How to Succeed in Fintech in 2022 Using Big Data and Machine Learning?
Dec 15, 202111 min read
Co-founder and CEO of Ideamotive. Entrepreneur, mentor and startup advisor.
Big Data, together with one of the key branches of AI—Machine Learning, are hands-down some of the two most popular buzzwords in finance in recent years—and not without reason. ML and Big Data are taking over one industry after another, with Fintech, in particular, growing exceptionally fast. As more people are beginning to realize the possibilities of AI solutions for businesses, the quicker they evolve, and in 2022 they are only going to get bigger. AI-based tools that Fintech of today offers have already made a massive impact, not only on the financial world but also the world in general. Fintech is already lifting people out of poverty by providing easily-available financial services to anyone who owns a mobile device, and frankly, it seems like this is just the beginning.
The combination of ML and Big Data is most certainly not arbitrary. One truly unique element that quickly comes to mind is the incredible boost in the sheer amount of data that only machines can handle. Big Data would not exist if it weren't for machines that have substantial computing power, as it would otherwise not be humanly possible to process this much data. What follows the size is the accuracy of data recognition. When comparing how much data can be processed by a machine vs. a human being, it is no secret which of the two is inevitably going to be more efficient.
Because machines can be fed such large amounts of data, they can also become better at processing it and automatically recognizing different inputs. ML can learn from trends and create different intelligent outcomes, which lead to enhanced accuracy, decision-making, and efficiency in the data processing. That’s not to say humans are inutile; top fintech engineers are absolutely incremental to any fintech business’ success as someone has to get the machine going. Let's take a closer look at some of the real-life examples of how Big Data and ML are applied by some of the greatest in the Fintech industry to their success.
1. Fraud Detection - how Big Data helps fintech stay secure.
Cybercriminals are out there to get you.
When was the last time anyone has ever heard of a bank heist? Not too recently I'm sure. The truth is modern bank heists, unlike the ones from the pre-internet era, are not carried out with guns and masks. Modern criminals are getting smarter and more effective every day, and they are often impossible to find. These cyber-criminals, or hackers, are relentlessly trying to steal from individuals, small businesses, or even major corporations like, for example, Microsoft.
Know The Dangers
Whenever a breach like this occurs, it comes with a risk of exposing sensitive personal data records, including full names, addresses, email correspondence, payment logs, and more, which, for any business, may lead to loss of trust or even lawsuits. For individuals, it could mean losing personal photos, blackmail, or even identity.
In the post-covid era, there's an observable increase in online fraud, as people continue to spend more time online than ever before. The fact that even the biggest players often fail to secure their businesses from potential breaches signals that there is still a lot that has to be done. Major corporations, such as Patreon and Razer, understand these dangers and the importance of keeping the data as safe as possible by using solutions like Shield or Seon. One example from the Fintech sector is Gemini, a popular online cryptocurrency trading platform. Fraud protection startups are sprouting all over the internet and it seems as if there are at least a dozen new freshly-founded companies every day. it is a good idea to learn more about their services, as they might just save your company one day.
That's where technology comes in really handy
Thanks to Big Data it is now possible to process and analyze millions of databases comprising records of previous breaches and make sure there are fewer chinks in our armors. What's more, we can use ML, or to be more exact artificial intelligence neural networks, to process the data and predict different outcomes so that we are ready before the strike comes.
Falling a victim to a hacker can mean anything from losing $100 off a credit card to losing identity or a business altogether. The sophistication of online criminals is undoubtedly increasing, but luckily, so does that of cyber security.
2. Extremely Powerful Data-driven Analytics
Data analytics in Fintech is growing exceptionally fast and Machine Learning is what makes it possible.
Machines, apart from being capable of processing massive amounts of data, have another advantage over humans—they have no feelings.
A machine can process endless amounts of data with no bias whatsoever. This results in cleaner, more exact data that leaves far less room for error which in turn translates into a better understanding of the customer. But this is just the beginning when it comes to how Big Data helps fintech.
Automated analytics is what allows to automatically detect relevant patterns, trends, or anomalies, and provide real-time insight to individuals or business owners without having to manually analyze any of them.
New technologies such as Machine Learning algorithms can be applied to provide consistent monitoring of operational performance. What's more, they enable users to track user-defined log-based metrics and search massive data sets for factors that correlate with desired business outcomes. Then, they notify the user about any observable changes at set intervals or triggers and deliver the fully-analyzed results. Great examples of tools that allow for extremely concise automated data-driven analytics could be Hubspot or Google Analytics, without which, frankly, modern business would not be possible.
3. Data-driven Decision Making (DDDM)
Feelings are fine. As long as they are supported by data.
Allowing a machine to evaluate the performance of a human being may be controversial, but it is not as bad as it sounds. Like with everything else, there are certain patterns in human behavior. A machine, or a program to be more exact, overseen by a keen eye of a data scientist, is able to analyze those patterns and provide unbiased, data-based information. That way, AI can be administered to optimize those patterns and improve overall productivity. By gathering large chunks of data and constantly analyzing it, AI makes it possible to predict positive and negative patterns and change them, pushing decision-making to become a data-driven and unbiased endeavor. Of course, there are some downsides to this too, as machines are not quite able to fully understand human emotion and behaviors, making such plain data seem a little bit harsh when judging the performance of an individual.
DDDM is a clear opposite of what one feels, thinks, and believes. That's not to say that what we feel and think does not matter. With this technology, we finally have an additional pair of eyes that can see the world in a fully rational and unbiased way - something that we have never been able to achieve before. Not only does it not take anything from our humanness, but it adds a massive factor to how we manage, organize and ultimately, perceive the world around us.
Big Data is truly everywhere.
Building on the previously mentioned data-driven analytics, it seems only natural to mention enhanced personalization and profiling. In the age of increasingly decentralized and globally available mobile banking systems, it is the customer who calls the shots. Fintech is growing extremely fast which results in a massive boom in different platforms for customers to choose from. In the end, they will most likely lean towards the ones they like best so it is up to the Fintech companies to meet them there.
Big Data and AI-based Machine Learning guarantee an immeasurable edge over the competition by analyzing great quantities of high-quality data and using that data to create customer-centered personalized services making sure customers receive everything they need. What's even more important is that in the digital age information is more available than ever, providing endless amounts of data to derive information from.
5. More Effective Risk Management
Take your risks the smart way.
There is no doubt that there are risks involved in any business, whether it is Fintech, finance, or any other sector. Risk management, in short, is figuring out a viable long-term strategy that will allow the company to minimize, or even completely eliminate certain risks. In reality, however, it is fairly clear that the elimination part is a little bit like the bigfoot - it might be somewhere out there, but nobody has quite managed to find it yet. So, until somebody finds it, the only thing that can be done is to accept that there are always some risks that can not be avoided and prepare for them as best we can.
What are our options?
The smart thing to do right now is to use the best of our data to optimize risk management, accept some well-calculated and predictable risks, and keep up the overall functioning of the company. Risk management analysis can be thought of as being cognizant of the fact that there are risks to be faced by all financial institutions, including Fintech, and developing the most suitable ways to address them. The elimination of risk or avoiding risk altogether is usually not possible, so let’s keep that in mind when devising a risk management strategy.
While carrying out a credit risk management analysis, financial institutions have to carefully evaluate the reliability of individuals, groups, and corporations who apply for a loan. The lending party has to make sure that the applicants will be able to fully repay the loan before they are given any money.
With the use of Big Data, it is far easier to manage the data of those who could not repay the potential loan and filter the most problematic ones out automatically, managing the risk of lending the money to an insolvent individual. Because Big Data is capable of assembling massive data sets in record time it automatically allows companies access to more quality and quantity of data, allowing for more risk-management processes to be automated, saving both time and money, while ensuring superior precision and real-time updates.
6. Customer Segmentation
Know your audience.
In a nutshell, customer segmentation can be understood as putting customers into various categories, such as gender, age bracket, profession, or hobbies, and is based on their similarities and characteristics. The only limit to segmentation is the quantity of data a given company has, but other than that, it has practically no limits.
Data is money - how Big Data helps fintech business-wise
As the overall value of data continues to rise, companies and other institutions are more willing than ever before to pay a major buck for quality insight into things like consumer behavior, market trends, or product performance. They do that because that way they can learn more about their customers and their needs which in turn greatly facilitates taking key strategy and marketing decisions. In turn, that means that there is a growing need for capable Big Data engineers who are not easy to come by.
It is no secret that Big Data and Machine Learning are absolutely key when it comes to analytics, and it is no different in this case.
ML is capable of managing various segments of customers with utmost precision which frankly is impossible with the amounts of data companies are dealing with today. That makes it an absolutely invaluable tool when it comes to learning the latest trends as well as customer data analysis.
7. Process automation and RPA
Robotic process automation (RPA) is the use of AI-based Machine Learning software technology in designing, creating, and managing software 'bots' that take care of real-life tasks that, until recently, could only have been handled by humans. By simulating real-world tasks they can carry out activities such as invoice processing, customer support, or even hiring and onboarding of new employees. RPA is being adopted by businesses for a number of different purposes, ranging from automating recurring processes to examining vast amounts of data that can help determine the information that is suitable for us.
What do I get from using RPA?
The RPA technology can result in a massive reduction in the number of errors that may often occur while carrying out the most repetitive tasks. Top AI engineers can automate these tasks making it finally possible to effectively minimize human error and improve the overall quality of the work. What is even more impressive is that it is less expensive and far more efficient than ever before.
Let’s talk human to robot.
Chatbots are conversational systems based on Machine Learning and Big Data technology. AI-based technologies continue to thrive shifting the path of customer service every day. Chatbots AI relies largely on Big Data - its amount and its quality. The new advancements in Machine Learning and other data technologies are expected to lead to even more useful chatbots in the future.
Do I need a chatbot?
Well, yes and no. Chatbots can successfully and reliably automate customer service via various communication channels like text messaging or chatting. What is so great about chatbots is that they allow for an instantaneous response whenever a customer asks a question. It is especially helpful because the customer is not forced to wait, often for hours, before a real person is available to respond.
A well-designed and carefully programmed chatbot is able to provide a surprising amount of valuable and accurate answers that can greatly improve the customer experience as well as successfully reduce the overall workload. Moreover, they can recognize your typos and still understand what you wanted to say, all thanks to Big Data and the number of typos they were able to go through in record time!
There certainly are times where a chatbot is not enough. Luckily, chatbots can be programmed to identify various problems the customer might be facing and, if needed, they can notify their human overlords about the issue.
One of the prime examples of chatbots in Fintech is Erica, the chatbot of the Bank of America. Erica acts as your personal accountant, helping you with your finances every step of the way, whether it be viewing balance, locating your past transactions, or reminding you of your upcoming bills. And yes, you guessed it, she's there for you 24/7, always ready to help.
9. No-need for the outdated in-person transactions
Stay home, save lives. And money.
In 2021 it is hard to imagine a regular customer actually wanting to go to a bank branch in person unless they really had to. Why withdraw, deposit, or trade at a physical location, when it can be done as safe, if not safer, from the living room? In this day and age, everything has to happen as fast and as digital as possible. This has some downsides, perhaps, such as fewer human-to-human interactions, but does appear to have even more upsides.
Time is money
Big Data wants nothing to do with meeting anyone in person, as all the valuable data can easily be collected online. Furthermore, because the amount of data is so vast, it is easier now than ever to segment customers and serves their personal needs—something no human could do. More importantly, the pandemic has shown us that classic, physical institutions, are fragile, and often obsolete.
Modern Fintech players, like BlockFi or Gemini, make sure to pay a major buck and employ top algorithm developers so that they can do their business on mobile, with very limited in-person interaction, not because it is a bad thing, but because few people need it. A well-designed chatbot and support system can really go far without having to meet face-to-face. Which is good!
10. Remain competitive in one of the most rapidly evolving financial sectors
Don’t fall behind the competition.
The massive impact of Big Data and Machine Learning is not just anecdotal. The question is not if, but how Big Data helps fintech. Statistics show that as of 2020, the Big Data market has reached a whopping $140 billion and is well on its way to reaching $230 billion by 2025. At the same time, the Machine Learning market is no slouch either. The AI market, which Machine Learning is a part of, is valued at $62.35 billion US dollars as of 2020 and is expected to grow at a CAGR of 40.2% in the years 2021 to 2028, according to this research study from grandviewresearch.com.
We are at a point where the financial market is so deeply intertwined with both AI and Big Data that it is becoming hard to separate classical finance from Fintech. Increasingly, big banks and financial institutions come out with new ways to please their customers. To do that they build handy mobile apps, invest in chatbots, and analyze massive amounts of data in order to segment and personalize it. These statistics simply state the obvious: there is no finance without AI and Big Data in 2021 or the near future.
11. What will the future bring and why should you care?
The future of both Machine Learning in Fintech is certain—they are here to stay. What should also be obvious at this point is that Fintech is not the only industry in which the two have managed to put down deep roots. All of the tech giants, like Microsoft, Facebook, or Google are working on their own AI technologies as they continue gathering massive amounts of data on a daily basis.
The future before us is not always certain, however, we have reached the point where we are fully dependent on machines and are becoming more deeply intertwined with AI. What we can do is take one step forward and invest in the future by investing in these technologies and machine learning engineers who are capable of creating them.
Are you looking for ML & Big Data engineers to join your Fintech business?
Having access to top people who are already ahead of the curve is critical when dealing with groundbreaking, game-changing technologies. At Ideamotive, we believe in the value and change that one truly passionate individual can make and admit only the best. We make sure that our customers get only the highest quality and the most avid professionals that can alter the whole game all by themselves. If you are looking for qualified Machine Learning and Big Data engineers to join your Fintech business, look no further. We have the people you need.
Robert is a co-founder and CEO of Ideamotive. Entrepreneur, who with passion spreads digital revolution all around the internet. Mentor and advisor at startup accelerators. Loves to learn and discover new business models.