll soon begin to lose track of the crucial information from the financial report you’re in charge of.
And we seriously doubt that you’ll be motivated, available, and patient enough to create that presentation from scratch each day. even if it only included replacing the outdated numbers with the more recent ones.
The distinction between BI and DS
The direction of the arrow on a time axis is another element that makes the conventional analytics tools obsolete. Business intelligence frequently makes reference to historical data and prior occurrences. However, what we need to do right now is concentrate on how to proceed in order to accurately forecast future financial patterns.
How the banking sector is transforming as a result of Big Data science
The legacy financial systems of the 20th century are obsolete in the modern world. Big Data is popular. A financial institution now needs to go much further, much faster, and pay considerably more attention to key socio-economic factors that have been undervalued up until now if it wants to stand out.
It’s interesting to note that not every industry in business has mastered the art of extracting and utilizing this crucial data.
the cutting-edge industry
The good news is that the financial services sector was among the first to take use of Big Data’s benefits. The stock exchange sector, established banks, and cutting-edge fintech companies have been at the forefront of innovation in this market.
Financial institutions have always supported practical applications and encouraged its analysts to make informed choices on the future of their clients.
The benefits of utilizing data solutions in the financial sector have already been extensively covered in writing. In order to better understand how big data might fundamentally change the financial markets, we’d want to focus on three specific areas. They are fraud prevention, banking personalisation, and hedge fund investments.
The question is whether to invest or not to invest.
Portfolio managers have a profitable but tough career in hedge funds. They work with a very small number of well-known clientele who make large investments in hopes of seeing quick returns. It’s a financial industry where standard regulatory rules don’t always hold true. Therefore, portfolio design and risk management strategies are different from conventional investing methods.
An analyzer of data cannot manually attain this degree of mathematical correctness. The direction to go is big data science.
Hedge fund unstructured data
Managers of hedge funds now use unconventional techniques to help them decide. They frequently need to concentrate on unstructured data in addition to examining the financial elements of every transactions. Their work frequently include looking at information from different sources regarding the opinions of their clients in order to comprehend the market they are investing in.
For instance, being active on social media is crucial. Elon Musk’s reported acquisition of the app and subsequent withdrawal of the deal has only recently caused a Twitter fury. These occurrences might have a significant influence on the stock market and need a speedy response.
Hedge fund managers might use it as a solid indicator of when to sell shares or hold onto them.
We’re selling; will you purchase?
You’ll undoubtedly concur that receiving a $50 “Happy B-day” shopping voucher from your bank on the day of your birth is a lovely touch. Additionally, you might value receiving care from a financial counselor who calls you by your first name.
But do you recall how banks used to barrage their consumers with many, generic offers in the (not so distant) past? Therefore, if you and your neighbor utilized the same financial institution, you would both get the identical message—about being able to take out a $100K loan—from that financial institution. No matter what your income is levels were either identical or dissimilar.
Back then, it was more about “selling” products to anyone who was interested in purchasing them than it was about determining if the customer would even find the offer to be reasonable.
Your customized bank
It is a much more thoroughly explored approach in the finance industry’s Big Data analytics age.
These days, when you receive a loan proposal, it is specially built to take into account your present income, projected financial purchasing power, or meticulously calculated credit score. Additionally, it differs significantly from the offer offered to your neighbor.
Making decisions on this front has become simpler thanks to Big Data Science technologies like artificial intelligence tools, machine learning algorithms, or predictive models. Progressive financial organizations can get the upper hand in customizing offerings to their clients’ genuine needs by utilizing big data and natural language processing.
Alibaba and preventing fraud
Big Data has been useful in identifying security concerns in addition to better consumer analytics, more accurate risk assessment, and wiser stock market investing. Examples include identifying credit card fraud, removing email frauds, or dismantling whole criminal networks.
Alibaba, one of the largest international merchants, used big data to create its fraud risk management system. The company has every reason to invest in preventing illegal activities given 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 US dollars]”.
Its multi-layered system for combating fraud has several layers. Account check, device check, activity check, risk strategy, and a manual review at the conclusion are the five (!!!) phases that make up the procedure. How comprehensive is that for a verification?
Avoid adding your name to the hackers’ list.
Big Data may, however, have both advantages and disadvantages, much like many other cutting-edge life sciences.
And they start employing increasingly advanced techniques, such as social engineering, DDoS assaults, or cyberstalking, to con individuals or steal crucial information.
It shouldn’t be shocking that the bulk of fraud instances involve money. “Consumers lost $5.8 billion to fraud” in the US alone in 2021. That’s a That is an increase of 70% from the prior year.
Unfortunately, if the priceless databases are not adequately protected, thieves may have equal access to Big Data as the “good guys.” And “the human factor” isn’t always to blame.
Therefore, it’s crucial to make sure that you not only invest in this new technology but also pay attention to making it safe against any threats.
The benefits of developing a Big Data strategy for your company
Is my company prepared for big data? is likely a question you’re asking yourself right now. Furthermore, “Can I afford to switch to using Big Data analytics?”
Every fintech firm is unique, as are every financial start-up. So let’s consider it from a different angle rather than giving you a firm response.
Although no one in the entire world ever discusses such enormous sums on a regular basis, the number does exist in mathematics.
However, a typical calculator won’t be able to handle this many zeros correctly. And that deficiency of capability relates to the difficulty of using Big Data as well. Although there are certain advantages to this new technology, it also has unique difficulties because it is still a relatively young field.
After all, processing huge data volumes isn’t always necessary for financial organizations. Many smaller ones even lack the necessary equipment, materials, or infrastructure to do so. Additionally, sometimes basic business analytics will work just well.
But you should start considering having a Big Data strategy in place starting now if you want to remain competitive in the fintech or insurtech industries in the future.
Participate in the revolution in banking
We are aware that some firms may find it expensive to invest in internal Big Data solutions. Or perhaps you’re simply not ready to apply it in your business yet.
That is why it makes sense to hire professionals like our data scientists to handle your Big Data analytics. So that your business can meet future difficulties, we can assist you in becoming a part of the Big Data user community now.
Conclusion
The twenty-first century would be known as “The Era of Big Data Science” if we had to give it a moniker. One of the finest instances of its application in practice is the banking and financial services sector.
The financial industry is already undergoing a change driven by data, analytics, and cutting-edge technology. You must determine if You must decide whether or not you are ready to join it.