Dealing with data is one of the main issues company leaders face. Nearly nine out of ten finance experts think that data has the power to alter corporate practices in general. It is believed that the top 10 business goals for approximately 52% of corporate leaders are Big Data and analysis. All of this shows that business is approaching a time when data is becoming the main driving factor following the upheaval of the last several decades.
As the financial sector continues to evolve, fintech development companies are playing an increasingly important role in driving innovation and digital transformation. By leveraging big data analytics, fintech development companies can gain insights into consumer behavior, market trends, and risk management, which helps them to develop more effective products and services. With the ability to process vast amounts of data in real-time, fintech development companies are able to provide customized solutions to meet the specific needs of their clients. As such, big data analytics has become an essential tool for any fintech development company looking to remain competitive in today’s fast-paced financial landscape.
As financial organizations continue to be filled with Big Data – voluminous, high-speed, and diverse information assets – the question arises of how to extract information from them so that it is of value to the business. AI for finance should be considered not only as a means to comprehend the accumulated information, but also as a discipline whose work depends on the availability of large amounts of data.
To take advantage of the consequences of data processing, businesses will have to solve several tasks. What does the era of data dominance bring with it for business leaders and, in particular, for financiers? What benefits will new strategies for data analysis bring? And what is the role of management accounting specialists here?
Users are constantly generating data: when they make purchases, plot routes on maps, run search queries, go for a run with a smartwatch, post on social networks, or order food online. From car manufacturers to social services, this is how companies obtain data.
Most often, Big Data refers to terabytes, petabytes, and even zettabytes of information. Almost any impersonal information about users belongs to Big Data.
The term Big Data itself appeared only a few years ago, but the first studies on the isolation of data from other data have been conducted for a long time. The mathematical theory of identifying specific data on a fuzzy data field and clustering information has been described for 70-80 years, but the technical capabilities for conducting such analysis have only appeared relatively recently.
In the financial sector, Big Data is used in several ways:
1. One of the performance indicators of banks is the number of financial products sold per client.
How can your bank increase its financial product offerings? You need to make the right offer, a service that is relevant at the very moment it is needed and will be in demand. For example, if a person is going on vacation, and the bank says to him: “Maybe you need a card for travelers?” Or the client’s wife asks to make repairs, and the financial organization itself tells him that there is a favorable loan offer just for these purposes. How did banks find out that a person needs these services now?
Big Data came to the rescue. From analyzing user behavior, companies move on to compile a list of banking products used by people similar to this client. And now, they can prepare personalized recommendations.
Big Data analysis helps to create in-demand products and offer services where they are needed, such as opening bank branches in retail outlets where bank cards are actively used. After all, this means that this is where the largest number of their consumers are concentrated.
Now financial institutions take into consideration not only conventional data: the customer’s sociodemographic details, credit profile, the discipline of repayment of previous loans, and salary level. Individuals also analyze data on purchases.
Banks also analyze the behavior of users on social networks. Conclusions about a person’s social status, education, and qualifications can be inferred after analyzing posts. In the evaluation of legal entities that apply for a loan, banks analyze, in addition to financial indicators, the frequency of mentions of the company in the media and the tone of materials.
Anti-fraud systems analyze a large number of parameters (i.e., the country of operation, the amount of payment, and its repeatability) to identify potential fraudsters. Big Data helps to create a profile of the average payer. Based on this information, the level of the potential danger of conducting a fraudulent operation is assigned.
Robots are advancing into the financial services industry. Robo-advisors provide customers with affordable, real-time, and individualized financial portfolio guidance. Today, the sole use of algorithms in Big Data analytics is being utilized to passively handle portfolios without human involvement.
Various sources, such as employee records, communications, business apps, and more, are used to gather financial data. Big Data fusion and reconciliation need data integration instruments that streamline the accessibility and storing procedures.
Combined, Big Data and cloud solutions may solve these difficult business issues. The adoption of cloud technologies by more financial service organizations will serve as a stronger signal to the financial industry that Big Data alternatives are useful not just for IT scenarios but also for business apps.
For example, users who do not leave digital traces are suspicious. Even a newly registered email can become a “wake-up call” for the security system. If access to the mobile bank is provided through accounts in social networks, the system detects fake users.
Banks and fintech companies analyze not only their data flow but also begin to exchange information about users among themselves. And in the future, companies will actively share data to see a more complete picture of customer behavior.
Data-driven strategies enable people and corporations to closely monitor historical trends to forecast the future. Big Data can help with this, but there is still a lot to be done, because few businesses have adopted a fully data-driven business model.