Big Data Analytics in the Banking Industry

 

The implementation of big data analytics (BDA) in organizations is a costly and complex undertaking, and given there is no recipe that guarantees a successful implementation and execution, a careful and honest assessment of different aspects of the business must be conducted, from the technology, data collection, the human resources involved in the process, and managerial support (Hajiheydari et al., 2021). Hajiheydari et al. state that approximately 60% to 85% of BDA projects fail not because the technology is bad, but because organizations do not have a clear understanding of how to utilize data analytics to construct achievable business goals, or how to successfully integrate its practice with day-to-day business operations (Sun et al., 2020). BDA offers financial institutions countless benefits, and provides valuable insights to personalize customer service via the analysis of transactional records and other data, including social media information (Flynn, 2020; Sun et al., 2020). 

Big Data Analytics as a Business Strategy 

Decision-making in the financial industry depends on the collection and processing of customer data in efforts to extract actionable insights that support decision-makers (Sun et al., 2020) in the formulation of better business strategies that meet the needs and wants of their customers, create business value (Hajiheydari et al., 2021), improve adaptability, profitability and cash flow, and market competitiveness (Jiangbo, 2022). 

The qualities of big data known as the three V’s (volume, variety, and velocity), have made information the lifeblood of the financial industry, it is the “new oil” that has impacted how the banking industry conducts business, policy-making, enabled market appraisals, and through BDA, it has contributed greatly to the improvement of customer relationships (Sun et al., 2020). 

Hajiheydari et al. (2021) details the path to a successful BDA implementation and execution through what are called “enablers.” Many of these enablers or components are related to one another and there are also dependencies that exist between them; therefore, organizations must address all the levels and dependencies between enablers when evaluating the efficacy of a BDA project. The list of enablers is as follows: 

1. Big data governance, 
2. Data-driven culture, 
3. Top management support, 
4. Technical and skilled workforce, 
5. Data integrity, 
6. Big data privacy, 
7. Concerns with big data security, 
8. Financial support, 
9. Integrating big data in business processes, 
10. Management of legacy systems and dependencies, 
11. Appropriate organizational structures, 
12. Readiness of infrastructure, 
13. Selection of appropriate big data technologies 
14. Clear and justifiable business case to support investment, 
15. Scalability, 
16. BDA alignment with business strategy, 
17. Reliability of big data technologies (fault-tolerance), 
18. Capabilities for big data customization, and 
19. End-user empowerment (Hajiheydari et al., 2021). 

BDA enablers represent the business dynamic of the financial industry of today, and although it is a challenging practice, the benefits are many not only for customers, but for financial institutions and the industry in general as it generates an enhanced, data-driven decision-making process that is well aligned with business strategy in order to achieve a competitive advantage. 

Benefits of Big Data Analytics in the Banking Industry 

Banks generate massive amounts of personal data from customers and also corporate data from daily business transactions, regulatory processes, and reporting, and along with social media information, including sentiment analysis, the banking industry can perform robust BDA to measure and manage credit risk (Sun et al., 2020), analyze and detect fraudulent transactions, develop improved BDA techniques to eliminate fraud in the future (Dixon, 2019), and offer personalized customer service (Flynn, 2020). 

Machine learning (ML)-based credit risk early

warning systems are possible with the help of BDA and data mining, including the capability to determine customer interest rates and the analysis of credit scores to pinpoint fraudulent activity and/or behavior (Flynn, 2020; Jiangbo, 2022; Sun et al., 2020). Common fraud techniques the banking industry deals with on a consistent basis are employee fraud, check kiting (writing checks against accounts without any funds), false loan applications, and empty envelope deposits through automated teller machines (ATMs) (Dixon, 2019), and with the popularity of online banking, especially since the COVID-19 lockdowns, more and more fraud techniques are attacking customers’ data and finances such as account takeover (ATO), sim swapping, phishing, malware, card not present (CNP), and man in the middle (MitM) attack (OneSpan, 2022). 

Fraud committed through online means is continually evolving, but so are the techniques banks develop with the help of BDA and ML to detect, adapt to, and predict fraud, also known as “fraud analytics,” by employing authentication technology that consists of four categories of data: (1) Knowledge (something the customer knows about like a password), (2) Possession (something the customer has like a mobile phone), (3) Inherence (something the customer is like a fingerprint), and (4) Behavioral (something the customer does like a bank transaction) (OneSpan, 2022). 

BDA is currently the best methodology for detecting fraud, and with its capability for processing data in real-time along with powerful ML algorithms, fraudulent transactions can be flagged quickly, an activity impossible for humans to achieve manually (Dixon, 2019). 

OneSpan (2022) explains the different ML-based analytics conducted to tackle financial fraud are predictive analytics, pattern recognition and anomaly detection, visual analytics tools, and forensic analysis, which fraud investigation teams are unable to conduct due to the massive amounts of data that requires processing, whereas ML algorithms process the data in real-time, learn from it and are able to employ the newly learned instructions immediately. Millions of customer bank transactions are analyzed by BDA and ML algorithms to analyze patterns and identify those consistent with fraudulent markers. The system is then able to flag those transactions quickly and make a decision whether to allow the transaction to continue or block it altogether (Dixon, 2019) without posing any inconvenience upon the customer. Dixon continues by stating fraud claims cost customers $34 if detected within 24-hour period, but those identified after 3 to 5 months cost $1061. BDA can identify fraudulent transactions in real-time, resulting in cost savings as well as preserve a bank’s reputation. 

BDA enables financial institutions to provide personalized experiences to their customers through the concept of profiling to increase the number of satisfied customers and encourage retention (Flynn, 2020). Combining customer historical records with BDA-based profiles, allows banks to know their customers better and understand their online banking behaviors in order to tailor available services to their unique preferences. Customer profiling includes social media information, demographics, spending, product/service usage, including any declined offers, and sentiment analysis associated with the banking industry in general (Flynn, 2020). 

Only through BDA the analysis of massive amounts of collected customer data can be performed to determine credit scores and identify and mitigate credit risk, tackle fraud with innovative ML-based techniques, and personalize services based on customer preferences to increase satisfaction, encourage retention, and maintain a good reputation, and along with an assessment of “enablers,” financial institutions can improve their profitability and remain competitive with the use of BDA as a business strategy, including its integration into the construction of goals, daily operations, and the decision-making process. 

References 

Dixon, M. (2019, February 26). How can data analytics tackle banking fraud. Selerity. https://seleritysas.com/blog/2019/02/26/how-can-data-analytics-tackle-banking-fraud/ 

Flynn, S. (2020, December 28). How big data analytics are used in the banking industry. Open Data Science. https://opendatascience.com/how-big-data-analytics-are-used-in-the-banking-industry/ 

Hajiheydari, N., Delgosha, M. S., Wang, Y., & Olya, H. (2021). Exploring the paths to big data analytics implementation success in banking and financial service: An integrated approach. Industrial Management & Data Systems, 121(12), 2498-2529. https://doi.org/10.1108/IMDS-04-2021-0209 

Jiangbo, Y. (2022). Big data analytics and discrete choice model for enterprise credit risk early warning algorithm. Security and Communication Networks, 2022, 1-13. https://doi.org/10.1155/2022/3272603 

OneSpan. (2022). Fraud analytics. https://www.onespan.com/topics/fraud-analytics 

Sun, H., Rabbani, M. R., Sial, M. S., Yu, S., Filipe, J. A., & Cherian, J. (2020). Identifying big data’s opportunities, challenges, and implications in finance. Mathematics, 8(10), 1-20. https://doi.org/10.3390/math8101738