Financial institutions are among the largest customers of Big Data solutions, and their operation has changed considerably with the adoption of modern digital technologies. The development of cloud computing services, the increased availability of high-performance data centers, and the appearance of global networking technologies with high data transfer rates shaped the new look of all fields of the financial industry. In this article, we listed the modern implementations and advantages of Big Data in Fintech. If you prefer to learn more, there is a comprehensive review that covers many additional aspects of Big Data and Its business impacts.
Fintech companies obtain multiple benefits from using Big Data solutions, and those benefits are especially illustrative in comparison with some “old-school” companies that offer financial services and rely on human skills instead of digital technologies. It is evident that traditional approaches are insufficient, and that human resources of an enterprise cannot provide the adequate efficiency of handling large amounts of information and, for some cases, require converting data from an analog to a digital form and vice versa, which results in a significant loss of productivity.
The total amount of information in the Fintech industry keeps growing every day, as well as the resulting data flows. As a result, there is a constant need for more powerful, effective, and scalable solutions to handle such volumes of information. In this case, handling involves collecting data from different sources, processing it for further storage in an organized form, maintaining large databases by removing duplicates and synchronizing entries, performing data analysis with specified parameters, and outputting analysis results in some visual form. To ensure the smooth flow of these processes, they require the cooperation of Big Data, Machine Learning, and Artificial Intelligence solutions.
Now, let’s look at the most popular use scenarios involving Big Data software in Fintech.
- Risk management and development planning
Using a large bulk of information as source data for strategic planning, prediction, and modeling provides more precise results and allows covering more possible situations. In their turn, more accurate predictions help in creating more effective and comprehensive business plans that take into account all possible risks and lead to better profits. Nowadays, business development planning cannot rely solely on the expertise and intuition of business analytics and advisors. To be ahead of competitors, companies should invest in automated analytic solutions that use Big Data, ML, and AI technologies to ensure better forecasting.
- Market research and investment planning
Thorough research, analysis, and planning discussed in the previous paragraph in relation to business development are also highly important for developing investment strategies or making quick decisions regarding daily trades. These tasks can be effectively performed by Big Data solutions customized for specific needs and enhanced with machine learning models and analysis algorithms.
- Fraud prevention and credit history checking
The ability to keep and retrieve credit history records gives banking institutions a powerful weapon to prevent frauds and minimize losses due to clients’ inability or unwillingness to return loans. Specialized databases contain information on action sequences or general patterns that are peculiar to fraudulent schemes, and this information is constantly updated as new schemes are discovered and analyzed. Moreover, the ability to perform fast and accurate solvency analysis and credit background checks leads to better customer experience since the waiting time is reduced.
- Customer research and offer personalization
The information obtained from the clientele of financial institutions is an invaluable source of marketing insights. It is analyzed in order to divide the target audience into several categories, find the behavioral patterns of customers in every category and the personal preferences of each client. Thus, it is used to build promotion strategies and create personalized offers that take into account the individual needs and habits of every customer in particular.
The implementation range of Big Data software grows even bigger with the development of new technologies, such as more effective machine learning models, enhanced AI algorithms, faster computing units with a better performance-per-Watt ratio, and other hardware and software innovations and improvements. Moreover, the total amount of data flows and accumulated information is constantly growing and, thus, demands more powerful solutions to handle such volumes with the ability of further scaling according to future needs.
Overall, Big Data software has become a necessary took rather than redundant luxury, especially in the banking and financial industry that is characterized by high risks and a competitive environment. However, to achieve maximum effectiveness, the corresponding solution must ensure the smooth cooperation of AI, ML, and Big data technologies. This way, the large-scale information analysis can be performed in an automatic manner but with custom parameters to achieve the relevant results.
As you can see, the use of Big Data is invaluable in business as a whole and in Fintech in particular. The ability to handle more source information results in better predictions, accurate analysis, detailed planning, reliable risk management, and other significant benefits.
