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It is estimated that by the end of 2020, there were approximately 40 zettabytes (40 trillion gigabytes) of data in the world. If it feels a bit difficult to wrap your head around that number, don’t worry – you are not alone! It is a truly staggering amount of data and this figure underlines the importance of needing to find a way to make sense of it all so that it can be used productively.

The financial industry is particularly reliant on big data to function smoothly and, as such, it is important to find new ways of analyzing data more quickly and effectively. There is no shortage of data, but the question that is foremost on everyone’s minds is: How can we use this data in the financial industry?

It is important to note that the financial industry itself has undergone a vast number of changes in the past few years. Banking is a continually evolving concept, and the latest development in the banking scene is the introduction of smaller fintech companies that are attempting to make banking easier. That said, it is also important to note that smaller fintech companies often do not possess the same data-analyzing tools that larger, well-established banks have access to.

Data analysis has many advantages for both small and large financial institutions, including:

Greater Automation and Optimization of Processes

The introduction of artificial intelligence to the banking industry makes it possible to help customers more quickly and with fewer staff members. Chatbots can serve as first-line customer service staff, which allows more customers to be served with fewer employees.

Big data is the driving force behind AI products like chatbots, and they are becoming more and more clever as more and more data is being analyzed. Data analysis allows chatbots to better identify patterns and answer questions better.

Chatbots are a great example of one automated process but they are by no means the only process that can be automated by using big data. Banking is about risk management and this is where big data really comes into its own.

Effective data analysis can help banks and other financial institutions to identify risks more effectively and become responsible lenders. Data Science has become much better at identifying patterns, and this attribute can be applied to scan vast amounts of data with a view to identifying trends and other factors that come into play in the risk assessment process.

By automating this process, banks can make it easier for their customers to receive pre-approval for certain products, which can lead to more customers taking up offers and using services without placing additional strain on the workforce. While Data Science and automation don’t necessarily eliminate the need for human involvement in the decision-making process, they can place more vital information at the fingertips of decision-makers so that better decisions can be made.

Early Detection of Market Trends and Changes

Data Science and analytics enables the financial sector to identify changes in trends in the financial industry and react accordingly. This quick identification of new trends, combined with the faster reaction, can ultimately significantly improve any company’s bottom line and keep its customers happy by introducing the products that they need when they need them.

Artificial intelligence, combined with Data Science and analyzing, also enables financial institutions to introduce new products and services to markets when they are most receptive. In short, without effective data analysis, it would be difficult for companies to remain competitive in the fast-changing financial services sector.

Fraud Detection

Data analysis has another important role: It helps banks and authorities to detect fraud. More and more of us are living an online life, and this means that we leave traces of ourselves on the internet. These traces can be used by thieves in a variety of nasty ways and, therefore, we must do everything that we possibly can to reduce the risk of fraud.

Big data has an important role to play in this process because it can make it possible for a data scientist to identify trends and patterns where fraud is concerned. In some instances, big data can even lead authorities to the perpetrators of fraud.

Financial institutions and their clients are frequent victims of fraud, and for this reason, there is a large, vested interest in using big data to reduce the risk of doing business in the online world.

In Conclusion

Big data has become an important part of every industry, including the fintech industry, and it affects our daily lives in countless ways. When it comes to our finances, we must be even more proactive in our use of Data Science to both protect our interests and advance the financial service industry.