Financial Management in the Big Data Era

Magdalena M^dra-Sawicka

Warsaw University of Life Sciences—SCCW

Introduction

In recent years, several authors have undertaken international research on Big Data in finance and its possible implementation and application in practice. This chapter presents the results of the synthesized literature review of Big Data used by managers in the financial management of financial and non-financial organizations.

In the area of financial management, we can notice a greater need for Big Data technology and data for services such as data warehouse, decision analysis, inquiry statistics, and customer analysis (SMB World Asia, 2015). According to IBM research, around 70% of banking and financial market firms report to use Big Data analytics is creating a competitive advantage for their organization (Turner, Schroeck, & Rebecca, 2013). The use of these technologies becomes the basis for conducting productive operational activity. The purpose of this chapter was also the present concepts and new trends in financial management in the Big Data era.

Ulis chapter proceeds as follows. Section 5-2 introduces Big Data techniques used in financial management. Section 5-3 presents the systematic literature review of different areas where Big Data technologies are being implemented and support financial managers’ decisions. Section 5-4 concludes this chapter with essential recommendations for the use of Big Data in financial management.

Big Data Characteristics in Financial Management

The era of “digital economy” brings digital technologies almost to every sector. Big Data opens new perspectives in finance by bringing the evolutionary breakthrough; thus, the world of finance has the unique ability to combine real-world data with online world data.

Big Data technologies in financial management give support to the decisionmaking process due to a higher level of automation that makes company performance more efficient in case of lowering cost, increasing productivity, improving customer relation, risk detecting, and legal action data processing. Big Data implementation helps in the long run to increase the company’s operational efficiency. It benefits in increasing profitability, growing market share, and lowering risk losses. Big Data technology gives the ability to manage vast amounts of diverse data at the right speed and at the right time to enable them to react in real time. The information technologies market in the financial sphere is one of the fastest-growing ones (Bataev, 2018), with data production that concerns daily operations. Tire data analysis technique discovers more patterns by involving machine learning that extracts proper information and transforms it by using different models for the defined task. Tire machine learning is supportive by artificial intelligence that imitates human intelligence by computer. These technologies allow better data management and its analytics across financial technology that implements automation before complex decision-making processes.

Big Data system joins the aspect of size and speed of data processing. Big Data is a combination of old and new technologies as part of a new data management concept that supports financial and non-financial institutions in developing corporate knowledge about customers, services, market activities, and creating firm’s value.

Hie leader in the digital transformation of the economy is the technology for processing and analyzing Big Data that developed in recent years (Bataev, 2018). Big Data have three main characteristics: volume—are measurable, velocity—need to be processed quickly, and variety—represents a different type of data (timeseries, semi-structured, metadata, and so on). Big Data technologies provided a more natural way to collect data and joined them into massive dynamic databases (Choi, Chan, & Yue, 2017).

Big Data in finance also include structured and unstructured data. Big Data are constituted by techniques and processes, information, privacy/security/ethics, professional competencies, and dedicated modern application. Applications that use Big Data could be used in trading signals, fraud prevention, the Internet of Tilings, and customer insights (Cockcroft & Russell, 2018; Tian, Han, Wang, Lu, &t Zhan, 2015). Big Data technologies in finance have a core topic that could be divided into business intelligence and data mining, system that could be used in industries and security systems, and risk management operating systems. Big Data areas also concern financial distress modeling, financial fraud modeling, and stock market prediction and quantitative modeling (Gepp, Linnenluecke, O’Neill, & Smith, 2018).

According to Bataev (2018), Big Data technologies are being introduced in the financial sphere in targeted mobile marketing, detecting signs of fraud, or managing cash to attract customers.

Implementation of Big Data Technologies for Supporting Financial Management Decision

Different approach of Big Data management constitutes from the company status: financial institution or not. In addition, the use of Big Data in financial management occurs at various levels of the organization’s functioning.

Financial and Non-Financial Organizations

Financial Institutions

Investment bankers, financial advisors, loan officers must have accurate customer information to make better decisions. These organizations are extracting new insights from existing and available internal sources of information to define the most useful Big Data technology that will be accurate for organization strategy (Turner et al., 2013). Big Data solution in the banking sector introduces a higher level of automation, which provides a higher-quality information concerning the return on investment, better model of personnel management, and risk identification. In the aspect of financial management, Big Data in banks improve their operational, efficiency, customer service, risk management (Bataev, 2018). Furthermore, Big Data in the case of the banking system ensure better data protection and confidentially and substitute the lack of qualified personnel while the ongoing data is processed automatically and analyses by created algorithms. Financial institutions can forecast the bankruptcy of a firm better and predict defaulters of loans by introducing the Big Data predictive analytics (Huttunen et al., 2019). In the case of banks, Big Data are used in cybersecurity solutions and customer knowledge management.

The cost of financial fraud has obvious economic implications. Thus, fraud detection is quicker and more effective, especially in the case of financial institutions like banks. Banks identify suspicious connections and present them graphically in the form of networks of contacts. Through visualization, it is possible to diagnose new types and ways of scams faster and more effectively. Banks analytics monitor and track transactions with a high risk of fraud, mostly because they know specific facts beyond (Debenham, 2016). It allows blocking a transaction before the funds are taken out of the client’s account.

The integration of the Internet with Big Data technology supports financial institutions with opportunities to link unstructured data from publicly available sources with records of transactions. These smart analytics provide a solution for identifying fraud by monitoring websites that contain allegations of fraud (Debenham, 2016). Lending firms exchange information about their customers and process a growing amount of personal data thanks to Big Data analytics. Lenders access databases and other data managed by third-party providers to evaluate a consumer’s credit application to assess the creditworthiness (Ferretti, 2018).

The Big Data technology gives the possibility to design rules and strategies in the finance area and helps to achieve a balance between flexibility and efficiency (Choi, Wallace, & Wang, 2018).

Non-Financial Companies

Big Data help enterprises in profit maximization by optimizing the business process (Vera-Baquero, Colomo-Palacios, & Molloy, 2013) by leading toward customer retention (ur Rehman, Chang, Batool, & Yinh Wah, 2016). Big Data implementation in the finance sphere of a company helps to modernize the company’s financial transactions. Many businesses rely on a data system that creates the view of customers, products, and suppliers by using a variety of sources. Big Data in finance allows for increasing the visibility of process information to create a clear understanding of the market and support the trading strategies (Fang & Zhang, 2016). Bigger firms have/produce more data and hence benefit more than smaller firms from Big Data analysis. It is consistent with the statement that large companies shape Big Data.

Big Data in financial management can influence corporate activity via operations (assets) or funding (debt and equity) (Begenau, Farboodi, & Veldkamp, 2018). These are important in case of decisions on granting trade credit. It also helps to reduce unnecessary outflow from companies. Big Data tools help to optimize the cash management in the company, which is especially important in the case of a big group of clients and developed a structure of trade credit. These tools become more wildly practice; thus, it could open up the option for obtaining higher revenues.

Hie possibility to join private companies databased with external one gives new options for better client risk management and thus a better chance to increase sales. Thus, Big Data analysis systems help make better business decisions and react quickly to the market situation. This information primarily creates the opportunity to adapt to customer expectations and improve the quality of products, and increases the company’s competitiveness in the long run. Massive data resources can also help recover lost clients by creating advertising campaigns or long-term strategies.

Non-financial enterprises use Big Data technology by including data sources directly from operating activities. The direct data sources in companies are gathered from operational information related to supply chain management, production, fleet management, marketing strategies, behavior analysis of employees, etc. (ur Rehman et al., 2016). Big Data help non-financial companies to integrate and consolidates various economic and environmental data into one comprehensive, live-automated data warehouse. This technology could be used in agricultural finance, natural resource, and environmental fields of companies’ operation. It can be accessed by data visualization tools that support the analyses of commodity futures used for selecting the most beneficial contracts, spot price interpolation to set the commodity spot price (Woodard, 2016). Big Data analytics support also decision in inventories transactions, inventory receipts, issues, valuations, frequency identification, and financial statement report creation.

Big Data also support customer preference identification by text analysis on the posted opinion of customers that induce the study of negative words with lower stock returns (Subrahmanyam, 2019). The advanced analytical algorithms based on Big Data enable building a detailed customer profile, which will include their needs, preferences, and capabilities. The personalized offer depends on the current context and contact channel.

Capital Market Investments

The Big Data model is used on capital market in various ways like enhancing the capital investment thanks to real-time predictive modeling, decreasing the cost of capital by selecting the most optimal financial resources, used market stock prediction model (that used the fundamental information and sentiment measure or information from social media (O’Connor, 2013)). The investment choices of financial managers affect the prices, cost of capital on the market. Thus, Big Data analysis improves investors’ forecasts and reduces equity uncertainty (Begenau et al., 2018). The advantage of using these technologies in financial management underlines the importance of real-time predictive modeling. This approach also includes proper classification, clustering, and association rules, which helps to switch managers from retrospective analytical techniques to predictive financial outcomes.

Stock market prices are from a technical perspective modeled by using machine learning algorithm (Nayak, Pai, &. Pai, 2016; Cockcroft & Russell, 2018). Machine learning forecasts stock returns based on a large number of documented anomalies (Subrahmanyam, 2019) and thus provide manager information. Another approach of Big Data used in finance shows the aggregated rankings of stock, according to the level of forecasted returns of different securities (Da, Huang, & Jin, 2018). That’s why Big Data technologies impact on decreasing cost of capital. It is supported by faster-processed data that cover the macro announcements, financial statements, competitors’ performance, and other industry results. High-speed data processing lowers uncertainty, which reduces risk and makes investments more attractive (Begenau et al., 2018).

Tire stock predicting model of market valuation in the Big Data era based on the media pessimism measures are more popular than the fundamental values of equities (Bollen, Mao, & Zeng, 2011; Tetlock, 2007). This approach includes using social media, news articles, Twitter, and historical data series variables in fundamental analysis of stock prices (Attigeri et al., 2015). It changes the perspective of decisions taken by managers in the area of company finances.

Risk Management

All financial transactions and credit transactions involve risks or uncertainties. Financial risk prevention and financial fraud identification have been the essential aspects that financial companies need to focus on and manage (Zhang, Li, Shen, Sun, Yang, 2015). Risk management in banks is more developed via bid data. Its usage allows building a portfolio of owned financial instruments, which helps to reduce customer risk and improve credit risk management.

Tire other option is the optimization of bank investments by using up-to-date data that help you to choose the best investment options. It supports financial managers moving toward a more strategic and proactive role in the finance area.

Financial risk management uses different financial instruments to manage various types of risk, such as operational, credit, market, foreign exchange, or liquidity risk (Huttunen et al., 2019). Credit intermediaries are exploiting the mechanisms of data collection under Big Data. Data for the analysis of creditworthiness come, among others, from social media portals, where not only the client information but also friends’ social status and financial situation are assessed (Yu, Huang, Hu, & Cai, 2010). In the case of risk management, Big Data technology decreases the problem of asymmetric information by reducing the cost for both financial institutions and enterprises (Cao, 2015; Ferretti, 2018).

Financial Markets

Financial markets are characterized by a large number of heterogeneous participants interacting with one another in nonlinear ways (Tang, Xiong, Luo, & Zhang, 2019)- Big Data views in the analyses of the capital market include its volatility, portfolio development, risk analysis, and market transparency (Ferreira Si da Costa, 2017)- The volatility concerns the dispersion of the assets price.

Big Data technology supports managers’ decision by implementing advance prediction model that provide higher gains for investor due to more reliable analyses when the models have access to high-frequency data with continuity data production. Building an investment portfolio and risk analysis thanks to that Big Data enhances fundamental analysis by processing and quantification different business valuation models, which help to consider the best market opportunities for financial management. Machine learning techniques and statistical modeling are supporting Big Data solutions (James, Witten, Hastie, & Tibshirani, 2013). Market is transparently investigated by using business intelligence to identify market liquidity and thus its efficiency. Further development of this application Big Data usage on information disclosure is important for the functioning of financial systems across different markets (Ferreira Si da Costa, 2017), as well as significant due to changes in the regulations.

Data from the financial market data are accessible on a real-time basis and could be used with a business intelligence system. This solution is joined mostly with cloud services that can be used to store data in remote locations. Other Big Data technologies are based on data extraction and algorithm creation, which is crucial for machine learning. It reduces the risk and creates new business value to financial management date from a more comprehensive perspective.

Accounting Data Processing

The whole data-generating process as a result of purchases, sales, and other transactions is related to companies accounting system. Only by integrating (compiling multiple data), selecting (extracting relative data), cleaning (removing conflicting data), transforming (transforming into easy-to-extract forms), searching (extracting data model in an intelligent way), and estimating (assessing its value) technology transform accounting data into relevant and useful information for managers (Ke & Shi, 2014). These applications become standard tools in the case of large companies.

Budget Management

The budget performance evaluation is a kind of innovation in financial management methods. Quick data analysis through Big Data technologies and tool provide up-to-date information that notifies managers to locate money in more effective financial instruments and capital resources. Big Data technologies support the application of budget performance management and analysis techniques (Liu, 2014), both in the case of government and private companies. Budgets can provide a report that presents limited resources of the company and requires control. Tire company data platform undertakes data analysis and could improve budget performance by focusing on data collection, conversion, integration, and storage (Liu, 2014). Big Data technologies support budget adjustment and analysis. After integration budgets with the business information system, the budget report will be quickly available for further decisions, and the budget evaluation could be performed at every moment. Financial managers can compare multiple future outcomes and customize the budgeting model to various assets and portfolios that the company possesses (Huttunen et al., 2019). Data science supports budgeting optimization and allows companies to combine diverse financial and non-financial data and produce more comprehensive reporting systems.

Controlling and Audits

Big Data has been used in many areas to enhance the process of inspecting, transforming, and modeling (Cao, Chychyla, & Stewart, 2015). It is already applied in formal auditing, data analysis in continuous auditing, and risk management (Zhang, Yang, & Appelbaum, 2015). In the auditing profession, both quantitative and textual data are being used. Big Data analysis improves corporate finance, controlling, and financial audits. Auditors use Big Data models to predict financial distress and detect financial fraud. Big Data also refers to the techniques and technology used to uncover patterns in data by prepared algorithms. External auditors can improve their fraud risk assessments by using Big Data financial fraud models (Gepp et al., 2018). As a background for model creation, Big Data systems use historical data about previous frauds.

Auditors could use Big Data techniques and methods for forecasting financial distress. Data mining techniques in the form of neural networks are used to build and test distress modeling (Chen & Du, 2009). Auditors are reluctant to use techniques and technology that are more advance comparing to those used by clients of firms (Alles, 2015). Real-time auditing processes are required. Huis, continuous auditing refers to a constant cycle of auditing (Gepp et al., 2018; Routledge, 2018).

Conclusions

Tire application of Big Data is transforming the entire financial industry and the characteristics and role of financial managers’ decisions by breaking the data barriers and ensuring new quality. Big Data in financial management is becoming one of the most promising areas of management (Sun, Shi, & Zhang, 2019). However, its proper application needs to be supported by the higher cost of its implementation. Big Data technologies also include higher pressure on data protection and training of personnel. Furthermore, it also includes the higher level of the distrust of new technologies, continuing changes in the structure of the data, and other external data, which brings the problem with proper integration of the data and the quality from different providers.

Big Data technology provides alternatives for asset valuation and thus creates the business knowledge for managers (Huttunen et al., 2019). The use of the data should reflect the investor’s expectations that reduce their uncertainty about investment outcomes.

Hie key challenges are the problems with data integration and data collection (Cao et al., 2015). The Big Data implementation impacts on closer cooperation of non-financial companies with financial institutions (Vera-Baquero et al., 2013). The data protection becomes a problem of Big Data development and thus creates a complex and multifaceted concept from a societal and legal obligation (Ferretti, 2018).

Hie Big Data analytics in financial management stand in front of many changes, like how to effectively extract valuable information and design a highly efficient computing system. These systems need to process distributed historical and incoming data. Another problem is the creation of data centers (Big Data parking with cloud solutions), a system that will deploy thousands of computers effectively (Tian et al., 2015). Big Data requires a diverse range of tools, including data warehousing and providing for data anonymity (Cockcroft & Russell, 2018).

Hie financial manager will receive thus ready dashboard of data and information that are connected with graphs and standardized models and taxonomies. It will improve operations and the creation of further business opportunities. Htis approach will change manager involvement in optimizing operational efficiency.

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