Financial technology, commonly referred to as “data science in fintech,” is one of the fastest-growing areas of technological innovation and is popular with venture capitalists.
FinTech refers to a collection of technologies centred on the development of new consumer banking and financial service delivery methods.
When you use PayPal, Google Pay, or your credit card to pay for an online purchase, you, the consumer, the e-commerce company, and the bank all use FinTech to complete the transaction.
FinTech has evolved over time to disrupt nearly every aspect of financial services, including payments, investments, consumer finance, insurance, securities settlement, and cryptocurrencies, among others.
FinTech companies heavily rely on fintech data science to streamline financial decision-making and deliver innovative solutions. So let’s begin by defining data science and analysing its significance.
What is data science?
Data science is a field of study that combines industry expertise, programming skills, and knowledge of mathematics and statistics to extract insightful information from data. to accomplish tasks normally requiring human intelligence Data scientists apply machine learning algorithms to data types such as numbers, text, images, video, and audio to develop artificial intelligence (AI) systems. These systems, in turn, generate insights that analysts and business users can translate into tangible business value. If you want to learn more about data science in FinTech, look into the data science course fee.
Why is data science so significant?
Until data is transformed into useful information, it serves no purpose. Two of the most important tasks in data science are the extraction of actionable insights from large datasets containing both structured and unstructured data and the discovery of hidden patterns. The significance and utility of data science derive from its plethora of applications, ranging from simple tasks like asking Siri or Alexa for recommendations to more complex applications like controlling autonomous vehicles or robots.
In order to facilitate enterprise-wide decision making, organisations can also measure, monitor, and record performance metrics. Businesses can analyse trends and make crucial decisions in order to retain customers, boost performance, and increase profits. Models based on data science can predict future actions based on historical data. This facilitates the discovery of optimal business outcomes for organisations. By combining existing data with other data points to generate actionable insights, data science assists organisations in identifying and refining target audiences. Data science also assists recruiters in combining data points to identify the most qualified candidate for a company’s needs.
Role of Big Data in FinTech
We now live in an unprecedented era due to the vast amount of data surrounding us, the vast majority of which is brand new. Here are some statistics to support my argument:
There are 9 million emails sent every second, 95 hours of video uploaded to YouTube every minute, 500 million tweets every day, 700 billion minutes spent on Facebook every month, 40,000 Google searches per second, which is equal to 1.2 trillion searches per year, 9 million products ordered on Amazon every second, and 3 exabytes of data sent or received by mobile devices every month, which is expected to grow to 30.5 exabytes per month by 2020.
If you were curious, 1 exabyte is equal to 1 billion gigabytes. In order to put this into perspective, it is estimated that the entire printed book collection contains 10 terabytes of data.
Recent estimates place the size, which includes audio, video, and digital content, between 3 and 20 petabytes.
So, one exabyte could hold 100,000 times the printed material, or 500 to 3,000 times the entire contents of printed books, and we generate 3.3 exabytes of data per month!
Even though it may be disparate information, the best FinTech companies are all attempting to capture, quantify, and derive insights from the abundance of data that surrounds us today—some may even call it “noise.”
Even more exciting from our perspective is the fact that savvy entrepreneurs are attempting to create entirely new information services businesses out of data using FinTech data science, which they can capture, manage, and extract insights from. Take a look at what FiscalNote, DataFox, and ThinkNum, among others, are doing. (I’m willing to bet that their products will blow a lot of people’s minds.) There are many factors driving the “Big Data” (or “information services”) opportunity within FinTech, but I will focus on just two.
First, there has been a sudden explosion of newly accessible data sets as nearly all information, including financial transactions, moves online. Second, technological advancements now make it possible for machines (i.e., computer code) to replicate and increasingly replace a substantial portion of the work traditionally performed by human analysts (both the most demanding and well-paid analysts).
I believe there is a tremendous opportunity to create value for companies that can automate the collection of relevant data (using tools such as natural language processing) and deliver actionable insights drawn from data using data science, artificial intelligence, and advanced visualisation capabilities. Due to the complexity of this job, companies pay FinTech data scientists a high salary.
There is a growing emphasis on big data and the opportunities it conceals in the financial industry. As a result of its technological foundation, Big Data presents both profit-generating opportunities and challenges, particularly for the FinTech industry, and we are only at the beginning of what can generate such vast quantities of consumer and transactional data.
Big Data is empowering innovators to advance hundreds of digital, technology, and internet industries, thereby creating new profit sources, improving customer experiences, and developing life-changing technologies. Big Data has helped the FinTech industry get off the ground, and as it grows, we will face new challenges and opportunities.
What Data Science Contributes to FinTech
Data science is gaining popularity among FinTech companies because it enables them to solve a variety of business problems swiftly. The following are the most prevalent use cases in the FinTech industry:
Data science is also used in other ways in the FinTech sector, such as real-time analytics, consumer analytics, algorithmic trading, robo-advisors, and financial planning.
In the FinTech industry, these use cases create a lot of jobs for data scientists. If you want to switch your career to data science, you should look into the best data science bootcamps.
How Might Big Data in FinTech Impact the Customer Experience?
As technology advances, an increasing number of FinTech platforms use data analytics to comprehend consumer behaviour and market trends. It enables them to enhance their services and products and, as a result, meet customer demands more effectively.
Improvements to security
In the banking industry, fraud is a cause for concern, especially as mobile banking gains popularity. Nevertheless, FinTech companies can utilise big data and machine learning to create fraud detection systems that detect anomalies in real time. They will identify illegal activities, including suspicious transactions, logins, and bot activities.
With chatbots, assistance can be personalised.
The power of big data can be tapped by FinTech companies to personalise chatbot customer service. Chatbots powered by AI will have access to unprocessed data, enabling them to provide precise and pertinent responses to customer inquiries.
Ensuring a seamless multichannel experience
Changing consumer preferences and the need to increase market share have prompted financial institutions to adopt multichannel service delivery. Financial institutions will use big data analytics to fine-tune their services across multiple platforms to meet the needs of their customers. They will also utilise historical and real-time data to identify potential customer problems.
A/B testing-based enhancements to UI/UX
Thanks to big data, FinTech companies have access to real-time information regarding how users interact with their products, the average time spent on the portal, system, or app, and the most popular features.
With this information, businesses can compare the UI/UX design of the two product variants to determine which version offers a superior user experience. They also know a lot about the differences between products and how those differences affect the customer experience.
What are the advantages of data science for financial institutions?
The application of data science has improved financial services by accelerating lengthy processes. Listed below are a few examples of how data science can provide organisations with financial benefits.
- Analyze the outcomes of the customer satisfaction survey
Using survey results, big data evaluates customer satisfaction. It aids financial institutions, for instance, in determining the rate and causes of customer churn and in conceiving of novel methods to keep their audience interested in their services. It has also been used to manage product and feature requests and analyse customer support trends.
- Evaluate customer behaviour and develop new products
Customer preferences and dislikes change depending on the situation. Big financial data from the past enables businesses to investigate changing customer behaviour and develop products and services that streamline banking operations.
An excellent example is how Oversea-Chinese Banking Corporation (OCBC) developed a successful event-based marketing strategy by analysing a large quantity of historical customer data.
3. Forecasting future market trends
Big data is utilised by FinTech companies to comprehend the evolving financial industry. With access to historical data, these companies are able to track purchasing behaviour and forecast future trends. As a result, they can use these facts to make crucial decisions that enhance the customer experience.
Many FinTech data science projects are available for those interested in learning more about this dynamic industry and gaining a thorough understanding of how these elite firms operate. Several illustrations are provided below to illustrate how data science and FinTech are complementary.
4. Credit card fraud detection
Unusual credit card transactions and fraud can be identified by machine-learning models. The first and most important step is to collect the raw data and then clean it (remove unwanted characters and make it readable) to make it more structured and sorted. This data is then used to train the model to predict the probability of fraud. Detecting fraudulent credit card transactions involves the following steps:
using different algorithms, like logistic regression, random forests, support vector machines (SVMs), and others, to figure out if credit card transactions are real or fake.
5. Profiling credit cards lets people guess whether the people who use them are cardholders or fraudsters.
using outlier detection methods to find transactions that are very different from normal credit card transactions (called “outliers”) in order to find credit card fraud.
The effectiveness of machine learning (ML) models exceeds that of conventional fraud detection models. They are capable of recognising thousands of patterns within large datasets. By analysing app usage, payments, and transaction methods, ML provides insight into user behaviour.
6. Predicting which customers will use international credit cards within the next few months
Using historical customer data such as travel history and card spending, a machine learning algorithm can predict which customers are likely to utilise international cards in the upcoming months. This information will assist businesses in providing personalised offers to customers in order to increase revenue.
Conclusion
As a young and rapidly expanding industry, FinTech absorbs all the knowledge and approaches that provide a boost to its products and digital ecosystems. In contrast to conventional banks, the architecture of digital banks is more adaptable, allowing them to integrate modern services and employ the most recent data mining techniques. Startups and established businesses require Data Science consulting services to help them organise processes and enhance products, so don’t hesitate to enter the Data Science stream and acquire Data Science FinTech jobs.
