How Does Big Data Use in Modern Banks?
In today’s fast-paced, technology-driven world, the banking industry has undergone significant transformation, much of which is fueled by the utilization of Big Data. Modern banks are leveraging vast amounts of data generated by daily transactions, customer interactions, and digital activities to gain insights, make informed decisions, and improve their services. This article delves into how Big Data is being used in modern banks, exploring its impact on customer experience, risk management, operational efficiency, regulatory compliance, and innovation.
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?
American Banker 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 is the Role of Big Data Increasing?
Customers today view banks differently than they did in the past. Take Spencer’s situation as an example: he used to have to visit the bank in person to resolve his issues, but now he can get answers to almost any inquiry online.
The role of bank branches is evolving. They can now focus on more significant activities, while clients use mobile apps, have 24/7 online access to their accounts, and can perform any transaction from their smartphones.
It’s also important to note that in today’s world, people are more willing to share personal information. They leave reviews, share their locations, and create social media accounts. This increased risk tolerance and openness to sharing personal data result in a massive volume of information from diverse sources. Consequently, the importance of Big Data is growing.
How Modern Banks Use Big Data
Modern banks can draw insights about customer segmentation, analyze income and expense structures, assess transaction channels, gather feedback, evaluate potential risks, and prevent fraud using advanced technologies.
Here are some examples of how banks are leveraging Big Data and the benefits it provides:
1. Analysis of Clients’ Incomes and Expenditures
Banks have access to extensive information on their customers’ earnings and expenses, including salary data over time and income that flows through their accounts. Financial institutions can use this data to determine whether a customer’s salary has increased or decreased, identify stable sources of income, analyze spending patterns, and track transaction channels.
With this information, banks can make informed decisions regarding credit extensions, assess risks, and determine whether a client may be interested in benefits or investments.
2. Enhancing Customer Experience
One of the most significant applications of Big Data in modern banking is the enhancement of customer experience. Banks now have access to an unprecedented amount of information about their customers, including transaction histories, social media activity, online behavior, and more. By analyzing this data, banks can gain deep insights into customer preferences, needs, and behaviors, allowing them to provide personalized services and targeted marketing.
For example, Big Data enables banks to offer personalized financial advice, tailored product recommendations, and customized solutions that align with individual customer needs. This personalized approach not only improves customer satisfaction but also fosters loyalty, as customers feel more valued and understood by their banks.
Moreover, predictive analytics powered by Big Data allows banks to anticipate customer needs before they arise. For instance, if a customer’s transaction history shows a pattern of recurring payments or large purchases, the bank can proactively offer related services, such as loan options or savings plans, at the right moment. This level of personalization is made possible through the analysis of massive datasets that traditional banking methods could never achieve.
3. Risk Assessment and Fraud Prevention
Understanding typical financial behavior patterns helps banks identify potential issues. For example, if a “cautious investor” attempts to withdraw their entire account balance, it could signal that their card has been stolen and is being used fraudulently. In such cases, the bank would contact the customer to verify the situation.
Examining various types of transactions significantly reduces fraud. For instance, Data Science in banking can assess risks in stock trading or evaluate a loan applicant’s creditworthiness.
4. Customer Retention and Loyalty Programs
Big Data is instrumental in helping banks retain customers and build loyalty. By analyzing customer data, banks can identify key drivers of customer satisfaction and loyalty, such as service quality, pricing, and convenience. This information allows banks to tailor their offerings to meet customer expectations and reduce churn.
Loyalty programs are another area where Big Data is making a significant impact. Banks can use Big Data to analyze customer spending patterns and preferences, allowing them to design more effective loyalty programs that reward customers based on their behavior. For example, a bank might offer personalized rewards, such as cashback or discounts, on products and services that a particular customer frequently uses. This level of personalization not only enhances customer satisfaction but also encourages repeat business.
Moreover, Big Data enables banks to track the effectiveness of their loyalty programs in real-time. By monitoring customer engagement and redemption rates, banks can make adjustments to their programs to ensure they remain relevant and attractive to customers.
Challenges and Ethical Considerations
While the use of Big Data in modern banking offers numerous benefits, it also presents several challenges and ethical considerations. One of the primary concerns is data privacy. With the increasing amount of data being collected, banks must ensure that they handle customer information responsibly and in compliance with data protection regulations.
There is also the risk of bias in Big Data algorithms, which can lead to unfair treatment of certain customer groups. For example, if an algorithm is trained on biased data, it may inadvertently discriminate against customers based on factors such as race, gender, or socioeconomic status. Banks must take steps to ensure that their algorithms are fair and transparent.
Additionally, the sheer volume of data generated in the banking industry can be overwhelming. Banks need to invest in the right technology and expertise to manage and analyze this data effectively. This includes building robust data infrastructure, hiring skilled data scientists, and implementing strong data governance practices.
Finally, banks must be mindful of the potential for data breaches and cyberattacks. As banks increasingly rely on Big Data, they become more vulnerable to cyber threats. Ensuring the security of customer data is paramount, and banks must invest in advanced cybersecurity measures to protect their data assets.
The Future of Big Data in Banking
The use of Big Data in modern banking is still evolving, and its impact is expected to grow in the coming years. As technology continues to advance, banks will have access to even more sophisticated tools and techniques for analyzing data and gaining insights.
One of the key trends shaping the future of Big Data in banking is the integration of artificial intelligence and machine learning. These technologies will enable banks to automate more processes, make more accurate predictions, and deliver even more personalized services to customers.
Another emerging trend is the use of blockchain technology in conjunction with Big Data. Blockchain’s decentralized and transparent nature makes it an ideal solution for secure data sharing and transaction processing in the banking industry. By combining Big Data with blockchain, banks can enhance data security, reduce fraud, and improve transparency.
In addition, the rise of open banking will continue to drive innovation in the banking sector. As banks collaborate with fintech companies and other third-party providers, they will be able to leverage Big Data to offer new and innovative financial services that meet the evolving needs of customers.
Conclusion
Big Data is undeniably transforming the banking industry, offering new opportunities for enhancing customer experience, improving risk management, increasing operational efficiency, and driving innovation. However, banks must also address the challenges and ethical considerations associated with Big Data to ensure that they use this powerful tool responsibly and effectively.
As the banking industry continues to evolve, Big Data will play an increasingly important role in shaping the future of finance. By embracing Big Data and leveraging its full potential, banks can not only stay competitive in a rapidly changing landscape but also deliver greater value to their customers and stakeholders.