Big Data in Finance: Opportunities, Challenges & CFO Strategies

Introduction

The growth of Big Data has had significant transformative effects on several industries, including technology, agriculture, health, education, and finance. Over the past decade, the number of humans using smartphones has increased tremendously, creating a large pool of user data as smartphone users stamp their digital footprints all over the web. The electronic data collected as a result of these activities yields Big Data that provides valuable insights to decision-makers looking for data points to inform their processes. The growth of Big Data has necessitated the development of computer algorithms and tools capable of analyzing data at scale. Good analysis and interpretation of data allow organizations to co-create solutions alongside their target users and generate new value that is highly tailored to the needs of the market (Turner, Schroeck & Shockley, 2013).

The financial services sector is one of the industries that have adopted Big Data at scale to inform decision-making. In stock trading, algorithmic trading and Big Data help investors utilize historical data to maximize returns on their portfolios. As the adoption of Big Data in the financial services sector continues, the landscape of the industry is likely to see significant changes in the coming years. Nonetheless, organizations are still facing challenges in the capture, storage, analysis, and interpretation of huge volumes of data (Chen, Chiang & Storey, 2012).

Big Data Opportunities in Financial Services

Traditional data management tools and models have posed several challenges to the finance industry in terms of security and operational inefficiencies. One of the major security issues is fraudulent activity by both insiders and outsiders. With the world becoming increasingly connected, outsiders acting in bad faith — such as hackers — have more opportunities to access company data. Traditional models lack the systems required to secure digital assets as hacking techniques become more advanced. This security problem must be addressed urgently given the significant risk it poses to the financial services sector. The growth of Big Data and associated IT infrastructure can help resolve this problem. Another limitation of traditional data tools is the inability to accurately analyze customer sentiment. Customer tastes are rapidly changing as consumers gain more choices in the market, and industries must be able to fulfill these evolving needs satisfactorily (Nath, 2019).

For companies to know what their customers want in a timely manner, they need to collect and analyze large volumes of customer data. Traditional data management tools and models do not provide the techniques necessary to collect or analyze such data quickly. Additionally, traditional tools lack key functionalities such as the ability to segment customers by psychographic and demographic profiles. This inability to perform advanced segmentation limits businesses in their capacity to accurately target market segments during advertising campaigns. There is a growing need for more advanced systems that can help overcome these challenges, particularly in the financial services sector (Zhou, Fu & Yang, 2016).

The growth of technology and ever-increasing data proliferation has transformed how industries operate. In recent years, approximately 90% of the world's data has been generated within a very short period, with the world creating some 2.5 quintillion bytes of data every day. This phenomenon is known as Big Data, and it presents several opportunities across data collection, data processing, and data analysis. CFOs are increasingly devoting time to data analytics, and the functions of their departments are constantly expanding and shifting. The average finance professional must now expand their skills to include data analysis and analytics to meet the demands of the modern finance department. As a result, CFOs are prioritizing the retraining of finance professionals within their teams (Zhou, Fu & Yang, 2016).

It is vital that organizations focus on business areas where Big Data can provide disproportionate value. For most financial services organizations, this means conducting deep customer analysis to develop a better understanding of customers and anticipate their financial needs. This data can be used to generate leads, improve existing products, and explore new technologies and business channels (Zhou, Fu & Yang, 2016).

One of the most important facets of Big Data is predictive analytics. Predictive analytics are useful in foreseeing fraudulent activity and stopping it before it causes harm to an organization. Further, predictive analytics can predict potential breakdowns in a financial system. As many industries have already done, the financial services sector can also leverage Big Data in marketing. Financial services firms must build strong relationships with their clients and develop platforms that enable easy interaction with customers. The value of such an approach lies in informed, relevant, and timely customer interaction, which provides valuable real-time data for the organization (Chen, Chiang & Storey, 2012).

Big Data is particularly powerful when it comes to market segmentation — a vital marketing concept that ensures organizations tailor their messages to specific audiences. When organizations leverage Big Data to segment their marketing messages and utilize the broad reach of the internet, the potential impact on the bottom line is substantial. According to an IBM survey, no fewer than 25% of financial organizations are already using Big Data to build competitive advantages. Big Data can help finance departments identify customer segments in real time, detect and prevent fraudulent activity, improve promotional activities, accelerate growth, and track key performance metrics (Chen, Chiang & Storey, 2012).

Many CFOs' applications of Big Data have had positive effects on productivity and business outcomes. Specific capabilities such as record linkage, deep learning, and graph analytics have proven valuable in fighting crime, reducing fraud, closing gaps in systems, combating identity theft, and addressing several other important concerns. It is also notable that many systems developed today are open source and provide decentralized platforms that are easy to install, configure, and manage. Most of these systems include built-in machine learning libraries that make data analysis more accessible for organizations (Jin et al., 2015).

Big Data is also creating opportunities for organizations to enter industries adjacent to their core offerings. For instance, large finance firms and financial technology companies are entering the microfinance sector. The competition created by such new entrants benefits both industries and consumers by driving better products and more competitive pricing. CFOs use Big Data to analyze whether such strategic moves are financially feasible. Given the huge volumes of data companies collect today, these feasibility evaluations tend to be substantially accurate (Joshi, 2018).

Conclusion

Big Data has had several transformative effects across industries, especially in the financial services sector and within organizational finance departments. CFOs have recognized the role Big Data plays — and will continue to play — in both operational and strategic decision-making. While CFOs and their organizations may feel inundated with data, this abundance ultimately serves their organizations' interests. Due to the relative novelty of the field, Big Data is being embraced rapidly, and this will drive the process improvements that typically accompany adoption at scale. Increased uptake of Big Data by the financial services sector and finance departments will ease the CFO's workload through greater automation, improved statistical accuracy, enhanced collaboration capabilities, and more sophisticated analytical tools (Turner, Schroeck & Shockley, 2013).

CFOs understand that to compete in today's data-driven market, their organizations must leverage all available data assets to better understand their customers, markets, products, competitors, supply chains, employees, and other industry stakeholders. Big Data will prove useful to all business leaders, not just CFOs (Turner, Schroeck & Shockley, 2013).

References

Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4).

Jin, X., Wah, B. W., Cheng, X., & Wang, Y. (2015). Significance and challenges of big data research. Big Data Research, 2(2), 59–64.

Joshi, N. (2018). How big data can transform the finance industry. Retrieved October 7, 2019, from https://www.bbntimes.com/en/technology/big-data-is-transforming-the-finance-industry.

Nath, T. I. (2019). How big data has changed finance. Retrieved October 7, 2019, from https://www.investopedia.com/articles/active-trading/040915/how-big-data-has-changed-finance.asp.

Turner, D., Schroeck, M., & Shockley, R. (2013). Analytics: The real-world use of big data in financial services. IBM Global Business Services, 27.

Yin, S., & Kaynak, O. (2015). Big data for modern industry: Challenges and trends. Proceedings of the IEEE, 103(2), 143–146.

Zhou, K., Fu, C., & Yang, S. (2016). Big data driven smart energy management: From big data to big insights. Renewable and Sustainable Energy Reviews, 56, 215–225.