The use of Big Data has grown to become an important part of most industries, including the financial industry. This paper will look at how Big Data is used throughout the financial industry to achieve various organizational goals. One goal this paper will examine is how financial institutions have been utilizing Big Data to gain a bigger market share through better customer service. In today’s global economy, customer retention is important to every organization.
With more customers, there is inherently more risk. A financial institution must examine and balance what types of risks it will be taking on. One of the main types of risk for financial institutions is in borrowing money from customers in the form of a loan. Depending on the type and size of the loan, the financial institution could be taking on considerable risk.
Another key business goal that will be examined in this paper will be how financial institutions have been using Big Data to detect and even prevent fraudulent activity. Being able to quickly and accurately detect fraud can help to reduce the overall risk to any financial institution. However, before examining how Big Data is put to work in financial institutions around the world, it is helpful to have a fundamental understanding of Big Data.
Overview of Big Data
The idea of Big Data is not a new concept and has been around for years. There are many facets of Big Data and how it can be used in modern business. As the name implies, Big Data is generally thought of as large amounts of information. Although Big Data is usually made up of large amounts of information, one key characteristic is its ability to take data from multiple different sources and perform search queries across all of it. It is essential that big data can work with data sets that are not traditionally easy to query (Minelli, Chambers, & Dhiraj, 2013). Another key characteristic of Big Data is its ability to help make the data more meaningful through veracity (Tang & Karim, 2019). One way this can be accomplished is by having enough data to help detect the risk factors of fraud.
Veracity is one of the four V’s that help to define Big Data. The four V’s are represented by volume, velocity, variety, and veracity (Tang & Karim, 2019). Volume represents the large amount of data that Big Data can handle. Velocity is used to discuss how Big Data can process information in real-time. Big Data can also handle information from different data types, hence the term variety. Finally, veracity refers to the quality and value that can be created from Big Data (Fan et al., 2021).
These data sets can be generated from a large number of sources such as online blog posts, social media sites, mobile applications, and cloud storage (Minelli, Chambers, & Dhiraj, 2013). It is estimated that over two exabytes of data are generated every day (Jamar et al., 2017). Furthermore, with the growth of computer hardware, users no longer need to decide on what data to keep and what data to get rid of, so people are saving more data than ever before (Minelli, Chambers, & Dhiraj, 2013). With such rapid growth of data on so many different platforms, it is easy to understand the need to have a Big Data solution capable of dealing with non-traditional data sets.
Another main factor of Big Data is that it must be usable (Minelli, Chambers, & Dhiraj, 2013). Gathering and collecting large amounts of information is meaningless if the data does not help solve a problem. Depending on the data being collected and analyzed, it could help solve several different issues within a wide range of industries.
There are several areas that Big Data can be applied in the financial industry. Some of the areas where Big Data can help are; examining governance issues, making marketing decisions, customer analytics, and even helping to identify and detect fraud (Jamar et al., 2017). With a greater amount of data, companies can have more power to determine how to make their originations more profitable and therefore more successful overall.
The new generation of banking that utilizes Big Data for critical decisions is often referred to as Banking 4.0 (Mehdiabadi et al., 2020). The concept of banking 4.0 stems from the idea of industry 4.0 and how organizations are adapting to business in the new era of information technology. Industry 4.0 is thought of as the next big industrial revolution that involves “cyber-physical systems” (Mehdiabadi et al., 2020), which can be considered as bringing technology into the same world of physical processes such as manufacturing, supply chains, and banking (Mehdiabadi et al., 2020).
Industry 4.0 consists of a wide range of new and innovative technologies. These new technologies of industry 4.0 include the Internet of Things (IoT), Artificial Inelegance (AI), and Big Data (Tam, 2020). Not all technological advances in industry 4.0 are utilized, but each and every organization. An organization needs to determine what type of technology it can leverage to build a better and stronger company. Financial institutions are starting to take advantage of AI and big data systems to build better customer relationships and retain business.
A key aspect of customer retention is keeping systems easy to use for the end-user. Ease-of-use is key for young and old customers alike. The younger population wants an easy way to perform financial tasks such as transferring funds and paying bills while on the go. Older individuals who might not be as comfortable with a technology need an easy-to-use interface so they can perform their needed financial tasks. Both of these different groups can be accommodated online or E-banking.
E-banking has become an expected standard for financial institutions to offer their customers. As customers of financial institutions have grown accustomed to having ease of use through E-banking, there has been an increase in E-commerce in general and an overall increase in online shopping. Unfortunately, this increase in E-banking and E-commerce has led to online fraud of credit cards (Dudin et al., 2018), which will be examined later in this paper.
Big Data can be useful in understanding what customers are looking for and what their future needs might be. One way of building better customer relations for the financial institution is to be able to provide more loans to their customers. Although building customer relationships are important for any organization, lending money can be a risky business.
The fundamental idea behind risk management is to determine and reduce the overall risk to the organization. Big Data analysis can be used to help analyze key aspects of risk through the process of data mining (Md Rashid & Iqbal, 2017). A common data mining practice is to look through big data two determine an individual’s credit score. The credit score is an overall gauge of how reliable the individual is in paying back loans.
The idea of using Big Data to help determine what action(s) an organization should consider taking to help reduce their overall risk (Tang & Karim, 2019). The more information a company has to help make decisions, the better those decisions could turn out to be. This is true in many industries, but especially in finance.
An example of risk management in the financial sector would be conducting informational background checks on individuals requesting large loans. If the financial institution is asked to provide a loan to help an organization startup for the first time, there could be a great amount of risk. The financial institution will most likely do a significant amount of data analysis on the individual(s) requestions the loan for the newly formed company.
Some loans can be risky for banks depending on the customer’s repayment history. Big Data analysis can be used to search through disparate information and better determine if a customer’s loan should be approved. Larger financial institutions have been using Big Data for years to help reduce risk by first determining the likelihood that the individual will pay back the loan (Hassani et al., 2020).
The growth of technology has allowed smaller financial institutions to take advantage of Big Data analysis to be more competitive. By using Big Data analytics over a more traditional loan approval approach, smaller banks can approve more loans with a higher likelihood of being paid back and retaining more of their customer base (Mehdiabadi et al., 2020). In addition, knowing an individual’s credit score can inform the financial institution of their risk when giving out a loan to a specific customer.
Fraud can happen in many different businesses, but the financial industry is the number one target for attackers (Dudin et al., 2018). The driving factor of cyber-attacks against financial institutions is to gain access and obtain money from a person’s account. Depending on the type of attack, the malicious actor might go after a single individual’s account in a targeted attack against that person or go after any number of accounts just to make a financial gain.
In the case of a targeted attack, the attacker is singling out an individual for a specific reason. There are many reasons for a targeted attack of this kind, such as political motivation, the possibility of large financial gain, or a personal vendetta against a specific individual. Whatever the motivation for the attack is, the attacker’s goal is to target only one person at a time.
In contrast to a targeted attack against an individual, it is common for attackers to target an organization such as an online retailer that accepts credit cards from multiple individuals. In this attack, the goal of the attacker is to gather as many credit cards as possible. The gathered credit cards are then either used by the attacker or sold on the dark web.
Regardless of the attack and motivation, it is up to the financial institutions to determine when fraudulent activity has occurred on one or more accounts. Big data can be used to help determine when fraud has taken place by correlating the spending patterns of individuals and looking for anomalies. And such normal detection is utilizing hey credit card in different geographic locations where it would be impossible for the individual to be in both places during the transaction.
For example, one transaction at noon took place in New York City, and another transaction took place at 1:00 in California. It would be physically impossible for an individual to be in both cities within one hour. This type of anomaly detection works well for card-present transactions. In a card-present transaction, the individual needs to present the physical card to be swiped at a payment terminal. In the example above, it would be impossible for the individual to physically travel across the country in one hour to make both card-present transactions.
However, with the ever-evolving nature of online purchases, more non-card-present transactions are now happening in recent years than card-present transactions. With a non-card present transaction, the credit card it’s accepted via the Internet through online purchasing portals. Accepting a credit card virtually makes it extremely plausible that a transaction could be made in New York at noon and the purchase in California at 1:00 by the same individual. Detecting anomalies for online credit card fraud it’s much more difficult.
Electronic payments are quick and convenient for the end-user to use on a regular basis. Since electronic payments are used so often, they have become a primary target of attacks. Attackers have found that it is a better return on investment to attack a single organization and steal thousands of credit cards at the same time. These credit cards can then be sold on the dark web and used by other malicious actors around the world. By stealing so many credit cards from a central location and then dispersing them via the Internet two other criminals, it is difficult for financial institutions to define the original breach.
It is a common practice for credit card thieves to hold onto large batches of credit card information for several months before selling them. By holding onto the credit card information before a sale, it becomes more difficult for the financial institutions to determine the point of origin of the original compromise. Determining the original breach location needs to be established so that the credit card customers can be notified that their card(s) have been compromised. Finding the original breach is also important to helping to determine the root cause of the breach to help advance the fight against credit card fraud.
Big Data can help to detect various types of fraud through different types of data analysis (Tang & Karim, 2019). For example, Big Data can be used to track an individual’s spending habits and locate abnormal transactions. As discussed above, this type of analysis is great for card-present transactions but has also been adapted for non-card present transactions. Big Data is key since the information needed comes from a multitude of different sources, and it would be quite difficult to normalize that much information. The velocity aspect of Big Data is critical to the success of fraud detection since the information needs to be processed and acted on as quickly as possible (Tang & Karim, 2019). Without fast action, it would be more difficult to prevent fraud.
As discussed in the previous section, there are multiple attacks that an attacker can use to steal consumers’ financial information, such as credit card details. Dealing with and trying to prevent attacks against financial information systems is a war being waged on several different fronts at the same time. One technique for fighting fraud is to prevent it before it happens.
In the last several years, payment card manufactures have begun to include Near Field Communication (NFC) technology within many banking and credit cards (Dudin et al., 2018). The NFC technology-enabled cards can use a one-time transaction code that is unique for each transaction (Studevent, 2019). The transaction code only contains details about that single transaction. By only having the transaction details and not all the card details, it is more difficult for an attacker to duplicate the card based on a single transaction code.
Chip-enabled cards have been around for many years, but it was not until 2015 that merchants in the United States (US) were required to accept them (Studevent, 2019). All merchants are required to accept NFC-enabled and chip-based cards, or they will need to pay a higher insurance premium.
The majority of merchants across the US now accept chip-enabled and NFC-enabled credit cards at their locations. These technologies can go a long way in preventing credit card fraud for card-present transactions. However, NFC-based cards are not considered a foolproof way to defend against an attack and need to be accompanied by other mitigation techniques (Dudin et al., 2018).
Some of the other techniques used are to monitor a user’s purchasing habits in near-real-time (Wewege et al., 2020). With real-time spending monitoring happening, trends of spending habits can be created quickly so that non-normal transactions will stand out. These suspicious transactions then can be confirmed with the end-user if they made the transaction or not. If the end-user states they did not make the transaction, then the financial institution will consider that particular card compromised.
The use of Big Data in financial institutions has had a positive impact on many different facets. One main area of positive influence has been the ability for banks to pre-determine an individual’s ability to repay a loan. Being able to better predict if a loan will be repaid, a financial institution can reduce the amount of risk they are accepting when borrowing money. The bank can also increase its overall customer satisfaction by approving loans that may have been overlooked in the past.
Big Data has also been used quite successfully in finding and preventing fraud. By determining an individual’s spending habits, a financial institution can better detect abnormal spending in near real-time. The quick detection of fraudulent spending can help reduce the need for customers to dispute a past charge. Today, the financial institution could confirm with the customer if they made a specific transaction via a text message.
Although Big Data has helped financial institutions considerably in areas of customer service and fraud detection & prevention, there is still more work that needs to be done. One such area is to expand the input data sources to help make the analytics more accurate and reliable. Accuracy and reliability can be a big challenge when attempting to detect fraud since most individuals now shop online and can quite easily make a purchase in different countries on the same day.
As discussed throughout this paper, Big Data plays a critical role in helping financial institutions make decisions and avoid risk on a daily basis. By taking advantage of real-time data analysis, financial institutions of all sizes can help reduce their business risk.
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Fan, X., Li, X., Lu, W., Webster, C. J., Chen, Z., & Lin, L. (2021). Big Data-Driven Pedestrian Analytics: Unsupervised Clustering and Relational Query Based on Tencent Street View Photographs. ISPRS International Journal of Geo-Information, 10(8), 561. http://dx.doi.org/10.3390/ijgi10080561
Hassani, H., Huang, X., Silva, E., & Ghodsi, M. (2020). Deep Learning and Implementations in Banking. Annals of Data Science, 7(3), 433-446. http://dx.doi.org/10.1007/s40745-020-00300-1
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Mehdiabadi, A., Tabatabeinasab, M., Spulbar, C., Amir Karbassi, Y., & Birau, R. (2020). Are We Ready for the Challenge of Banks 4.0? Designing a Roadmap for Banking Systems in Industry 4.0. International Journal of Financial Studies, 8(2), 32. http://dx.doi.org/10.3390/ijfs8020032
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Tang, J., & Karim, K. E. (2019). Financial fraud detection and big data analytics – implications on auditors’ use of fraud brainstorming session. Managerial Auditing Journal, 34(3), 324-337. http://dx.doi.org/10.1108/MAJ-01-2018-1767
Wewege, L., Lee, J., & Thomsett, M. C. (2020). Disruptions and Digital Banking Trends. Journal of Applied Finance and Banking, 10(6), 15-56. https://coloradotech.idm.oclc.org/login?url=https://www.proquest.com/scholarly-journals/disruptions-digital-banking-trends/docview/2463171819/se-2?accountid=144789