How Data Science is Changing in Fintech: Roles, Examples, and Use Cases

FinTech, or financial technology, is a rapidly expanding field of technical innovation that has gained significant traction among venture capitalists. A group of technologies known as “fintech” are centred on novel approaches to offering consumers banking and financial services. FinTech is used by you, the customer, the e-commerce business, and the bank to complete online payments made with PayPal, Google Pay, or credit cards. Almost every facet of financial services, including payments, investments, consumer finance, insurance, securities settlement, cryptocurrencies, and more, have been disrupted by fintech over time.

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What is Data Science?

This is a fast expanding subject that uses sophisticated computational and statistical techniques to draw conclusions and information from data. To analyse big, complicated datasets, it integrates a variety of methods and resources from disciplines including computer science, mathematics, and statistics.

Basically, data science is about identifying patterns and connections in data and then applying that knowledge to forecast or optimise results. This may entail anything from figuring out what kind of customers they are to spotting fraudulent activity or forecasting market movements.

Data scientists employ a range of methods, including predictive modelling, data mining, and machine learning, to do this. In order to handle and analyse massive datasets, frequently in real-time, they also depend on specialised software tools and computer languages.

Use Cases of Data Science in Fintech

Fraud detection and prevention

Financial institutions place a high priority on fraud detection, which is why they are always searching for solutions to automate risk management and prevent fraud. Scams come in many forms and aim to mimic, steal, or execute money laundering operations. Prevention, protection, and reporting methods are necessary for an anti-fraud technology to be effective. Real-time results are produced by a data warehouse by feeding it into models with real-time data that is received from payment processing systems. In addition to defining fraud collaboration patterns and producing interactive charts and diagrams, data science is used in FinTech organisation scanning.

Customer Behaviour Analysis 

Predictive analytics, consumer behaviour modelling, and real-time user segmentation are made possible by deep learning about customer performance. With the help of BI tools, you can see how your users are making use of the digital banking ecosystem. FinTech companies can use user financial behaviour statistics to inform their product strategy. Customer lifetime value (CLV) is another indicator that data scientists can offer FinTechs. This represents an estimate of all the advantages that a business can have from a relationship with a consumer.

Risk Analysis

If a user appears to be trustworthy, extra services, higher cash credits, and lower rates may be made available to them with the aid of a risk modelling system. Models based on product consumption and open-source data from several sources can be constructed by data scientists.

Process Improvement

The application of the digital twin approach, a recent invention in product development, can serve as the foundation for process improvement. To model changes and evaluate their potential effects in the future, financial institutions or digital banks might track and examine metrics related to offline operations and customer support processes.

Product Improvement

Product usage study and market data can serve as the foundation for product improvement efforts. Customer’s functional behaviour changes and their reactions to changes in FinTech goods can be modelled and predicted by data scientists.

Personalized Marketing

Financial firms are able to provide tailored advertisements and products for every user segment because of thorough analysis of trends and client preferences. Personalised marketing efforts enhance user experience, increase client retention, and assist reach the target demographic.
Additionally, personalisation raises return on investment and enhances conversion rate, which boosts the organization’s financial performance.

Examples of Fintech Data-Driven Projects

The businesses listed below are a few instances of how fintech Data Science has been successfully incorporated into their operations.

DBS and personalized recommendations

DBS Bank has been making significant investments lately in data-driven technologies and artificial intelligence. The bank offers consumers tailored advice on financial decision-making based on its unique insights into their behaviour. In addition to assisting the bank in identifying anomalous transactions, these technologies also raise security.

Scotiabank and receipt management

Utilising machine learning for banking data analytics, namely for receipt management, is another intriguing application. Scotiabank has integrated Sensibill’s mobile banking technology, enabling customers to snap pictures of their bills. Purchase documentation from clients are easily accessed and stored by the data science programme. Users now have a useful method for organising both digital and paper receipts.

Curve and protection from fraud risks

Another example is the Curve firm, which increased its fraud security for their credit card aggregation app by implementing a Sift Science Machine Learning solution. By analysing user behaviour, the platform can immediately detect and stop fraudulent use by highlighting suspicious activities and signals.

Australian bank and automatic customer interactions

Automating consumer interactions is another application for data science. For instance, a bank lender with headquarters in Australia partnered with the Demyst  Data technology to expedite client verification and processing. By automating client contacts, the data-driven technology reduces manual verification by 40%.

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Role of Big Data in FinTech

We now live in an unprecedented period due to the abundance of data around us, the majority of it is new. Here are some stats to demonstrate my point.

• 9 million emails are exchanged every second
• 95 hours of video are posted to YouTube every minute
• 500 million tweets are published daily
• 700 billion minutes are spent on Facebook every month
• 40,000 Google searches are conducted every second, or 1.2 trillion searches annually.
• 9 million things are ordered on Amazon every second, and mobile devices send and receive 3 exabytes of data per month (which is predicted to rise to 30.5 exabytes by 2020).

For the record, one exabyte is equivalent to one billion gigabytes. To put this into perspective, 10 terabytes of data are thought to be included in all printed publications, or the contents of all books ever produced. According to recent estimates, the size ranges from 3 to 20 petabytes and includes digital, audio, and video resources. Hence, one exabyte may store 500–3,000 times the whole contents of printed books, or 100,000 times the printed material, and each month we generate 3.3 exabytes of data!

The most successful FinTech businesses are all attempting to collect, quantify, and derive meaning from the vast amount of data that is already available to us, even though some may even refer to it as “noise” or different types of information.

What’s even more intriguing, in our opinion, are some extremely astute entrepreneurs who are attempting to use FinTech data science to acquire, manage, and extract insights from data in order to create completely new information services firms. Take a look at the work being done by FiscalNote, DataFox, and ThinkNum, among others. (I predict that many people’s minds will be blown by their items.)

The “Big Data” (or “information services”) possibility within FinTech is driven by a number of variables, but allow me to concentrate on just two. The first is that there has been a massive explosion in the availability of new data sets, with nearly all information—including financial transactions—moving online. Second, a great deal of the labour that was previously done by human analysts—both the most difficult and highly compensated analysts—can now be replicated by machines, or computer code.

Big data and the potential it holds are becoming more and more important in the financial sector. Because of its technological roots, big data presents both opportunities and obstacles for profit, particularly for the FinTech industry. Furthermore, the potential for generating such vast amounts of consumer and transactional data is still untapped. Big Data is enabling entrepreneurs to progress hundreds of digital, technological, and internet-related industries, generating new revenue streams, improved customer experiences, and life-changing new innovations. Big Data in FinTech has aided in the industry’s incubation, and as it grows, we will encounter new opportunities and difficulties.

How Can Big Data in FinTech Influence the Customer Experience?

An increasing number of FinTech sites, such as Bizinsure Insurance As technology develops, fintech companies use data analytics to comprehend industry patterns and consumer behaviour. It assists them in enhancing their offerings, which helps them better satisfy consumer needs.

Security improvements

The banking sector is concerned about fraud, particularly in light of the growing popularity of mobile banking. FinTech companies may, however, create fraud detection systems that instantly identify anomalies by utilising big data and machine learning. They are going to pick up on illicit activity like suspicious transactions, logins, and bot activity.

Ensuring a frictionless multi-channel experience

Financial institutions are adopting multi-channel service delivery due to shifting consumer tastes and the desire to increase their market share. Financial companies will employ big data analytics to fine-tune their services across various platforms to fulfil the needs of their clients and guarantee a positive customer experience. Additionally, they will make use of both past and current data to spot any client problems.

Personalization of help with chatbots

Companies in the FinTech sector can leverage big data’s capacity to customise chatbot client support. With access to raw data, AI chatbots will be able to provide precise and concise answers to consumer inquiries.

Better UI/UX based on A/B testing

FinTech organisations can now obtain real-time data about how consumers engage with their products, the average amount of time spent on the portal, system, or app, and the most frequently used features, all thanks to big data.

With this data, these companies may compare the two product iterations to determine whether one has a better UI/UX design. Furthermore, they possess a comprehensive comprehension of the distinctions across products and their impact on the customer experience.

Conclusion

FinTech, an emerging sector with rapid growth, assimilates all available information and methodologies to enhance its digital ecosystems and products. Digital banks, in contrast to traditional banks, have more flexible design that enables them to adopt the newest data mining techniques and interact with contemporary services. Don’t hesitate to enter the data science field and apply for data scientist FinTech jobs. Both startups and established businesses need data science consulting services to help them streamline operations and enhance their products.

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FAQs

Q1. Is data science used in finance?
Ans: Indeed, financial institutions employ data science to prevent fraud and get to know their customers better.

Q2. What is the definition of a FinTech data scientist?
Ans: A FinTech data scientist is a person who examines previous data, identifies trends in the data, and then projects the future under specific assumptions. The FinTech data scientists are highly skilled in statistics and have solid domain knowledge.

Q3. Does FinTech involve coding?
Ans: Indeed. To become a data scientist in any field, coding is a prerequisite.

Q4. Does FinTech require Python?
Ans: Indeed. Python is needed in order to analyse the vast quantity of data. Exploratory data analysis is a highly recommended task that is simple to use.