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HomeBlogData Science Data Science in FinTech: Roles, Use Cases, and Benefits
Financial technology, popularly referred to as FinTech, is one of the fastest-growing areas of technological innovation and is popular with venture capitalists. FinTech refers to a set of technologies that focus on new ways of providing banking and financial services to consumers. When you use PayPal, Google Pay, or your credit card to pay online, you, the consumer, the e-commerce company, as well as the bank are all using FinTech to complete the transaction. Over time, FinTech has evolved to disrupt almost all aspects of financial services, including payments, investments, consumer finance, insurance, securities settlement, cryptocurrencies, etc.
FinTech companies depend heavily on FinTech data science to simplify financial decision-making and deliver cutting-edge solutions. So first, let's see what data science in fintech is and its importance.
Data science is a field of study that combines industry expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. To perform tasks that normally require human intelligence. Data scientists apply machine learning algorithms to numbers, text, images, video, audio, and more to create artificial intelligence (AI) systems. These systems, in turn, generate insights that analysts and business users can translate into tangible business value. Want to learn more about Data Science in FinTech? Check out the Data Science course fee to start your journey.
Data is not useful until it is transformed into valuable information. Mining large datasets containing structured and unstructured data and identifying hidden patterns to gain actionable insights are two main tasks in data science. The importance and application of Data Science lie in its tremendous uses, from everyday activities such as asking Siri or Alexa for recommendations to more complex applications such as controlling self-driving vehicles or robots.
Organizations can also measure, track, and record performance metrics to facilitate decision-making across the enterprise. Businesses can analyze trends and make key decisions to retain customers, improve performance, and increase profitability. Data science models use historical data and can predict future actions. This helps organizations find ways to achieve the best business outcomes. Data science helps organizations identify and refine audiences by combining existing data with other data points to generate actionable insights. Data science also helps recruiters combine data points to identify the best candidate for the company's needs.
we now live in an era that is unprecedented due to the amount of data around us, most of which is new. Here are some statistics to illustrate my point
In case if you want to know 1 exabyte = 1 billion gigabytes. To put this in perspective, it is estimated that the entire contents of books printed ever hold 10 terabytes of data in all printed materials. Recent estimates of the size, including audio, video, and digital materials, start at 3 to 20 petabytes. So, one exabyte could hold 100,000 times the printed material, or 500 to 3,000 times the entire contents of the books printed, and we produce 3.3 exabytes of data every month!
The best companies in FinTech are all trying to capture, measure, and draw insights from all the data that is around us now, even though it may be different information – some may even label it as “noise”.
Even more exciting from our perspective, there are some very savvy entrepreneurs who are trying to build entirely new information services businesses out of data using FinTech data science that they can capture, manage and draw insights from. Check out what FiscalNote, DataFox, and ThinkNum, among others, are doing. (I daresay their products will blow a lot of people's minds.) There are many factors driving the “Big Data” (or what we call “information services”) opportunity within FinTech but let me focus on just two. First, there has been a big bang moment of newly available data sets, when almost all information, including financial transactions, is moving online. Second, advances in technology now allow machines (i.e., computer code) to replicate and increasingly even replace much of the work historically performed by human analysts (both the most demanding and well-paid analysts).
I believe there is a great opportunity to create value for companies that can automate the collection of relevant data (through tools such as natural language processing) and deliver actionable insights drawn from data using data science, artificial intelligence, and advanced visualization capabilities so due to the complexity of this jobs, companies pay FinTech data scientist massive amount salary.
In the financial industry, there is an increasing emphasis on big data and the possibilities hidden in it. Big Data, due to its technological foundation, has profit-generating opportunities and challenges, especially for the FinTech sector, and we are only at the beginning of what can create such a large amount of consumer and transactional data. Big Data is empowering innovators to advance hundreds of digital, technology, and internet industries, creating new sources of profit, better customer experiences, and new technologies that shape the way we live. Big Data in FinTech has helped incubate the industry, and as it continues to expand, we will face new challenges and opportunities.
Data Science is becoming a trend for various FinTech as it can help them quickly solve various business problems. Here are the most common use cases in the FinTech industry:
Fraud detection is of paramount importance to financial institutions, so they are constantly looking for fraud prevention tools and various ways to automate risk management. Various types of scams attempt to imitate, steal, or carry out money laundering schemes. An effective anti-fraud tool requires prevention, protection, and reporting systems. A data warehouse receives data on-the-fly from payment processing systems and feeds it into models to generate real-time results. Data science in scanning FinTech organizations also helps define fraud cooperation patterns and create interactive charts and diagrams. To learn more about fraud detection using a live course, check out applied Data Science with Python.
Deep learning about customer performance enables real-time user segmentation, customer behavior modeling, and predictive analytics. BI tools allow you to visualize your users' financial activity in the digital banking ecosystem. Statistics on users' financial behavior can help FinTech organizations create product strategies. Another metric that data scientists can provide to FinTechs is the customer lifetime value (CLV). This is a projection of all the benefits a company can derive from a customer relationship.
A risk modeling system helps determine if a user is trustworthy and has access to additional services, higher cash credits, and lower rates. Data scientists can build models based on product usage and open-source information from various sources.
Product improvement strategies can be based on product usage analysis and market information. Data scientists can model and predict functional changes in customer behavior and responses to changes in FinTech products.
Process improvement can be based on the use of the digital twin approach, which is a recent trend in product development. Financial organizations or digital banks can monitor and analyze offline operational and customer support process metrics to simulate changes and assess future impact.
Personalized marketing is one of the most powerful tools for promoting FinTech products. Data Science makes it possible to analyze user behavior patterns and suggest relevant financial products and services to users.
And some other uses of data science in the FinTech industry are Real-time analytics, consumer analytics, algorithmic trading, Robo-advisors, financial planning, etc.
These use cases create ample FinTech data science jobs, so to transition your career in data science, check out top Data Science Bootcamps.
More and more FinTech platforms like Bizinsure Insurance Fintech are using data analytics to understand consumer behavior and market trends as technology advances. It helps them improve their services and products and thereby better meet customer requirements
Fraud is a cause for concern in the banking industry, especially now that mobile banking is gaining traction. However, FinTech businesses can use big data and machine learning to build fraud detection systems that detect anomalies in real-time. They will detect illegal activities such as suspicious transactions, logins, and bot activities.
Businesses in the FinTech industry can use the power of big data to personalize chatbot customer service. AI chatbots will have access to raw data, allowing them to answer customer questions accurately and to the point.
Changing consumer preferences and the need to gain market share have led financial institutions to embrace multi-channel service delivery. To ensure that their customers have a satisfactory experience, financial businesses will use big data analytics to fine-tune their services across different platforms to meet the customer's needs. They will also use historical and real-time data to identify potential customer issues.
Thanks to big data, FinTech companies have access to real-time data that shows how users interact with their products, the average time spent on the portal/system/app and the most used features.
With such information, these businesses can evaluate the two versions of the product to see which one offers the better UI/UX design. In addition, they have an in-depth understanding of the differences between products and how they affect the customer experience.
Data science has enhanced financial services by speeding up processes that would otherwise take a long time. Below are a few examples through which organizations can gain financial benefits using data science.
Big data evaluate the level of customer satisfaction from survey results. For example, it helps financial institutions identify the rate and reasons for customer churn and helps them come up with newer ways to keep their audience interested in their services. It has also been used in managing product and feature requests, as well as analyzing customer support trends.
Customer likes and dislikes shift depending on need. Historical financial big data helps businesses explore evolving customer behavior and enables them to come up with invaluable products and services that streamline banking processes.
An excellent example is how the Oversea-Chinese Banking Corporation (OCBC) designed a successful event-based marketing strategy based on the large amount of historical customer data it collected.
To understand the changing financial industry FinTech firm uses big data. With access to past data, these companies can track purchasing behavior and predict future trends. As a result, they can use these facts to make critical decisions that enhance the customer experience.
There are many FinTech data science projects available to learn more about this exciting industry that would give you a clear idea of how things work in these elite firms. Below are a few examples that will help you to understand how data science and FinTech go hand in hand
Machine learning models can recognize unusual credit card transactions and fraud. The first and most important step involves collecting the raw data and then cleaning it (Removing unwanted characters and making it readable) one in all making it more structured and sorted, which is then used to train the model to predict the probability of fraud. Below are the steps involved in detecting fraud in credit card transactions:
Classifying whether credit card transactions are authentic or fraudulent using algorithms such as logistic regression, random forests, support vector machines (SVMs), and many more
Machine learning (ML) models are more effective than conventional fraud detection models. They can recognize thousands of patterns from large datasets. ML offers insight into how users behave by understanding their app usage, payments, and transaction methods
Using historical customer data such as travel history and spending on a card, a machine learning algorithm can predict which customers will likely use international cards in the upcoming months. This information will help companies to provide personalized offers to customers so that they can increase their revenue.
As a young and fast-growing industry, FinTech absorbs all the knowledge and approaches that give an additional boost to its products and digital ecosystems. Unlike mainstream banks, the architecture of digital banks is more flexible, allowing them to integrate with modern services and use the latest data mining techniques. Startups and mature companies require Data Science consulting services to help them organize processes and improve their products, so don't hesitate and jump into the Data Science stream and get data science FinTech jobs.
A FinTech data scientist is one who analyses historic data or finds out patterns in given data and then predicts the future based on certain conditions. The FinTech data scientist also possess strong domain knowledge and they have expertise in statistics as well.
Yes financial institutes use data science to know their customer better and to avoid fraud.
Yes. Coding is required to become data scientist in any domain.
Yes. Python is required to analyse the massive amount of data. It is easy to use and highly recommend to do Exploratory data analysis.
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