Financial institutions are discovering new business opportunities based on the huge flow of data that they manage.
Big data is the collection of massive volumes and types of information, which grows exponentially over time. One example in the financial market is the hundreds of billions of banking transactions that are made every day, when clients use ATMs, open accounts, take out mortgages, make mobile payments, and so on.
Initially, big data was used as a tool to analyze data correlations: making it possible to identify relationships, even amidst the chaos. The next step drew on these correlations to identify problems through data mining – which is the aspect of automated learning that makes it easier to identify patterns in universes of information. Finally, all of these data variables were applied to understanding users and developing products that can satisfy their needs.
As digital platforms have flourished, the number of data points that are produced and consumed has grown even more – and managing it all poses commercial and technological challenges.
In the case of the financial industry, the use of big data is also shaping up to be an opportunity to developed more personalized, digital attention capabilities and to improve operational efficiency. Keep in mind that it is not just about ‘static’ data, but about all types of records, including behaviors, preferences and trends, that make the information even more valuable.
Big data opens a very promising door to launch efficient new business models in finance, focused on targeted niches of people who have common needs.
It is not surprising that 90% of business leaders cite data collection and analysis as one of the key resources for businesses, according to a study by BSA | The Software Alliance.
With this in mind, here are the big data trends in the financial industry for 2021.
Data as a service (DaaS)
One trend for big data in financial services that has grown in 2021 is that of data as a service (DaaS), which allows the industry to be much more competitive and better understand the customer experience.
DaaS allows stakeholders to delve deeper into the most important data for each area, thanks to the fact that these are simplified and accessible. This way, identifying trends or consumption patterns becomes much easier.
Having access to a vast and varied pool of data that is aggregated into coherent formats is useful when it comes to processing and delivering new applications or reports, even those of a regulatory nature.
The main advantage of big data coupled with a service approach is that it offers agility, especially when it comes to implementing changes (such as digital transformation), as well as transparency, order and simplicity.
Data democratization is also taking center stage this year. Having access to a great amount of information for everyone is an essential part of open finance.
Open finance has led to ambitious goals and joint projects with financial technology companies to make better use of the data held by banks.
This new focus on data democratization invites stakeholders of the financial ecosystem to be much more agile and creative to create and offer personalized products and services, even in real time.
In this environment, banking services alone won’t be enough: rather, offering other services, through the use of data, will be the key to succeed in the market. To make this work, financial institutions will need strategies to best analyze and use the data, as well as technology providers who will make the experience as enriching and personalized as possible.
Personalization in banking
The most recent trend in banking regarding big data is personalization of the experience. Along with data management and analysis, technology and process automation, the financial institutions are offering personalized products and services according to each consumer’s needs and goals.
In a study by the marketing company Epsilon, 80% of respondents stated that they would be more likely to buy a product or purchase a service from a brand that offers personalized experiences.
The convenience of personalized experiences is also incentivizing users to be more flexible with their own data. They are happy to share their information, as long as they know it will have a positive influence on their interaction with the product or service.
One example in the industry is the use of financial incentives, such as discounts or offers that are tailored to customer needs, to promote online registrations. This practice is a powerful motivator for users to provide their data.
Data personalization has also gained relevance with the new players that are disrupting various industries, such as Netflix or Spotify, to name a few. Financial entities have not been left behind and are also offering more attractive products, which is resulting in their customers being more willing to use them.
In sum, big data allows banks to make the most of their customers’ history and data to offer products and services in real time, when and where they need it, considering not only their current situation, but also their future needs.