How does Big Data use in Modern Banks?
Let’s take a closer look at the tasks that Big Data can help within modern banks
, as well as how it can help with cybersecurity and client loyalty.
Handling data before and now
A normal bank customer – let’s call him Spencer – strolled into a branch in his city fifty years ago and was greeted by a cashier. Because he had offered services to Spencer for many years, the cashier knew who he was dealing with. He knew where Spencer worked and what his financial demands were, and he knew how to treat him properly.
For a long time, there was a model like this. Customers who had personal interaction with bank staff earned and retained their trust in the bank.
Spencer may now work for a multinational corporation with operations across the globe. It’s probable that he’ll spend two years in London, then a year in Berlin, another two years in Dubai, and then another two years in Singapore.
If the old scheme had been in place until now, it would have been completely incompatible with today’s reality. Spencer’s financial circumstances would be unknown to any bank employee, and no one would know how to address his present financial needs.
We live in a world where many businesses, including modern banking, use a new customer service approach to handle problems. In banking, data science enables continuous analysis and storage of all data from both traditional and digital sources, resulting in an electronic trail for each client. Here, Big Data technology
comes to the rescue.
What is Big Data?
Big Data is a term used to describe an ever-increasing volume of structured and unstructured data in multiple formats that all relate to the same context. Volume, velocity, variety, value, and authenticity are the major characteristics of this technology.
Such large data sets from multiple sources are beyond the capabilities of our current information processing technologies. Major global corporations, on the other hand, are already utilizing Big Data to address non-standard business difficulties.
According to Reuters, the Financial Stability Board released a study in 2019 underlining the importance of diligent oversight of how businesses use Big Data. Microsoft, Amazon, eBay, Baidu, Apple, Facebook, and Tencent, for example, all have massive datasets that provide them a competitive advantage. Some of these companies currently provide financial services to their customers in addition to their primary operations, such as asset management, payments, and loans.
The importance of Big Data for banks
As a result of the availability of essential data, non-banking enterprises might enter the financial institution sector. What about the use of Big Data in FinTech
by banks themselves?
has created a list of the most important banking trends
for the next ten years. One of the most crucial topics, according to experts, is the growing use of user data. After all, it is first-class performance if the bank can give the client the services and advice they require at the time.
Some banks have launched AI-powered apps that provide users with financial literacy, spending, saving, and investment advice based on their specific needs.
Huntington Bank, for example, launched the Heads Up app in 2019. Based on the dynamics of their expenditure, it offers warnings to clients about their ability to cover expected bills in the coming term. Users receive subscription payment notifications when their free trial period finishes and they are charged a membership price. Other alerts indicate erroneous withdrawals of funds from consumer accounts, such as when making a purchase at a store or restaurant.
These applications use Predictive Analytics to monitor transactions in real-time and identify consumer habits, providing them with valuable insights.
Why else is the role of Big Data increasing?
Customers today have a different perspective regarding banks than they did previously. Consider Spencer’s situation: he used to have to contact the bank’s physical location to resolve each of his difficulties, but now he can get a response to almost any inquiry online.
The function of bank branches is evolving. They may now concentrate on more important activities. Clients, on the other hand, use mobile apps, have 24/7 internet access to their accounts, and may do any action from their cellphones.
It’s also vital to note that in today’s world, people are more willing to reveal personal information. They leave evaluations, indicate their location, and create social media accounts. As a result of this level of risk tolerance and willingness to disclose personal data, a massive volume of data from diverse sources emerges. As a result, Big Data’s importance is growing.
How modern banks use Big Data
Modern banks can make inferences about their customers’ segmentation and the structure of their income and expenses, analyze their transaction channels, collect feedback based on their reviews, assess probable risks, and prevent fraud using the technologies outlined above.
Here are a few examples of how banks are utilizing Big Data and the benefits it provides.
- Analysis of clients’ incomes and expenditures
Banks have access to a variety of information on their customers’ earnings and expenses. This is data on their salary over a period of time, as well as the income that moved through their accounts. A financial institution can utilize this data to determine whether the customer’s wage has increased or decreased, which sources of revenue have been more steady, what the client’s expenditure was, and which channels the client used to do certain transactions.
Banks make educated decisions regarding the potential of credit extensions, analyze risks, and consider if the client is interested in advantages or investments based on the data they compare.
- Segmentation of the customer base
The bank separates its customers into numerous divisions based on specific indications after conducting an initial examination of their income-expenditure structure. This data aids in the future provision of appropriate services to clients. As a result, workers at the financial institution will be better able to promote ancillary products and entice customers with personalized offers. Furthermore, the bank can forecast customers’ predicted expenditures and revenues for the coming month and create specific strategies to ensure a net profit and optimize revenue.
- Risk assessment and fraud prevention
Knowing the typical trends of people’s financial behavior aids the bank in detecting problems. If a “wary investor” tries to withdraw the entire balance from their account, this could indicate that the card has been stolen and used by scammers. The bank will contact the customer to clarify the issue in this scenario.
Fraud is considerably reduced when different sorts of transactions are examined. Data Science in banking
, for example, can be used to analyze risk when trading stocks or assessing a loan applicant’s creditworthiness.
- Feedback management to increase customer loyalty
People nowadays provide comments on a financial institution’s work by phone or on the website, and they share their thoughts on social media. With the help of Data Science, experts examine these publicly available mentions. As a result, the bank will be able to reply to comments quickly and effectively. As a result, customer loyalty to the brand grows.
Today, Big Data analysis
provides new opportunities for bank growth. Financial firms that use this technology have a better understanding of their customers’ needs and are better able to make appropriate decisions. As a result, they can adapt to market demands more efficiently and quickly.