Take a deep dive into the interplay between fintech and big data analytics to learn a few things about how the landscape of financial services is going to evolve in the near future.
So where does big data come from and what is it in the first place? In short, big data is an overarching term that describes extra-large and constantly growing arrays of diverse (both in terms of nature and structure) data gathered from a variety of channels that are hard to manage in a more raw, unprocessed form. One of the fundamental characteristics of big data is the steady growth of its volume and complexity.
As seen in the illustration above, with around 1.1 trillion megabytes of data generated every day by nearly 4.7 billion Internet users, big data is something that the financial sector simply cannot ignore. And if it’s that high up on the agenda, fintech companies need to move fast to address the three V’s of big data: Volume, Velocity, Versatility. In plain English, they need to build data analytics platforms and adopt new techniques to process more data or different types in the shortest period of time — ideally, in real time.
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The importance of big data in the fintech industry becomes even more apparent if we take a deeper look at user demographics. According to recent studies, Millennials are now the driving force behind the rapid development of fintech services.
They are skeptical about the ways banks communicate with them and look for more:
In addition, this lack of affinity for conventional banking often results in poor credit scores and histories of a large percentage of Millennials, seriously limiting their access to loans and popular financial tools. As many as 50% of Millennials may be affected by this problem, analysts say. Traditional financial services are still falling short of understanding their target audiences.
All of the above calls for a serious revision of old strategies and a more extensive use of big data analytics in fintech.
There are several distinct areas where financial technologies and big data form a perfect symbiosis that leads to significantly better outcomes for service providers and consumers alike.
In principle, these and other related practices and approaches form the data management framework of any modern digital bank or financial technology company. Big data collection and 360-degree data analysis are no longer viewed as a fad that reflects the latest fashion in computer science. On the contrary, they are essential components of any institution working with money and striving to be effective in a very competitive landscape.
Wondering how the AI + big data combo could be used to enforce your financial startup idea? We have the answers to your questions and will gladly share our thoughts with you.
Big data in fintech companies is truly ubiquitous and actively used in different contexts for a broad range of purposes. Let’s take a look at a few examples of where combining the use of big data and associated technologies makes the most practical sense.
Big data and ML-based analytics have been used in the industry for security and fraud detection purposes for quite a while. However, the more recent trend in online payments is the merger of payment processing with POS (Point Of Sale) lending mechanisms that enable users to get loans right at checkout. These systems combine ML algorithms and big data to instantly assess risk levels and the amount of credit available to the user, resulting in fewer cases of abandoned carts and higher conversion rates.
To take one example, the US-based SoFi.com uses all of the above-mentioned technologies to target young professionals and help them pay, save, invest, and borrow money in the most effective and secure way. The market is ready for next-gen financial platforms, and now is a great time for startups with innovative business models and a unique view on how consumers should be handling their finances online.
A lot of insurance companies still don’t use available data insights to create their products and services. On many occasions, they rely on demographic and statistical data that is outdated and no longer relevant. Because of this, they have a hard time setting optimal prices on their policies and often miss out on substantial financial opportunities that simply get overlooked. Modern insurance companies take full advantage of big data and actively use machine learning to create highly customized, low-risk insurance offers that address the specific needs of particular categories of users.
One great example of a service that is changing the car insurance scene today comes from the Swedish company Greater Than. Using massive amounts of literally on-the-ground information and road accident statistics, their business skillfully applies machine learning to aid insurance companies in estimating risks and adjusting pricing levels.
Big data and AI models are widely used in microfinancing and other types of lending businesses to reduce the cost of credit underwriting and make loans available to a wider audience that often has a challenged credit history. This promotes financial inclusion on the one hand and results in higher revenues for the insurer on the other. In addition, the wider accessibility of instant loans helps boost the economy in general and the business efficiency of small and medium enterprises in particular.
The number of POS financing companies and their revenues are growing at a steady rate. Market leaders like Klarna and Affirm are now facing serious competition from a new wave of POS lending startups, so now could be a great time to join the race with a focus on developing countries where the presence of traditional banking services is limited.
The priorities of real estate fintech companies are two-fold. First of foremost, they want to sell more with a higher margin, and this goal can be achieved through dynamic pricing, continuous market monitoring, provision of comprehensive information about properties in corresponding listings, and minimizing the risk of their clients defaulting on payments. To this end, they focus on capturing data from various sources and applying data analytics to make the right offers to the right people at the right time.
The other aspect of their business is retention. Leveraging the latest advancements in mobile technology, they can deploy low-power, low-maintenance IoT devices across rented properties as well as offer 24/7 infrastructure monitoring services. With detailed information constantly transmitted to the back office, they can optimize rental and maintenance offers dynamically and keep their customers happy for years.
In a comprehensive study by Deloitte Center for Financial Services, the authors conclude that “Companies can combine, analyze, and present insights from the large sets of data in a manner that tenants or other stakeholders can purchase and augment their actions and behavior.” This creates a fertile soil for startups employing this financial model to create IoT-based facility management solutions with multiple monetization models.
Now that we’ve covered the major areas in fintech and the big data analytics used in them, let’s focus on the specific ways in which these technologies and approaches can become a game changer for fintech institutions and their customers.
Customer segmentation is a part of Marketing 101 — and a very important one at that. In simple terms, it is the process of singling out different groups of customers based on either their actions/behaviors or specific characteristics, such as location, age, gender, marital status, income, education, job type and industry, just to name a few.
Data scientists apply a variety of methods, such as decision trees and clustering, to break down the entire pool of customers into groups with distinct properties. With this data, they can approximate the CLV (Customer Lifetime Value) of every customer or customer group, their appetite for investment risks, duration of mortgage repayment, and propose the best services for each category.
However, businesses are not the only beneficiaries of data analytics. Thanks to this data-driven approach, customers enjoy the improved relevance of offers, special loan repayment terms and other bonuses, and reciprocate with higher loyalty and lower churn.
Service personalization is the cornerstone of today’s fintech and something that the overwhelming majority of customers want to experience. For businesses in this field, the process consists of several steps:
Whether happy or unhappy, customers do not always share their feelings directly with their service providers. With big data and AI, companies can now crawl social networks and hear the voices of their customers more clearly. This results in faster responses to reported issues and the subsequent creation of a positive image of the company thanks to a public demonstration of high customer care standards.
The financial industry is heavily regulated at many levels and is subject to frequent audits and certifications that result in a high managerial overhead and resource drains. With big data and AI used strategically, fintech companies have a bird’s-eye view of relevant parameters and can be better prepared when the time comes to reconfirm their adherence to norms and regulations. Their customers, in turn, benefit from having services provided by a trusted financial partner.
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In the next few years, we will witness an intensifying convergence of traditional banks going digital and innovative fintech startups striving to claim their turf in the global financial world.
Big data in the fintech industry will continue to dominate the scene, assisted by evolving AI algorithms and the ever-deepening analysis of consumer behavior.
In order to stay competitive, financial companies will have to adopt a number of new practices — and often an entirely new mindset — focused on hearing the voice of the customers and, even more importantly, predicting their needs by following their digital footprints.
EPAM Anywhere Business has delivered countless financial solutions to startups and SMBs. And with direct access to a vast pool of EPAM’s fintech solution architects and engineers, we are always ready to discuss your product concepts and advise on the optimal approach to their implementation.
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