The role of technology in financial services has been a transformative one thanks, in part, to the vastness of data pools, or Big Data, available for FinTech to mine and analyze. This data harvesting has resulted in invaluable insights for the sector to use and drive the growth of FinTechs around the globe.
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.
Enhances Fraud Detection and Security Protocols
According to a study conducted by Immuniweb, 98 of the top 100 global FinTech startups are vulnerable to cyberattacks, including phishing and web and mobile app security threats. Sounds scary, right? Sadly, security liabilities are all too common in the sector, especially now, with the increasing popularity of FinTechs, online banking, and digital payments. As a result, the chances of fraud rise alongside the growing adoption of FinTech solutions, and the threats seem never-ending. Still, however concerning modern security menaces are, Big Data is starting to place itself at the forefront of the sector’s advancements in security protocols and fraud detection initiatives. This way, Big Data is helping FinTech firms and startups create better fraud detection algorithms, more robust security protocols, and impenetrable payment systems to withstand hacking attacks and fraud attempts.
Big Data’s ability to help prevent malicious attacks primarily lies in its information regarding which transactions individuals usually make, when they make them, the geographical location they make them from, and their usual patterns when using the app or website. Using this information and pairing it with data science, AI and ML algorithms, and Big Data analytics can help enhance security protocols by identifying fake behavior outside the user’s standard patterns. This detection usually relies on AI pattern recognition and Big Data analytics, and it identifies changing usage patterns that differ from the norm. Moreover, this process is entirely automated. It alerts the competent departments to contact the user to see if they or someone else making the transactions, thereby preventing fraud and security threats. This way, using Big Data analytics and AI-powered technologies to power fraud detection systems helps FinTechs protect their customer’s money. More importantly, it helps maintain the biggest asset for any financial company: their user’s trust.
Big Data and AI-powered technologies such as Machine Learning can also help FinTechs optimize their security protocols by monitoring the workflows of company workers to prevent information leaks, whether intentional or unintentional. It can also help identify irregularities within the company’s systems, suspicious activities, login attempts, malware and provides intelligent risk management insights to reduce, or eliminate, the impact of security incidents.
Helps Optimize Operations
While most FinTech startups and online banks have been implementing Big Data Analytics and its applications, the focus has usually been on sales, customer service, marketing, and security. However, given the importance and the amount of money and effort that FinTechs spend on operations, there should be a sharper focus on leveraging Big Data for operational capabilities. The predominant role that business operations have in ensuring that all services mentioned earlier work seamlessly makes them an essential part of the FinTech ecosystem. As such, FinTech firms and startups can gain exceptional competitive advantage by applying Big Data to push their operational effort forwards. Core operations such as risk assessment, loan servicing, human resources, and even legal departments can significantly benefit from Big Data applications. On that same token, Big Data in FinTech helps employees have the necessary data always at hand, helping them provide highly personalized experiences and handling individual customer cases better. Having readily available information improves operations and pushes efficiency forward in customer service, marketing campaigns, and other processes. Furthermore, with Big Data-driven insights, FinTechs can also assess production and gather and analyze customer feedback to tackle paint points, anticipate future demands, and improve decision-making processes.
Aids in Credit Risk Scoring
One of the most lengthy, tedious, and expensive processes FinTechs have to deal with is credit risk scoring. Operationally speaking, credit risk scoring has always been one of the most inefficient, protracted processes in banking due to the absence of data, the lack of knowledge about data analysis, and the shortcomings in the availability and access to data management tools. Furthermore, since most modern FinTech firms have to give out loans and credit cards to expand their portfolios, they must have the proper data management and analysis tools and regulate their risk exposure, keeping it as low as possible. However, with modern technologies demanding faster response times, the key to performing efficient credit risk assessments and eclipsing traditional banking’s drawn-out assessment processes lies in the quality and speed provided by Big Data and AI-powered technologies.
So, once the problem of insufficient data and lack of access to it is solved, the availability of information becomes an asset, and Big Data becomes the keystone to faster operations and more efficient credit risk assessment. Accordingly, due to the immediate and scalable nature of Big Data, FinTechs that use it have access to all the information they need about their customers, past, present, and even future. This last point is crucial because it allows firms to gauge the customer’s financial condition even after the loan or credit card has been issued and identify any changes to minimize risks. Big Data also allows FinTech companies to keep an eye on a user’s payment behavior and their communication with other financial firms.
Additionally, Big Data helps FinTech companies quickly broaden their customer base by providing insights into potential users and their spending habits. Traditionally, financial institutions avoided giving out loans, credit cards, and even checking accounts to people with no credit history. Today, thanks to Big Data, FinTechs can use information from social media, smartphones, and search engines to assess the creditworthiness of potential clients almost instantly. They can also direct personalized marketing services to potential customers with very low risks for the company. And at the same time, Big Data helps reduce human errors when developing risk profiles, identifying potential risk sooner, and fishing out bad payers beforehand. Still, while Big Data cannot wholly eliminate risks for FinTechs, it can help them identify those liabilities earlier and prevent further losses.
Remaining Challenges of Big Data in FinTech
1. Regulatory pressure
The financial sector is significantly regulated due to banks having to store customers’ financial information. Still, today’s concerns over privacy are making it increasingly complex for FinTech companies to collect and store user data in ways that don’t violate regulations. The FRTB, AML/KYC, and FATCA are some of these regulations that control how banking institutions collect information about their users. So, for FinTech firms to successfully integrate Big Data into their operations, they must meet all the requirements established by these and other regulatory laws. However, for Big Data to realize its true potential in the FinTech sector, a drastic shift in the regulatory framework is needed. Despite the advances in BDA, AI, and ML, the FinTech sector is still somewhat gridlocked by regulations that are stuck in an outdated mentality. This mentality also holds the FinTech sector back from adopting more complex BDA solutions and models that could further enhance its ability to fulfill its users’ needs.
2. Lack of IT Infrastructure
Big Data’s adoption in the FinTech sector is still at its early stages in most parts of the world. The vastness of its volume and variety, coupled with the importance of maintaining data quality, present challenges across the FinTech value chain, mainly due to the lack of IT infrastructure in the sector. Without the proper tools, techniques, and infrastructure, it is complicated to harness and analyze Big Data properly, let alone integrate AI and ML models into the existing organizational systems. Furthermore, most FinTech startups don’t have the capital to integrate everything Big Data encompasses into their businesses right away, while other larger incumbents still work with outdated legacy systems.
3. Poor Data Quality
Collecting large amounts of data is not a good thing on its own. What matters is the quality of that data and what companies do with it. Similarly, the accuracy of advanced Big Data Analytics capabilities and the integration of Ml and AI into the value chain of FinTechs are heavily reliant on the quality of the data they gather. However, most FinTech firms, especially startups, don’t fully comprehend the relevance of data quality. As a result, they risk making mistakes such as wrongly marketed products, faulty customer profiles, and inexact credit risk assessments. These mistakes can cost companies their clients, and they will have to reinvest large amounts of money to fix the errors produced by poor data quality. Additionally, poor data quality can also jeopardize regulatory compliance, data security, and company image.
As Big Data keeps gaining popularity among FinTech companies and starts to play a more significant role in the industry, it will become the lifeblood of countless organizations. FinTechs will be more adept at providing their customers with seamless service on every channel, thanks to the insights provided by Big Data. Similarly, improving their services and offering more personalized products and services can give the FinTech sector an edge and help firms increase their adoption rates. And since, unlike traditional banks, FinTech companies focus on creating personalized financial services, Big Data is the perfect ally to keep their user-oriented approach sustainable and scalable.