The Role of Big Data in Banking
- Kang siswa
- 2022 December 05T12:31
- Big Data
Big Data has been a topic that we hear about more and more lately. This technology is currently being extensively used in the financial sector in the digital age. Let's examine the jobs handled by big data in banking and how it protects cyber security and fosters client loyalty in more detail.
Handling Data Now And In The Past
A regular bank client, Spencer, entered a branch in his city fifty years ago and was greeted by a cashier. The cashier was familiar with Spencer because he had been his customer for a long time. He was aware of Spencer's employment location and his money requirements, so he knew how to assist him.
Such a model has been around for a while. Customers' faith in banks was earned and preserved by those who interacted with bank staff on a personal level.
Spencer might now be employed by a multinational corporation with locations around the world. It's feasible that he'll spend two years in London, one in Berlin, two more in Dubai, and then another two years in Singapore before moving on.
If the old plan had been in place up until this point, it would have proven to be completely unsuited to the realities of the present. No bank employee would be knowledgeable of Spencer's financial situation or be able to help him with his immediate financial demands.
We live in a world where new customer service models help various industries, including the banking industry, handle problems. Data science in banking enables the creation of an electronic client trail by continuously analyzing and storing all information from both traditional and digital sources. Big Data technology can help in this situation.
What is Big Data?
Big Data describes an expanding body of information that is both structured and unstructured and is available in a variety of formats. These technology's primary characteristics are volume, velocity, diversity, value, and authenticity.
Such data sets from diverse sources are larger than what can be handled by our standard information processing tools. Major global corporations are, nevertheless, already utilizing Big Data to address novel business difficulties.
Reuters reports that the Financial Stability Board published a study in 2019 emphasizing the necessity for careful oversight of how businesses use the Big Data technology. The major businesses, such as Microsoft, Amazon, eBay, Baidu, Apple, Facebook, and Tencent, have huge databases that undoubtedly provide them an advantage over rivals. Some of these businesses already provide their clients with financial services including asset management, payments, and lending activities in addition to their primary business operations.
What is Big Data in banking?
All industries can benefit from big data. The definition that is used the most is from Gartner: Big data, according to Wikipedia, is defined as "high-volume, high-velocity, and/or high-variety information assets that need cost-effective, creative forms of information processing that offer better insight, decision making, and process automation."
The phrase "Big Data in banking" refers to a much larger phenomenon: the collection of all financial products market activities that leave a digital trace. The fourth pillar, value, which turns data analysis and management operations into a company's business results, will be discussed once we have further discussed how the specific characteristics of Big Data—variety, volume, and velocity—work in the instance of banking.
Big Data's significance for banks
The availability of the required data allows non-banking enterprises to enter the financial institutions sector. What about the use of Big Data by the banks themselves in FinTech?
The major trends in the banking industry for the upcoming ten years have been compiled by American Banker. One of the most crucial areas, according to experts, is the growing importance of user data. After all, it is first-class performance if the bank is able to offer the client the services and guidance they require at that precise moment.
Some banks release AI-powered apps that allow customers to receive guidance on budgeting, spending, saving, and investing based on their unique needs.
For instance, Huntington Bank introduced the Heads Up app in 2019. Based on the patterns of their spending, it notifies customers if they will be able to cover the anticipated expenses in the following period. Users receive notices when their free trial period expires and they start being paid for the subscription. Other notifications, such as when making a purchase at a restaurant or store, indicate incorrect withdrawals of money from customers' accounts.
These programs use predictive analytics to track transactions in real-time and pinpoint consumer tendencies, giving them insightful data.
Why Else is Big Data Playing a Bigger Role?
The way that clients view banks has changed throughout time. In our example, Spencer previously needed to contact the bank's physical branch to resolve each of his problems, but today he can find a response to almost any query online.
Bank branches' responsibilities are evolving. They can now concentrate on other crucial activities. Customers use mobile applications, always have internet access to their accounts, and are able to carry out any task from their cellphones.
The fact that people are more open to sharing personal information in the current environment is also crucial. They register on social networks, post reviews, and indicate their location. The emergence of a vast amount of information from multiple channels is the outcome of such risk tolerance and desire to reveal personal information. This indicates that Big Data is playing a bigger role.
How banks use Big Data
Banks can make inferences about the segmentation of their clients and the structure of their income and expenses, comprehend their transaction channels, gather feedback based on their reviews, identify potential risks, and prevent fraud thanks to the technologies outlined above.
Here are a few illustrations of how banks employ big data and the advantages it offers them.
- Analysis of clients’ incomes and expenditures
Banks have extensive information about the earnings and expenses of their customers. This information relates to their earnings over a specific time frame and the money that entered their accounts. This data can be analyzed by a financial institution to determine whether a client's salary has increased or decreased, which sources of revenue have been more consistent, how much money was spent, and which channels the client utilized to conduct certain transactions.
Banks can assess risks, decide whether to extend loans, and determine whether a client is more interested in receiving benefits or making investments by analyzing the data.
- Segmentation of the customer base
Following a preliminary review of the income-expenditure structure, the bank groups its clientele based on a number of different variables. Future clientele can be served with the appropriate services thanks to this information. And as a result, the workers of the financial institution will be better able to market ancillary goods and draw clients in with the use of customized offers. A detailed plan can be created by the bank to guarantee net profit and maximize revenue by estimating the clients' anticipated monthly income and expenses.
- Risk assessment and fraud prevention
Knowing people's typical financial activity patterns enables the bank to recognize problems as they arise. For instance, it may be a sign that the card has been stolen and used by fraudsters if a "cautious investor" tries to withdraw the entire balance from their account. The bank will contact the client to clarify the circumstances in this scenario.
The chance of fraud is also greatly decreased by analyzing different kinds of transactions. Data science in banking, for instance, can be used to evaluate risks when trading stocks or determining a loan applicant's creditworthiness. Banks may manage operations that call for compliance verification, auditing, and reporting with the aid of big data analysis. Operations are made simpler, and overhead expenses are decreased.
- Feedback management to increase customer loyalty
People can now leave a financial institution feedback over the phone, on their website, or through social media. Data Science is used by experts to examine these publicly available mentions. The bank can therefore reply to comments in a timely and appropriate manner. This in turn fosters greater brand loyalty among consumers.
Big Data analysis nowadays creates new opportunities for bank growth. The use of this technology by financial institutions improves their comprehension of consumer needs and decision-making. They are able to swiftly and effectively adjust to market demands as a result.
