Data Science implementation in FinTech

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Innovative fintech product with Python-Django and data science technologies

A hectic life pace has made people comfortable doing various things on the run. Traveling, buying, negotiating – the growing digital generation is used to proceeding with an impressive number of tasks at once. While refusing to waste even a second of time, millennials prefer to perform all their day-to-day operations swiftly and efficiently, particularly when it comes to financial transactions and payments. Being influenced by the customers' needs for mobility and innovations, the financial industry increasingly involves tech solutions into almost all aspects of financial services. Consequently, the demand for financial technologies or Fintech is continuously growing. According to the current situation, the companies that managed to enter the fintech market with efficient software products have reached the top of the fintech revolution.

So, what does a successful fintech product consist of? There are no doubts that fintech applications should be reliable and fully-featured to run financial, investment, and mortgage transactions in a safe and secure manner. Also, they have to earn the trust of the users to stay on the market. As we can see, it is thrilling and challenging to provide IT solutions for the fintech industry. Thus, developing a fintech software product is not as easy as pie. Still, the flourishing fintech companies can prove that without the risk, there is no reward. So let's figure out which strategy your business should follow to keep up with financial tendencies, and how the right technology stack and data science practices may come in handy to make your company the next fintech guru.

Keeping up with fintech tendencies

In order to keep on track in the fintech industry, the company should offer unique and agile products that are flexible enough to meet the coming market challenges, and provide secure data processing. So, let's go deeper into details.

Numerous startups reach the fintech market every day, but not all of them can stay there for the long term. When entering the market, the newly emerged companies may meet the industry giants, who are difficult to compete with. Therefore, fintech newcomers have to enter the market with the products based on the proven technologies in order to react quickly to changing demands of the consumers and adopt the product according to market tendencies at a rapid pace. Also, the uniqueness of the product can be a superpower of a company, which helps to succeed in specific market niches. After entering the fintech market your software systems and applications will be tested by the users for flexibility and reliability. Efficient financial software development services will help your brand earn the trust of the customers, if they can exclude downtimes and assure the security of financial transactions. To meet these requirements, the fintech companies need trustworthy software technologies to give strong support to each financial venture.

Python plus Django – the lucky combo for fintech

Python was first released in 1991 and has been continuously gaining its popularity for a number of reasons. Let's determine some of them, especially the ones proving that Python is a lucky finding for the fintech industry. First of all, the users who admire Python and the so-called Pythonistas point out the agility of this programming language. It is convenient to learn with a number of cost-free and open-source programming libraries such as Pandas, SciPy, Numpy, Scikit-learn, etc. At the same time, swift processing of a vast amount of data can help your team quickly embed new features into reliable software products. Apart from this, Python code can be ported to many platforms – from Linux to PlayStation. Also, being upgraded by the generations of developers, Python technologies prove the high-level of security against web-based attacks.

Django, as a Python web framework, follows the principle of "keep it short and simple," which results in providing an easy and understandable coding process. Furthermore, Django is the right choice for the promising fintech projects as it allows the companies to become early market entrants with MVP (minimum viable product). Simultaneously, this framework suits the projects at any scale and capacity. Also, many things come easy with Django, such as REST API development and integration with other financial systems and platforms. Moreover, for over 13 years of existence, Django has been provided with numerous essentials, so its packages allow working with cutting-edge technologies such as data analysis, AI, and machine learning.

Data science insights for your business

The best techniques of data science embedded into the financial industry can push fintech projects to the new level. Fintech companies can benefit from Big Data solutions, get insights from the practices of Artificial Intelligence (AI) and predictive analytics. Let's overview some examples when data science comes in handy for fintech investment.

Robo-advisors and customer segmentation

Nowadays, data science technologies help in making rational financial planning and wise investment decisions, based on algorithms. Such tasks can proceed without or with a minimum human intervention as the Robo-Advisors are coming to the rescue. These financial advisors are digital platforms that aggregate and analyze the prominent financial data of the clients, such as financial status, risk capacity, and financial goals. Also, financial institutions and banking establishments can benefit from data science by aggregating the users' data, which helps them to create specialized clients' profiles and make customers' segmentation. It results in better planning of products or services that will be in demand by customers in the future.

Risk monitoring and fraud detection

Machine learning and AI practices bring significant enhancement to the financial industry by involving the data for building models that improve risk management and provide innovative solutions to detect secured lending deals and unsecured borrowers. Also, data science technologies and machine learning practices are used to protect financial transactions against possible fraudulent actions.

All in all, digital solutions based on Python and Django technologies, together with workable data science practices, will help you make a decisive impact on the fintech industry with a reliable software product.