Current landscape and influence of big data on finance

In the age of technological innovation, various types of data are available with the advance of information technologies, and data is seen as one of the most valuable commodities in managing automation systems [13, 68]. In this sense, financial markets and technological evolution have become related to every human activity in the past few decades. Big data technology has become an integral part of the financial services industry and will continue to drive future innovation [12]. Financial innovations are also considered the fastest emerging issues in financial services. More specifically, they cover a variety of financial businesses such as online peer-to-peer lending, crowd-funding platforms, SME finance, wealth management and asset management platforms, trading management, crypto-currency, money/remittance transfer, mobile payments platforms, and so on. All of these services create thousands of pieces of data every day. Therefore, managing this data is also considered the most important factor in these services. Any damage to the data can cause serious problems for that specific financial industry. Nowadays, financial analysts use external and alternative data to make better investment decisions. In addition, financial industries use big data through different predictive analyses and monitor various spending patterns to develop large decision-making models. In this way, the industries can decide which financial products to offer [29, 48]. Millions of data are transmitted among financial companies. That is why big data is receiving more attention in the financial services arena, where information affects important success and production factors. It has been playing increasingly important roles in consolidating our understanding of financial markets [71]. In any case, the financial industry is using trillions of pieces of data constantly in everyday decisions [22]. It plays an important role in changing the financial services sector, particularly in trade and investment, tax reform, fraud detection and investigation, risk analysis, and automation [37]. In addition, it has changed the financial industry by overcoming different challenges and gaining valuable insights to improve customer satisfaction and the overall banking experience [45]. Razin [65] pointed out that big data is also changing finance in five ways: creating transparency, analyzing risk, algorithmic trading, leveraging consumer data and transforming culture. Also, big data has a significant influence in economic analysis and economic modeling [16, 21].

This paper seeks to explore the current landscape of big data in financial services. Particularly this study highlights the influence of big data on internet banking, financial markets, and financial service management. This study also presents a framework, which will facilitate the way how big data influence on finance. Some other services relating to finance are also highlighted here to specify the extended area of big data in financial services. These are the contribution of this study in the existing literatures.

The concept of big data in finance has taken from the previous literatures, where some studies have been published by some good academic journals. At present, most of the areas of business are linked to big data. It has significant influence on various perspectives of business such as business process management, human resources management, R&D management [8, 63], business analytics [19, 26, 42, 59, 63], B2B business process, marketing, and sales [30, 39, 53, 58], industrial manufacturing process [7, 15, 40], enterprise’s operational performance measurement [20, 69, 81], policy making [2], supply chain management, decision, and performance [4, 38, 64], and so other business arenas.

Particularly, Rabhi et al. [63] mentioned big data as a significant factor of business process management& HR process to support the decision making. This study also talked about three sophisticated types of analytics techniques such as descriptive analytics, predictive analytics, and prescriptive analytics in order to improve the traditional data analytics process. Duan and Xiong [19], Grover and Kar [26], Ji et al. [42], and Pappas et al. [59] also explored the significance of big data in business analytics. Big data helps to solve business problems and data management through system infrastructure, which includes any technique to capture, store, transfer, and process data. Duan and Xiong [19] found that top-performing organizations use analytics as opposed to intuition almost five times more than do the lower performers. Business analytics and business strategy must be closely linked together to gain better analytics-driven insights. Grover and Kar [26] mentioned about firms, like Apple, Facebook, Google, Amazon, and eBay, that regularly use digitized transaction data such as storing the transaction time, purchase quantities, product prices, and customer credentials on regular basis to estimate the condition of their market for improving their business operations [61, 76]. Holland et al. [39] showed the theoretical and empirical contributions of big data in business. This study inferred that B2B relationships from consumer search patterns, which used to evaluate and measure the online performance of competitors in the US airline market. Moreover, big data also help to foster B2B sales with customer data analytics. The use of customer’s big datasets significantly improve sales growth (monetary performance outcomes), and enhances the customer relationship performance (non-monetary performance outcomes) [30]. It also relates to market innovation with diversified opportunities.

Big data and its analytics and applications work as indicators of organizations’ ability to innovate to respond to market opportunities [78]. Also, big data impact on industrial manufacturing process to gain competitive advantages. After analyzing a case study of two company, Belhadi et al. [7] stated ‘NAPC aims for a qualitative leap with digital and big-data analytics to enable industrial teams to develop or even duplicate models of turnkey factories in Africa’. This study also identified an Overall framework of BDA capabilities in manufacturing process, and mentioned some values of Big Data Analytics for manufacturing process, such as enhancing transparency, improving performance, supporting decision-making and increasing knowledge. Also, Cui et al. [15] mentioned four most frequently big data applications (Monitoring, prediction, ICT framework, and data analytics) used in manufacturing. These are essential to realize the smart manufacturing process. Shamim et al. [69] argued that employee ambidexterity is important because employees’ big data management capabilities and ambidexterity are crucial for EMMNEs to manage the demands of global users. Also big data appeared as a frontier of the opportunity in improving firm performance. Yadegaridehkordi et al. [81] hypothesized that big data adoption has positive effect on firm performance. That study also mentioned that the policy makers, governments, and businesses can take well-informed decisions in adopting big data. According to Hofmann [38], velocity, variety, and volume significantly influence on supply chain management. For example, at first, velocity offers the biggest opportunity to intensification the efficiency of the processes in the supply chain. Next to this, variety supports different types of data volume in the supply chains is mostly new. After that, the volume is also a bigger interest for the multistage supply chains than to two-staged supply chains. Raman et al. [64] provided a new model, Supply Chain Operations Reference (SCOR), by incorporating SCM with big data. This model exposes the adoption of big data technology adds significant value as well as creates financial gain for the industry. This model is apt for the evaluation of the financial performance of supply chains. Also it works as a practical decision support means for examining competing decision alternatives along the chain as well as environmental assessment. Lamba and Singh [50] focused on decision making aspect of supply chain process and mentioned that data-driven decision-making is gaining noteworthy importance in managing logistics activities, process improvement, cost optimization, and better inventory management. Sahal et al. [67] and Xu and Duan [80] showed the relation of cyber physical systems and stream processing platform for Industry 4.0. Big data and IoT are considering as much influential forces for the era of Industry 4.0. These are also helping to achieve the two most important goals of Industry 4.0 applications (to increase productivity while reducing production cost & to maximum uptime throughout the production chain). Belhadi et al. [7] identified manufacturing process challenges, such as quality & process control (Q&PC), energy & environment efficiency (E&EE), proactive diagnosis and maintenance (PD&M), and safety & risk analysis (S&RA). Hofmann [38] also mentioned that one of the greatest challenges in the field of big data is to find new ways for storing and processing the different types of data. In addition, Duan and Xiong [19] mentioned that big data encompass more unstructured data such as text, graph, and time-series data compared to structured data for both data storage techniques and data analytics techniques. Zhao et al. [86] identified two major challenges for integrating both internal and external data for big data analytics. These are connecting datasets across the data sources, and selecting relevant data for analysis. Huang et al. [40] raised four challenges, first, the accuracy and applicability of the small data-based PSM paradigms is one kind of challenge; second, the traditional static-oriented PSM paradigms difficult to adapt to the dynamic changes of complex production systems; third, it is urgent to carry out research that focuses on forecasting-based PSM paradigms; and fourth, the determining the causal relationship quickly, economically and effectively is difficult, which affects safety predictions and safety decision-making.

The above discussion based on different area of business. Whatever, some studies (such as [6, 11, 14, 22, 23, 41, 45, 54, 68, 71, 73, 75, 83, 85] focused different perspectives of financial services. Still, the contribution on this area is not expanded. Based on those researches, the current trends of big data in finance have specified in finding section.

After studying the literature, this study has found that big data is mostly linked to financial market, Internet finance. Credit Service Company, financial service management, financial applications and so forth. Mainly data relates with four types of financial industry such as financial market, online marketplace, lending company, and bank. These companies produce billions of data each day from their daily transaction, user account, data updating, accounts modification, and so other activities. Those companies process the billions of data and take the help to predict the preference of each consumer given his/her previous activities, and the level of credit risk for each user. Based on those data, financial institutions help in taking decisions [84]. However, different financial companies processing big data and getting help for verification and collection, credit risk prediction, and fraud detection. As the billions of data are producing from heterogeneous sources, missing data is a big concern as well as data quality and data reliability is also significant matter. Whatever, the concept of role of financial big data has taken form [71], where that study mention the sources of financial market information include the information assembled from stock market data (e.g., stock prices, stock trading volume, interest rates, and so on), social media (e.g., Facebook, twitter, newspapers, advertising, television, and so on). These data has significant roles in financial market such as predicting the market return, forecasting market volatility, valuing market position, identifying excess trading volume, analyzing the market risk, movement of the stock, option pricing, algorithmic trading, idiosyncratic volatility, and so on. Based on these discussions, a theoretical framework is illustrated in Fig. 2.

Massive data and increasingly sophisticated technologies are changing the way industries operate and compete. The financial world is also operating with these big data sets. It has not only influenced many fields of science and society, but has had an important impact on the finance industry [6, 13, 23, 41, 45, 54, 62, 68, 71‐73, 82, 85]. After reviewing the literature, this study found some financial areas directly linked to big data, such as financial markets, internet credit service-companies and internet finance, financial management, analysis, and applications, credit banking risk analysis, risk management, and so forth. These areas are divided here into three groups; first, big data implications for financial markets and the financial growth of companies; second, big data implications for internet finance and value creation in internet credit-service companies; and third, big data in financial management, risk management, financial analysis, and applications. The discussion of big data in these specified financial areas is the contribution made by this study. Also, these are regarded as emerging landscape of big data in finance in this study.

Big data, machine learning, AI, and the cloud computing are fueling the finance industry toward digitalization. Large companies are embracing these technologies to implement digital transformation, bolster profit and loss, and meet consumer demand. While most companies are storing new and valuable data, the question is the implication and influence of these stored data in finance industry. In this prospect, every financial service is technologically innovative and treats data as blood circulation. Therefore, the findings of this study are reasonable to conclude that big data has revolutionized finance industry mainly with the real time stock market insights by changing trade and investments, fraud detection and prevention, and accurate risk analysis by machine learning process. These services are influencing by increasing revenue and customer satisfaction, speeding up manual processes, improving path to purchase, streamlined workflow and reliable system processing, analyze financial performance, and control growth. Despite these revolutionary service transmissions, several critical issues of big data exist in the finance world. Privacy and protection of data is one the biggest critical issue of big data services. As well as data quality of data and regulatory requirements also considered as significant issues. Even though every financial products and services are fully dependent on data and producing data in every second, still the research on big data and finance hasn’t reached its peak stage. In this perspectives, the discussion of this study reasonable to settle the future research directions. In future, varied research efforts will be important for financial data management systems to address technical challenges in order to realize the promised benefits of big data; in particular, the challenges of managing large data sets should be explored by researchers and financial analysts in order to drive transformative solutions. The common problem is that the larger the industry, the larger the database; therefore, it is important to emphasize the importance of managing large data sets for large companies compared to small firms. Managing such large data sets is expensive, and in some cases very difficult to access. In most cases, individuals or small companies do not have direct access to big data. Therefore, future research may focus on the creation of smooth access for small firms to large data sets. Also, the focus should be on exploring the impact of big data on financial products and services, and financial markets. Research is also essential into the security risks of big data in financial services. In addition, there is a need to expand the formal and integrated process of implementing big data strategies in financial institutions. In particular, the impact of big data on the stock market should continue to be explored. Finally, the emerging issues of big data in finance discussed in this study should be empirically emphasized in future research.