How is Data Analytics Used in FinTech?

What is FinTech?

Financial technology, or FinTech, is a form of technology that’s involved with automating financial services. This field was designed to help businesses and consumers have more control over financial processes and operations. To do so, it leverages algorithms and customized software for smartphones and computers.

The various ways business transactions are executed is another facet of FinTech. This includes many technological interventions to commercial and professional finance, such as digital money. FinTech also pertains to various financial activities, such as managing investments, using a smartphone to deposit a check, and transferring money. Banks rely on FinTech for back end processes like keeping track of account activity, as well as front end endeavors, such as apps for checking an account balance.

Businesses around the world incorporate FinTech into their operations in many ways. Technologies such as contactless payment, payroll protection programs, e-commerce transactions, and payment processing are just a few advances FinTech has made possible. This field also pertains to efforts to develop and use cryptocurrencies.

This article will explore the important role data analytics plays in FinTech, as well as some of the benefits of incorporating it into a financial institution.

Applications of Data Analytics in FinTech

The field of data analytics is changing the landscape of the FinTech industry. It’s become more important than ever for a business to be able to handle large amounts of data in order to quickly extract insights. Incorporating new data-driven services can lead to an increase in a company’s revenue and efficiency, a reduction in costs and security threats, and a more competitive market presence.

Here are just a few of the ways data analytics is currently being used in the FinTech sector:

• An important first step when incorporating data analytics into the FinTech industry is to sort out data. The quality of data remains a concern for FinTechs. It’s crucial to have appropriate data that can be applied to AI and analytics. This means that unstructured data like emails, videos, and social media posts have to be merged with structured, internal data, such as SQL databases.
• Because of how quickly financial data volumes are expanding around the world, many organizations are now facing a corresponding challenge of managing this volume. In order to use this data to find actionable insights, businesses must be agile. Their data analytics efforts must be prepared for real-time market spikes and transaction surges.
• A skilled Data Analyst has an understanding of what information to search for when analyzing data. For example, in the banking sector, those trained in data analytics are tasked with creating customer profiles, as well as forecasting trends and behaviors.
• When applied to fields such as internet marketing, data analytics provides a means for a business to stand out amid competition by providing a personalized customer experience or even a customized marketing message that is geared toward certain customers.
• The use of algorithm-driven digital platforms such as robo-advisors is becoming commonplace in FinTech. They provide investors with automated investing and financial services. These robo-advisors require little or no human involvement. Instead, they begin by finding data on a client via an online survey. This survey indicates important information, such as their risk capacity, financial status, and monetary goals. This information is then used by the robo-advisor to offer advice to the client based on their current needs or goals.
• Detecting fraudulent financial transactions is another important way that data analytics is used in the FinTech sector. Big data analytics techniques like Deep Neural Networks can be applied to huge sets of data to predict where fraud may occur in future transactions.
• Machine learning and data science play a vital role in the process of risk analysis. Agencies that rate credit or work with credit scoring, such as FICO, incorporate techniques such as logical regression to help differentiate good borrowers from risky borrowers, as well as to forecast customer risk.
• The insurance industry uses data analytics and data science to help ensure that its company remains profitable and can manage its risk. This information also has applications in marketing, customer acquisition and retention, credit scoring, and in creating new insurance products.
• Both internal and external customer data is used by financial institutions to generate customer profiles, which are intended to customize their shopping experiences and provide them with offers based on their purchasing history.
• By applying predictive analytics to the field of FinTech, a company or organization can apply it to revenue projection and debt collection. Using predictive modeling allows a business to collect debts, even weeks after they were due.
• Another way data analytics factors into FinTech pertains to managing assets. Machine learning models are helpful tools that allow a company to work with a large amount of financial data and create subsequent asset management models based on it.
• Data analytics can help an organization ensure corporate compliance. In order to do so, it can monitor employee behavior throughout the organization to make sure corporate policies are adhered to.
• Customer service draws from big data to create a digital trail with information about a customer’s past financial behavior, as well as to identify potential problems and provide assistance when necessary. This information can also be used to offer customers the most appropriate products or services relative to their prior spending habits.

The Future of FinTech

While it’s hard to predict which FinTech innovations are yet to emerge, it’s safe to say that as technology continues to evolve, so will its intersection with the financial sector. Despite the effects of the COVID-19 pandemic, which have led to financial setbacks for many FinTech users, there is a greater demand for FinTech than ever before. More than ever, banking customers and businesses are depending on technology as a lifeline to their financial well-being. As the economy continues to recover, new FinTech opportunities are likely to emerge, ones that incorporate AI, blockchain, cryptocurrency, and other digital financial services.

The move within the financial industry toward data-driven solutions requires companies to quickly adapt and learn new tools and methods for working with data, and ensure that those at their workplace are sufficiently trained to keep up with new changes.

Hands-On Data Analytics & Data Science Classes

Noble Desktop also has a variety of data analytics courses available for those who want to learn more about the important role data analytics plays in the financial sector and beyond. These hands-on classes are taught by top Data Analysts and focus on topics like Excel, SQL, Python, and data analytics.

Those who are committed to learning in an intensive educational environment can enroll in a data science bootcamp. These rigorous courses are taught by industry experts and provide timely, small-class instruction. Over 40 bootcamp options are available for beginners, intermediate, and advanced students looking to learn more about data mining, data science, SQL, or FinTech.

For learners specifically looking to learn more about FinTech, Noble’s FinTech bootcamps are for you. Courses are available in topics like financial modeling, Python for finance, and data science.

For those searching for a data science class nearby, Noble’s Data Science Classes Near Me tool makes it easy to locate and learn more about the nearly 100 courses currently offered in the in-person and live online formats. Class lengths vary from 18 hours to 72 weeks and cost $915-$27,500. This tool allows users to find and compare classes to decide which one is the best fit for their learning needs.