Have you ever considered how much data you generate each day? Every credit card transaction, every message you send, every website you visit... It all adds up to 2.5 quintillion bytes of data produced by the global population every day.
This creates an infinite number of opportunities for the most forward-thinking businesses across a variety of domains to capitalize on that data, and the banking industry is no exception.
While nearly half of the world's adult population uses digital banking, financial institutions have enough data to rethink how they operate in order to become more efficient, customer-centric, and, as a result, profitable.
Benefits of Big Data in Banking
The banking sector is the engine that powers economies, nations, and organizations. It also produces massive amounts of data every second. Every transaction leaves a trace and generates data that was previously thought to be static and only useful to auditors for the purposes of accounting and auditing.
However, as Big Data technologies in other areas such as Healthcare began to show their true potential, we began to incorporate such "worthless" and "stale" data into those systems and began to truly see the potential of financial insights that could be used for a variety of purposes.
As a result, Big Data in Banking has untapped potential, and we'll try to discover the implications and benefits of how it works, as well as the possibilities that could be explored
Back in 2008, Big Data and Business Intelligence technologies aided in this endeavor and enabled Banking and Financial Institutions to challenge the status quo, kicking off the emergence of Big Data in the Banking Sector.
Banks use Big Data and BI technologies such as Hadoop and RDBMS in all of their processes, changing the face of banking for the better. Big Data has helped shape organizations and institutions all over the world, from digitizing all banking processes to converting developing economies from cash-heavy transactions to digital transactions.
Some of the benefits of Big Data in the Banking Industry are as follows :-
Customers are given personalized banking solutions
Big data, when combined with effective tools and technologies, can provide banks with a better understanding of individual customers based on inputs received.
This includes their investment habits, shopping habits, investment motivation, and personal or financial backgrounds. For example, they can predict and prevent churn by having a complete customer profile and data. Find the best way to solve any existing issues.
Big data is used by the banking industry to get to know their customers. As a result, they create products, services, and other offerings based on existing customer profiles that are tailored to their specific needs.
Segmentation of Customers
Customer segmentation allows banks to better target their clients with the most appropriate marketing campaigns. These campaigns are then tailored to meet their needs in a more meaningful way.
Banks will gain valuable insights into user behavior by combining machine learning and artificial intelligence with big data. It also allows them to optimize their customer experience accordingly.
Furthermore, banks will be able to categorize their customers based on various parameters, such as preferred credit card expenditures or even net worth, by being able to track and trace every customer transaction.
Analyzing Customer Feedback Effectively
Through feedback, Big Data tools can provide banks with customer questions, comments, and concerns. This feedback assists them in responding in a timely manner. Customers will remain loyal to a company if they believe their banks value their feedback and communicate with them promptly.
Detection and Prevention of Fraud
One of the most difficult challenges facing the banking industry today is detecting fraud and preventing questionable transactions. Big Data in banking enables them to ensure that no unofficial transactions occur.
It will also ensure the overall safety and security of the banking industry. Furthermore, banks can use big data to prevent fraud and make customers feel more secure by monitoring customer spending patterns and identifying unusual behavior.
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Four Pillars of Big Data in Banking
Three V's can be used to describe big data flows. This includes variety, volume, and speed. Check out how the pillars of big data relate to banking.
Variety - This refers to the various data types processed. Every day, banks must deal with massive amounts of data of various types. Banks have troves of customer data ranging from transaction details to credit scores and risk assessment reports.
Volume- This is the amount of storage space required for the data. JPMorgan Chase, China Construction Bank Corporation, and BNP Paribas, among others, generate terabytes of data every day.
Velocity- This is the rate at which new data is added to the database. With the volumes that today's banks deal with, handling 1000+ transactions is not a pipe dream.
Value - These three V's are meaningless unless a company has the fourth one, Value. The value for banks corresponds to using big data analysis results in real time to make business decisions.
Based on these indicators, banks can devise strategies:
Banks have several use cases to demonstrate how data has been harnessed and used for intelligent analysis. This data creates new and exciting opportunities for customer service by improving TAT and delivering customized service offerings.
Uses of Big Data in Banking Industry
Some of the applications of Big Data in the Banking Sector include :-
Uses of Big Data in Banking Industry
Profiling of Customers
Big Data assists banking institutions in profiling customers, allowing them to cater to individual customers based on their banking history and transactional patterns over the time they have been with the bank.
This enables them to create tailored plans and solutions for their clients. This boosts customer experience and helps banks differentiate themselves and retain customers. Banks can also target different products to different customers based on their demographics.
Detection of Fraud
Banks can detect fraud even before it occurs by analyzing data and using statistical computing. With unique fraud detection Algorithms that track and compute spending and other behavioral patterns, it is possible to identify and gauge if a person is on the verge of financial ruin and may be enticed to defraud banking institutions.
Retail banks, investment banks, NBFCs, private equity firms, and others all have a dedicated Risk Management department that heavily relies on Big Data and Business Intelligence tools.
Decisions on Lending
Lending is one of the most important decisions in the banking industry. It is critical to choose the right customer who is both creditworthy and financially sound to pay off debt. Moreover, historically, banks relied on credit rating agencies to assess a customer's creditworthiness, which could not tell the entire story because it considered one rationale while ignoring others.
With access to new insights from big data analytics, banks can consider other factors such as customer spending habits, the nature and volume of transactions, and so on when deciding whether to lend to a customer. This has broadened the horizon for bankers and financial institutions by providing them with more data and knowledge.
Compliance with Regulations
Keeping records and complying with regulations becomes much more effective and efficient with Big Data Analytics and BI tools. They can effectively manage and track all regulatory procedures, from various taxes to keeping records with central banks.
With legacy systems, it was very time-consuming and labor-intensive to ensure that compliances were in place and dealt with appropriately; however, with BI tools, it becomes extremely simple because all of the information is concisely put together in a way never before possible, making it easier for decision-makers to comply.
Furthermore, when properly programmed, they can manage such compliances, reducing the risk of error and fraud caused by human intervention.
Cyber attacks and online financial frauds are extremely common, and embezzlement is a problem that even the best organizations in the world face. We've seen many large organizations, particularly banks, fall victim to cyber-attacks in which not only money but also customer information is stolen.
Banks can set up robust internal control systems with the help of Big Data and AI tools, as these activities can sometimes be performed by someone from within the organization, and they can track customer behavior with Advanced Algorithms.
In addition, in the event of financial terrorism, they can actively collaborate and share insights gained from their tools of Business Intelligence and Big Data Analytics with governmental agencies to mitigate such risks.
Challenges of Big Data in Banking
However, there are some impediments to big data implementation in banking. Specifically, some of the major big data challenges in banking are as follows:
Legacy systems are having difficulty keeping up
The banking industry has always been slow to innovate: 92 of the top 100 global banks still run their operations on IBM mainframes. It's no surprise that fintech adoption is so high. Traditional financial institutions have no chance against customer-centric and agile startups.
However, when it comes to big data, things get even worse: most legacy systems are incapable of handling the increasing workload. Attempting to collect, store, and analyze the required amounts of data using an outdated infrastructure can jeopardize the overall system's stability.
As a result, organizations must either increase their processing capacity or completely rebuild their systems to meet the challenge.
The more data there is, the greater the risk
Second, where there is data, there is risk (especially given the aforementioned legacy issue). It is obvious that banking providers must ensure that the user data they collect and process is secure at all times.
Furthermore, data security regulations are becoming more stringent. GDPR has imposed new restrictions on businesses around the world that want to collect and use user data. This should be considered as well.
Big data is becoming unmanageable
With so many different types of data and their combined volume, it's no surprise that businesses struggle to keep up. This becomes even clearer when attempting to separate the useful data from the useless.
While the proportion of potentially useful data is increasing, there is still an abundance of irrelevant data to sort through. This means that businesses must prepare and strengthen their methods for analyzing even more data, and, if possible, find a new application for data that has previously been deemed irrelevant.
Despite the challenges mentioned, the benefits of big data in banking easily outweigh any risks. Data is a valuable resource because of the insights it provides, the resources it frees up, and the money it saves.
Also Read | Big Data in Retail Sector
The use of Big Data in the banking industry is progressing dramatically. By collaborating with Big Data, banks will be able to provide more improved services in a timely manner while reducing operational costs. They will be able to realize the benefits of Big Data by implementing Big Data practices.