How data science can disrupt the fintech industry?

Data science has several applications in finance, and we anticipate greater financial solutions in the future

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Similar to the legendary Gold Rush of 1849, businesses are now putting their toes into the data mine in an attempt to extract some value from it. This massive amount of data is compelling the fintech and banking industries to harness the potential of the hidden gems that data analytics can provide. However, every profit-driven organization out there is sitting on piles of large data accumulated through years of running the two core enterprise processes - Core and Support operations. And the majority of these fintech firms use these techniques daily. Because there is so much innovation involved, and some of the procedures are cutting-edge in terms of agile business operations, it goes without saying that AI is a component of this agility.


Just increasing efficiency and productivity while improving customer experience has become a major focus for financial organizations all around the world. As data becomes the new fuel, AI enables financial institutions to enhance business decisions by processing massive, diverse, and complicated data sets. Banks and financial organizations create massive volumes of data in the form of consumer transactional and non-transactional data, which cannot be denied. According to reports, 2.5 quintillion bytes of data are created every day. Unsurprisingly, traditional data processing fails to manage such a massive number of data and gives false insights.

Realizing the potential of big data needs an analytical mindset as well as technology like big data analytics, AI, and machine learning. These aid in the transformation of data into useful knowledge, reducing the danger of making judgments based on intuition. This is when the job of a data scientist comes into play. This discovery has been handled by a data scientist since it needs them to know exactly what information to seek that will enhance cross-selling and customer happiness. A data scientist in the banking business should assist, construct client profiles, anticipate behaviors, and track trends, to mention a few.

The crucial role of data scientists


The banking business has reached new heights in recent years as a result of innovative approaches to growing client demands for personalization and convenience. Previously, banks and other financial organizations adopted a one-size-fits-all policy in which every customer was handled the same way regardless of their requirements and interests.

Customers no longer visit banks for every function, such as depositing money or checking account balances. Users now check their account balances, deposit checks, pay bills, and transfer money using their mobile phones. There was a time when it would take years to establish a framework that would assist banks in gaining a comprehensive image of their clients. Adopting big data analytics is becoming increasingly vital as online banking grows in popularity. As a result, the new and expanding job of data scientists has evolved. A data scientist contributes to the meaning of raw data and uses it to derive insights for better analysis. They assist banks in developing a 360-degree strategy for their clients by analyzing: consumer spending patterns; customer segmentation; risk management processes; customized product offers; and customer loyalty.

Furthermore, data scientists assist banks in planning, creating, and managing the complex data flows, tools, and solutions required to connect the bank's data systems to analytics environments. Furthermore, data scientists typically work with complex analysis tools like R, SPSS, Hadoop, and advanced statistical modeling. As a result, the process of obtaining relevant information from massive amounts of data is expedited, and information based on important metrics suggesting best practices is provided. It is correct to say that the introduction of analytics has benefitted the fintech industry.


Why Are Data Science And Finance Such Good Allies?

Some of the factors that contribute to the importance of data science in finance include:

  • Data processing operations are simplified, and expenses are reduced.
  • Supports organizational operational and process change.
  • Aids in the optimal optimization of omnichannel inventory management.
  • Customer experience, touchpoints, and the customer journey are all important.
  • Knowledge for making educated decisions.
  • Customers are correctly segmented.
  • Analyze the investment risks.
  • Allows for extensive competition monitoring.

The Way Forward

Many big data firms use prediction algorithms to comprehend and alter data sets, digest massive volumes of data, and aid in making better investment decisions. They reveal a more thorough grasp of patterns and how these exact facts may assist investors in staying ahead of the competition. Data science has several applications in finance, and we anticipate greater financial solutions in the future. To be relevant and profitable in this hyper-competitive economic climate, banks must embrace big data analytics. Experts such as data scientists will provide an advantage to these rising trends, bolstering the future of the fintech business. As data has become the new oil to fuel decision-making, this career has limitless benefits and a promising future for the data science domain. Frameworks that are unprepared for the big data revolution are one of the most significant difficulties confronting the current banking business. As a result, banks will need to connect their people, processes, and technology platforms to create highly personalized client experiences based on data insights.

The article has been written by Krishna Kumar, Founder and CEO, Learnbay