We have been hearing more and more lately about big data. The financial industry is currently making extensive use of this technology in the digital age. Let’s take a closer look at the functions that big data in banking carries out and how it supports client retention and online security.
Data Management: Present and Past
A teller greeted Spencer, a dependable bank customer, when he entered a branch in his city fifty years ago. The cashier was well known to Spencer because he had been a regular customer. He understood how to help because he was aware of Spencer’s job and the amount of money he required.
A similar model has been used before. Consumers who interacted with bank workers personally earned and kept their trust in banks.
Spencer might currently work for a global corporation with locations all over the world. He might stay in London for two years, Berlin for one, Dubai for two more, and Singapore for another two before moving on.
The prior strategy, if still in place at this moment, would have been totally inappropriate for the current circumstances.Spencer was unable to get urgent financial assistance from a bank staffer, who was also unaware of his financial situation.
Modern customer service methods assist a variety of industries, including the banking sector, in handling issues. By continuously evaluating and saving all of the information from both traditional and digital sources, data science in banking enables the establishment of an electronic client trail. Big Data technology can be useful here.
What is Big Data?
The term “big data” refers to an expanding body of data that is both structured and unstructured, and that can be accessed in a variety of formats. Volume, velocity, diversity, value, and authenticity are the main traits of this technology.
Such data sets from numerous sources are larger than what can be handled by our standard information processing methods. But, major multinational organizations are already using Big Data to solve unique business problems.
The Financial Stability Board, according to Reuters, published a study in 2019 that emphasized the significance of thorough oversight of how businesses use Big Data technologies.Large databases are clearly an advantage that the largest companies, including Microsoft, Amazon, eBay, Baidu, Apple, Facebook, and Tencent, enjoy over competitors. Some of these businesses currently provide financial services to their customers in addition to their primary business activities, such as loan and payment processing and asset management.
What is Big Data in banking?
Big data can be advantageous for all sectors. The term most frequently used comes from Gartner: According to the definition of big data, it refers to “high-volume, high-velocity, and/or high-variety information assets that call for new, cost-effective ways of information processing.” that enables superior insight, decision making, and process automation,” according to Wikipedia.
In banking, the term “big data” refers to a far broader phenomenon: the collection of all transactions for financial products that leave a digital footprint. We will talk about the fourth pillar, value, after we have further addressed how the unique qualities of Big Data—variety, volume, and velocity—work in the context of banking. Value converts incorporating data management and analysis procedures into a company’s operational results.
The importance of big data for banks
The availability of the required information has made it possible for non-banking enterprises to function in the financial institutions sector. What about the banks’ own use of big data in fintech?
American Banker has listed the top 10 banking sector trends for the next ten years. One of the most crucial fields, according to experts, is the growing importance of user data. First-class performance is demonstrated by the bank’s capacity to offer the client the support and services they require at that particular moment.
Some banks offer AI-powered apps that give customers individualized spending, saving, and investment guidance.
For instance, in 2019 Huntington Bank released the Heads Up app. Based on their spending habits, it informs consumers if they will be able to afford the anticipated expenses in the following term. After the free trial period ends and the user begins being charged for the subscription, they are notified. Other notifications, such those that appear when making a transaction at a restaurant or store, show that clients’ accounts have had money improperly withdrawn.
These applications watch transactions in real-time and identify consumer habits using predictive analytics, providing useful data.
Why is Big Data Having a Larger Role Than That?
Customers’ perceptions of banks have evolved throughout time. In our case, Spencer previously had to contact the bank’s physical location to handle each of his difficulties, but he can now get a response. to practically any question online.
The duties of bank branches are changing. Now they can focus on other important tasks. Clients may perform any operation from their smartphones thanks to mobile applications, constant internet access to their accounts, and other features.
People’s increased willingness to share their personal information is another significant element. They sign up for social media platforms, write reviews, and share their location. Such risk tolerance and desire to disclose personal information have led to the emergence of a great volume of information from various channels. This suggests that Big Data is having a greater impact.
Big Data usage in banking
The technologies mentioned above enable banks to draw conclusions about the segmentation of their customers and the structure of their income and expenses, analyze their transaction channels, receive feedback based on client feedback, spot potential dangers, and stop fraud.
These are a few examples of how banks use big data and the benefits it can provide.
- analysis of customer spending and income
Banks have access to a variety of information about the earnings and expenses of their customers.Their earnings during a specified period of time and the money that went into their accounts are covered by this information. This data can be examined by a financial institution to determine whether a client’s income has increased or decreased, whether revenue sources have been more dependable, how much was spent, and other relevant information. which channels the client used to complete particular transactions.
Banks can make decisions about lending, risk assessment, and whether a client is more interested in gaining advantages or making investments by looking at the data.
- the consumer base is segmented
Following a preliminary examination of the income-expenditure structure, the bank classifies its clients into a number of groups based on a number of variables. Future consumers will be able to receive the appropriate services thanks to these details. And as a result, the workers of the financial institution will be better prepared to promote supplementary goods and tempt clients with tailored offers. By determining the clients’ projected monthly income and expenses, the bank can develop a thorough plan to ensure net profit and maximize revenue.
- Risk evaluation and fraud protection
The bank can spot issues as they develop by knowing people’s usual financial activity habits. One sign that a card has been stolen and used by fraudsters, for instance, is if a “cautious investor” tries to withdraw the entire balance from their account. The bank will contact the customer to provide more details about the situation.
Analyzing various types of transactions also significantly reduces the likelihood of fraud. Whether trading stocks or determining a loan applicant’s creditworthiness, for example, data science in banking can be utilized to evaluate risks. With the use of big data analysis, banks can handle tasks like compliance verification, auditing, and reporting. Overhead costs are cut, and operations are simplified.
- Managing feedback to enhance client loyalty
Nowadays, customers can provide financial institutions feedback over the phone, online, or on social media. Experts analyze these publicly accessible mentions using data science. As a result, the bank is able to respond to comments promptly and effectively. This encourages people to be more loyal to brands.
These days, big data analysis opens up new prospects for bank expansion. Because of the usage of this technology, financial institutions are better equipped to comprehend client expectations and decision-making. As a result, they are able to quickly and successfully respond to market demands.