The Role of Big Data in FinTech

Contemporary enterprises and sectors around the world aren’t strangers to the cliché we’ve all heard once before: data is the new gold. While it indeed has become a hackneyed phrase, it accurately depicts the way modern humans function; without data, our increasingly tech-dependent world wouldn’t move the way it does. Today, everything we use collects data about our habits, preferences, and behaviors, from smartphones to laptops and even home appliances. Whether that is a good or a bad thing is debatable, the heart of the matter is that the data pools keep growing, and businesses’ interest in them is only getting bigger. These neverending data lakes, or Big Data, are becoming fundamental for business success. Some sectors, like FinTech, are reaping the benefits of Big data to generate valuable insights and change the way they develop new business models. Moreover, Big data in Fintech can empower companies to provide better services and more user-oriented products to improve the way banks and financial technology companies operate. 

The way FinTechs manage and derive insights from Big Data is changing how they function and disrupt how the world uses this information to drive business decisions. These insights derived from Big Data are also helping the 20,000 or so FinTechs that currently operate globally to leverage Big Data Analytics (BDA). BDA can also be paired with modern technologies such as the Internet of Things (IoT), Blockchain, Artificial Intelligence (AI), and machine learning (ML). Thanks to Big Data, FinTechs can use these technologies as keystones to enable them to stand out in a fiercely competitive market where innovation moves at an unrelenting pace. In effect, the adoption of AI and ML paired with Big data and BDA is the pathway for FinTech startups and companies worldwide to rise above the competition and deliver business value that significantly surpasses the status quo.

Let’s dig deeper into what Big Data in the financial sector is and how it is used in FinTech.

 
 

Why is Big Data Crucial in FinTech?

Big Data is a collective term that describes ever-growing, large, diverse, structured and unstructured, and hard-to-manage volumes of data gathered from social, machine, and transactional sources. Put simply; Big Data is a group of complex, unorganized data sets that grow exponentially in volume, variety, and velocity. To illustrate, we know that internet users worldwide generate around 2.5 quintillion bytes of data every day. Additionally, just last year, every person generated 1.7 megabytes of data per second using their devices. That’s a lot of data we put out there every day. However, the amount of data isn’t what matters. It’s what companies do with it that matters. You can have data centers full of unstructured data, but if you can’t derive insight from it, it’s useless. Luckily, FinTechs can analyze Big Data for insights that lead to better decisions and strategic business moves. So, since these data sets are in fact so voluminous and grow so fast, conventional data processing software and techniques aren’t enough to manage and analyze them. Enter BDA. 

Big Data Analytics is understood as the complex process of applying tools and techniques to analyze Big Data to uncover patterns, correlations, trends, and preferences that can help organizations make more informed decisions and identify new business opportunities. For this reason, Big Data and BDA are becoming paramount for companies and organizations worldwide to make intelligent moves and add direction to their business endeavors. Thus, from healthcare and banking to e-commerce and sports, Big Data has become the ultimate game-changer that has the power to turn the FinTech sector upside down.

Now, why is Big Data so essential in FinTech? Even if you haven’t heard of it, the FinTech sector does use Big Data extensively due to the complexity of its services. Likewise, its use of modern technologies in day-to-day operations and its need for high-security levels and risk analysis also rely on large amounts of data. Additionally, and aside from the figures exposed above, which attest to the ever-growing volumes of Big Data we generate every day, we also know that the BDA market for the banking sector could rise to $62.10 billion by 2025. Who wouldn’t want to get a piece of that juicy market? The numbers speak for themselves. However, aside from the evident monetary benefits, Big Data can help FinTechs arrange their vast amounts of information and translate them into actionable insights. They can then use these insights to drive market predictions, design future strategies, and even personalize customer service processes, among other benefits.

The fact is that FinTech startups, online banks, and FinTech app developers are all unlocking the real power of Big Data. They use it to make intelligent business decisions that set them apart from competitors and large, long-established financial institutions. But how exactly is Big Data helping FinTechs accomplish these benefits? Read on to find out. 


 

Big Data’s Role in FinTech

 
 

Aids With Better Customer Segmentation

Customer service is one of the critical components of enterprise success, and FinTech is no different. However, in the era of modern technologies, delivering outstanding customer service goes hand-in-hand with having robust data mining and analysis techniques. These techniques are fed by Big Data and, ideally, provide insights that are translated into detailed user profiles and powerful customer segmentation strategies. The latter is one of the most effective ways FinTechs can get to know their customers on a more profound level to understand each customer’s needs better, target those needs, and identify their lifecycle to increase scalability, reach, and revenue. For instance, modern FinTech users demand more flexible journeys, with a survey finding that 71% of them now prefer multi-channel interactions. Moreover, 25% of Financial Technology customers now want a fully digital banking experience fueled by remote human assistance available when they need it. How else can companies identify and target these needs if not for Big Data and customer segmentation? 

Since Big Data collects vast amounts of information regarding customers such as age, sex, ethnicity, socio-economic status, location, preferences, purchases, and buying power, FinTechs can use data analytics to evaluate these data sets and create more specific user profiles. Also, in this regard, FinTech companies can pinpoint spending habits and identify their relation to age, gender, and even social class to identify the high-value customers that are most likely to make purchases. This way, Big Data allows FinTechs to ensure their clients have access to the most appropriate payment technologies, credit card limits, account capabilities, and promotions at the right time, and based on their spending capabilities, all thanks to proper customer segmentation. 

With these clear-cut customer profiles, FinTech companies can establish hefty, reliable customer segmentation strategies to drive their ability to satisfy specific customer needs instead of broad, generic ones. Equally important, FinTechs that use Big Data-driven insights for their segmentation strategies can also tailor their service portfolios for the different customer segments. In addition, they can also personalize their products to meet each customer’s preferences adequately. This way, with more efficient and adequate customer segmentation, FinTech companies can ensure higher customer retention and more robust customer service strategies that help with positioning.

Lastly, customer segmentation fueled by Big Data can also help FinTechs capture a significant portion of the market for their products before the established and traditional banking institutions and their newfound innovation efforts start catching up. These conventional banks are beginning to understand the benefits that technology brings to the sector and offer products and services that can compete directly with FinTech. However, traditional banks aren’t inherently proficient in harnessing and analyzing Big Data, one of FinTech’s chief characteristics. This way, Big Data provides a unique opportunity for FinTechs to gain some competitive edge and stand on top of traditional banks.

 
 

Helps Deliver More Customer-Centric Services

Big Data-driven customer segmentation strategies will unavoidably end in more customer-centric services. In the FinTech industry, like in many others, the ability to offer personalized services is one of the greatest assets and a top marketing tool. In fact, 76% of modern customers expect companies to understand their specific needs, behaviors, and expectations, meaning that the everyday user will not stand for generic, non-specific products and services. These demands for high-quality, distinctive, low-friction, and around-the-clock customer service experiences are non-negotiable and are demanded across every channel. So, to deliver these personalized services and experiences, FinTechs must enforce in-depth, holistic strategies that target user needs from every angle and get to know them on an almost personal level. FinTechs can only achieve this in-depth knowledge by leveraging the troves of Big Data available to them and gaining insight from them by using robust data analysis techniques.

Data-driven insights can also help Fintechs provide quick solutions to common problems banking users have faced for years. Annoyances such as switchboards, operators, long lines at bank branches, paperwork, and endless waiting hours to speak to someone over the phone or in-person are all coming to an end at the hands of FinTechs, artificial intelligence, and Big Data insights. Indeed, with Big Data-driven insights, FinTech companies can easily collect and analyze crucial information about their user’s banking activities, identify pain points, target anomalies, and errors, and react accordingly. This phenomenon will inevitably result in a substantial increase in the quality of customer service and the improvement of tech-driven service channels such as live chats, automated tellers, and chatbots. 

For instance, in the case of live chats, instant frontline personal interactions occur between the FinTech company and its users without the need for in-person meetings, branch visiting, or long waiting lines. Also, live chat agents can now access user data regarding what products the customer has, the ones they are most likely to buy, how they usually pay for stuff, their buying power, and their latest purchasing trends. These data sets allow agents to tailor FinTech experiences for users and solve their problems empathetically. And besides quick fixes to typical issues, customers also get relevant product recommendations based on Big Data insights, improving the accuracy of marketing initiatives and propelling customer retention and loyalty programs forward.