Big Data has been an increasingly popular topic of conversation in recent months. Financial institutions are also using this technology in today’s digital environment. Let’s take a deeper look at Big Data’s role in banking and how it protects customers from cyberattacks and boosts customer satisfaction.
What does the term “Big Data” mean?
Structured and unstructured data in different forms, often referred to as “Big Data” continues to rise at an exponential rate. Volume, speed, diversity, value, and truthfulness are some of this technology’s most important attributes.
Benefits of Big Data for the banking sector
As a result, non-financial firms may now join the financial services sector thanks to the availability of relevant information. Is there a role for Big Data in FinTech for banks, too? In the following decade, the banking industry will see several significant changes, according to a report published by American Banker. One of the essential topics, according to experts, is the growing importance of user data. Even while providing the customer with exactly what they want and need is first-class performance, the bank’s ability to do so is a significant plus.
Some banks have launched AI-powered applications of data science in banking that provide tailored financial advice based on users’ spending, saving, and investing preferences. Huntington Bank released the Heads Up app in 2019. It alerts customers to their ability to meet their financial obligations in the next period, depending on their current spending patterns.
When a free trial membership expires, consumers will get a message informing them their credit card will be charged. Erroneous payments from a customer’s account are also the subject of other alerts, such as those that appear while purchasing. These predictive analytics systems provide Real-time transaction monitoring and the identification of customer patterns.
Why is Big Data becoming more critical?
Today’s clients aren’t as trusting banks as they were in the past. Consider Spencer, who in the past had to go to the bank’s physical location to get a response to each of his questions; today, he can get an answer to almost every query he has about the bank online.
Changes in the function of bank branches have occurred. They may now devote their attention to more critical concerns. Mobile apps and ongoing internet access to their accounts allow clients to do any action they choose from their cellphones.
Furthermore, individuals are more open to sharing personal information in today’s environment. They post evaluations, identify their location, and establish social media profiles to share their experiences. Those prepared to take a risk and reveal their personal information generate a massive quantity of data from various sources. As a result, Big Data will play an increasingly important role.
How are banks using Data Science?
For example, banks may draw inferences about their customers’ segments and income and expenditure structure, analyze transaction routes, gather feedback based on customer evaluations, evaluate prospective risks and avoid fraud thanks to the technologies outlined above.
Analyses of the monetary transactions of clients
Customers’ income and spending data are readily available to banks. Their wages and pay for a certain period are included in this data set. For example, a financial institution might use this information to determine whether the customer’s wage has gone up or down, which sources of income have been more steady over time, how much money the client has spent, and which channels the client utilized to do particular transactions. Banks can make informed judgments about whether or not to provide credit, analyze the risks, and decide whether or not the customer is interested in benefits or investments by comparing the available data.
Customer base segmentation
Bank clients are divided into numerous groups based on several variables after assessing their income-expenditure pattern. You may use this information to provide good services to customers in the future. Because of this, workers of the bank will be able to market more goods and attract new consumers with personalized offers. The bank may also predict customers’ projected expenditures and revenues and comprehensive plans drawn up to assure the bank’s net profit and increase income.
Detection and avoidance of financial crimes
It is much easier for a bank to spot problems when it is familiar with customers’ typical patterns of financial activity. The card may have been stolen and used by fraudsters if a “cautious investor” attempts to withdraw their whole account balance. Calling the customer to explain things in this circumstance is expected.
Data Science may reduce fraud even more by looking at various transactions. Using Data Science in banking, for example, may be used to determine the risk of stock trading or assess a loan applicant’s creditworthiness. Compliance verification, auditing, and reporting may be handled more effectively with the support of Big Data analysis. This decreases the amount of time and money spent on administrative tasks.
Today, banks have new avenues for growth because of the advent of Big Data analysis. Customers’ demands are better understood, and judgments are made more accurately by financial organizations that use this technology. As a result, they are more equipped to meet the market’s expectations.
To know more about the technicalities and applications related to Data Science, you can enrol the industry based courses by The IoT Academy. This will help you in making a career leap with some amazing opportunities in the field of Data Science.
