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5 Applications of Data Science in FinTech: The Tech Behind the Booming FinTech Industry

Data Science has played a significant role in transforming the finance and banking industry by completely changing the ways in which they previously operated. Life has been made easier for the banking officials as well as the customers. FinTech:  a new term coined for the innovation and technology methods aiming to transform traditional methods of finance with data science forming one of its integral components.

Whenever you use your credit card, Amazon Pay, PayPal, or PayTm to make an online payment, the commerce company/seller and your bank, both utilize FinTech to make a successful transaction. With time FinTech has changed almost and every aspect of financial services, which includes investments, insurance, payments, cryptocurrencies, and much more. Fintech companies are heavily dependent on the insights offered by machine learning, artificial intelligence, and predictive analytics to function properly.

In this article, we will look at the contributions of data science in FinTech.


What is Data Science in FinTech?

Data science is a knowledge area that prepares data for analysis and delivers insights using advanced analytical tools. It cleans, structures, and manipulates large volumes of data to derive actionable insights. 

Data science helps organizations to use analytics at every level of the business. It is not a single subject or concept but a combination of different fields such as statistics, mathematics, data analysis, quantitative finance, algorithms, and visualization. It uses a combination of tools to understand big data easily and use the insights in real-time. 

The FinTech industry uses data science to get a deeper insight into customer behavior. This helps financial institutions create products and services that will align with the market trends and increase returns for the business. There are several roles of data science in the FinTech industry. The most important ones are as follows: 

Analyzing Customer Behavior 

Data mining, natural language processing (NLP), and text analysis are used to understand customer behavior. The FinTech industry also depends on customers to become successful, just like every other sector. Analyzing customer behavior gives FinTech companies numerous benefits such as-

  • Customer segmentation 
  • Increase cross-sales for additional products 
  • Measure customer lifetime value 
  • Reduce customers with below-zero value 
  • Increase brand value as a trusted financial partner 

Data science allows organizations to develop customer behavior models and run predictive analytics in real-time.

Predictive Analytics

Predictive analytics is a part of advanced analytics where future trends are predicted using historical and real-time data. Statistical modeling and machine learning algorithms are used on data collected and processed through data mining. Data science is used in FinTech to: 

  • Predict stock market trends 
  • Prices of financial products 
  • Alternate investment options, etc. 

Data science also helps with algorithmic trading, where issues related to pricing, trading volume, and timing are managed to increase the efficiency of the trading platform. We can observe this trend in the crypto market. 

R&D 

Research and development are an integral part of every industry. Data science is used to improve product development strategies so that the establishment can make the most of the changing market conditions and customer requirements. Data science and artificial intelligence are used together to achieve the goals. 

  • Identify the right time to launch a new product/service 
  • Introduce new financial products 
  • Create a comprehensive strategy to market the new releases 
  • Optimize the use of resources in R&D and increase the success rate 

Understanding the weaknesses in the company’s existing products/ services will help the management make changes to overcome the weak points and strengthen its presence in the industry. 


Advantages of Data Science in FinTech

  • Make better and faster decisions using accurate analytics 
  • Increase employee productivity 
  • Become a competitive company in the market 
  • Determine the right time to enter new markets 
  • Be ready to grab new opportunities 
  • Be prepared to deal with unexpected losses
  • Create a proactive work culture 

Applications of Data Science in FinTech

1. Credit Risk Scoring

With an aim to make “credit accessible to more number of people”, FinTech companies use robust machine learning algorithms to predict the creditworthiness of people. This lets them reach a wider customer base and reduce the rate of credit defaults. Traditionally, banks use very complex statistical methods to determine the credit score of an individual, but with the help of data science, the good and bad borrowers can be separated in a fraction of seconds.

In order to accomplish this task, a large number of data points are utilized by the companies. Also, all the data that is collected is further used to train the machine and improve its performance.  Therefore, data science provides a holistic view of one’s creditworthiness.

Companies like Alibaba’s Aliloan are an automated online system that provides small loans to entrepreneurs who otherwise would have been rejected by the banks because they have no collateral against which the loans could be given. This automated system collects information such as online transactions, business performance, ratings from the customers, and much more to calculate the creditworthiness of the business owner.

2. Fraud Detection & Prevention

Fraud detection and prevention have always been a top priority for FinTech companies. At present, it is estimated that financial institutions lose about $80 million every year due to fraudulent activities. With the evolution of data science, the ways to detect fraudulent activities have also changed. Machine learning-based algorithms are able to detect fraudulent activities better than the traditional systems that may sometimes even produce false positives and classify a normal transaction as a fraud as well.

The advanced fraud detection systems work on supervised and unsupervised machine learning (ML) algorithms. Supervised ML-based systems are fed with historical data that has been labelled as fraudulent and non-fraudulent. This data set helps the system to classify any ongoing transaction as normal or anomalous.

On the other hand, the unsupervised ML-based systems are just fed with a large amount of data that has not been previously classified, the system uses this data as a training set and learns to differentiate between standard and a fraudulent activity on the basis of transactions happening in digital space every day.

3. Customer Retention & Marketing

Fintech companies collect a large amount of data from their customers which is often used by them for financial analysis. This information can likewise be utilized for enhancing the client base and expanding their lifetime value. Customer data right from their transactions, social media engagement, and personal information can be taken into consideration and used to offer them a better experience.

For instance, by analyzing the previous products purchased by the customers’ algorithms can be created to predict their future choices. These bits of knowledge can also be utilized to comprehend what sort of items must be promoted among various age groups. FinTech companies may utilize client information to make thorough profiles of their clients and offer them a customized program for a superior ordeal.

4. Revenue & Debt Collection

One of the biggest challenges faced by FinTech institutions is to be able to collect revenue in a timely and transparent manner. Predictive analysis and machine learning algorithms can be used to profile customers and use the insights to create optimal revenue collection strategies. Right from credit application scoring to calling indebted individuals to pay the obligation, machine learning proves to be useful.

The calculations enable a monetary foundation to monitor their clients’ activities and subsequently discover the right time to make them a manual call for reimbursing the obligation. These techniques not just allow the foundations to spare a considerable measure of time yet, in addition, keep them from wasting their monetary assets.

5. Portfolio Optimization & Asset Management

Asset management and portfolio optimization (taking investment decisions) are significant elements of Fintech Institutions. The massive amount of big data collected by these firms can be used to construct machine learning-based asset management models. This idea has additionally offered to ascend to what we call Robo-advisors, which are fundamentally robots helping the organizations to take their asset allocation decisions. This mitigates monetary dangers as well as enhances the degree of profitability for the companies.


Final Thoughts

Data Science offers endless advantages to the fast-evolving FinTech institutions. Money and its management have always been a concern for everyone, no business ever wants to face loss. Therefore, it is a prerequisite that the market is analysed and understood in an efficient manner. The firms that will be able to gain these insights will definitely outperform the others in their respective fields.

Data Science & Analytics

At DataToBiz the Artificial Intelligence engineers have the skill to convert your business problem into a form that can be resolved by data science. After a thorough understanding of the issue that needs to be administered, the experts can devise algorithms to help you overcome the business & finance problems. They have an aptitude for machine learning and artificial intelligence that allows them to create optimal products as per your requirements.

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