Research Challenges and the Role of Data in Financial Management
Dr. Aleksandre Mikeladze
Associate Professor
Caucasus School of Business
amikeladze@cu.edu.ge
Abstract
There is a lot of work in developing countries focused on teaching and building research capabilities. When governments and individuals realize how difficult it is to have a significant impact on a problem, they may feel pessimistic, but this should not blind them to the reality that much has been done. The number of skilled scientists in emerging countries is much higher than it was a few years ago. Nonetheless, there is a significant gap between desire and success, and the question is what can be done to close it.[1]
The free sharing of knowledge between scientists, as well as openness, should be a basic standard in the scientific community. The greatest method to address a scientific problem is to make it known to a great number of people outside the field. It has been demonstrated that the capacity to bring together solvers with diverse scientific interests is related to successful problem resolution. Furthermore, competent problem solvers address challenges that are either inside or outside the scope of their expertise, suggesting knowledge transfer from one subject to another.[2] One of the purposes of this research is to look at some of the current challenges in academic research and provide some solutions. Furthermore, the significance of Big Data has been acknowledged, given that financial transactions now occur on a daily basis in the financial industry. In this context, the use of data processing and analysis technologies in the economic and financial sectors has recently helped both academics and professionals. The literature review highlights the significance of Big Data in the financial sector. The paper introduces the subject by concentrating on significant technological difficulties such as data processing, security, modeling, and interpretation.
Introduction
Openness, the free exchange of information between scientists, is recognized as an essential institutional norm for scientific progress. However, research shows that careers, publication priorities and financial concerns can outweigh openness and have a negative impact on the overall progress of science. As a result, activities to solve scientific problems have been limited, and a wide range of knowledge has not been fully exploited. Informing large groups of unknown “outsiders” about complex, unsolved scientific problems can be an efficient strategy. Many solvers rely on their experience and knowledge to solve problems.[3]
Currently, the expansion of information technology has resulted in the availability of many types of data, acknowledged as one of the most valuable assets. In this regard, the development of financial markets and technology during the last few decades has been linked to all human activities. Big data technology is already an important part of the financial industry. Financial professionals are increasingly relying on data to make better-informed investment decisions. Furthermore, the financial industry is utilizing big data in conjunction with a number of predictive analytics methodologies. Information is exchanged between firms. In any event, data is used in the financial industry’s day-to-day decision-making. Big Data impacts on economic research as well. In this study, we gathered and examined the perspectives of numerous researchers, academicians, and others on big data and financial operations. Big data research in financial services, on the other hand, is not as extensive as it is in other industries. There has been some studies on a few particular concerns, but no large-scale review that adequately reflects the impact and potential of Big Data in financial services. As a result, this research looks into the impact of big data on finance, which is unique. This research is also important for those who work in this field. In this paper, the issue of Big Data is examined from a variety of financial perspectives, providing the reader with a comprehensive picture. As a result, the goal of this research is to give an outline of the research issues as well as the function of data in financial management.[4]
We’ve been hearing a lot about “Big Data” in recent years. Decisions in fields such as business and economics are based on facts and analysis rather than experience and intuition. Financial operations controlled by transactions, records, accounting, and forecasts may come to life in the age of “big data”. Everything in our surroundings generates Big Data on a regular basis. “Public data” refers to information that is owned by governments, government agencies, and communities and may be utilized for a range of economic and administrative purposes. “Private data” refers to information stored by private enterprises, non-profit organizations, etc. Big Data has already demonstrated its usefulness and utility in a variety of fields. Indeed, in order for businesses to compete, bankers and managers across all industries must focus on Big Data. Companies may enhance their financial management, cut their cost of capital, and produce profits by thoroughly processing data and evaluating financial information.[5]
Literature Review
The paper discovered that the main problems highlighted fell into the following main groups based on information obtained from the papers: difficulties in obtaining data, lack of funding, guidance, training. Demand for resources far surpasses the availability of resources. When the international component is included, it is evident that large-scale projects are expensive for countries, with policymakers placing proportionally modest expectations on research. It is commonly known that certain publishers charge overpriced rates for their magazines. Motivation and incentives are also challenging. A key issue confronting research is a lack of training and mentorship from older academics. It was noted that many experienced academics who may serve as mentors are too preoccupied with their own work to spend much time interacting with new researchers. Policymakers and stakeholders are typically hesitant or unwilling to make much information publicly available. As a result, writing research without reputable sources is tough.[6]
Big Data in finance is a notion derived from prior publications, with some research published in academic journals. Big Data may aid in the resolution of business challenges and the management of data via the system infrastructure. The most effective firms employ analytics five times more frequently than less effective organizations. By evaluating client data, Big Data may also assist promote B2B sales. Big data sets including information about clients may be utilized to greatly boost sales growth and improve customer relationships. This also applies to market innovation, since there are several possibilities. Big data, its analysis, and uses are markers of a company’s capacity to respond to market opportunities by innovating.[7]
There is a continuing demand for technical innovation in a wide variety of operations in the financial markets. The volume of information and the mechanism of information distribution contribute significantly to the efficiency of financial markets. The consequences of financial big data, for example, are often predicated on specific financial theories. It may also be used for financial market analysis, which is a type of machine learning. Large corporations gain from Big Data because of their economic activities and significant history. Even huge corporations generate more data than smaller businesses. Big data is also useful in corporate finance in a variety of ways, including attracting more financial analysis, lowering a company’s cost of capital, and anticipating investor costs associated with financial actions. This reduces the cost of financing by allowing investors to evaluate more data and allowing huge enterprises to thrive. More data, earnings reports, export market demand statistics, competition performance, and future profits estimates may be processed by financial markets. Investors can lessen uncertainty about the result of their investments by anticipating future returns. The greater the amount of data analyzed, the less uncertainty there is, the lower the premium risk and cost of capital, and the more appealing the venture becomes.
The growth of technology has radically altered the way financial services, particularly those provided by banks and fintech firms, are delivered. Online transactions, financial software, and online banking, in particular, create huge data every day. It is consequently critical to manage this data. This is due to the fact that handling these financial services over the internet has a significant influence on the financial markets. When a corporation collects a big amount of data from many sources, multivariate variables emerge. However, maintaining these big data sets may be challenging, and if not handled appropriately, they can become a burden rather than a benefit. Fintech organizations can evaluate and service more clients in greater detail. We can gain by understanding and forecasting systemic financial risks as well. The issue is that consumers and small businesses do not have direct access to large data. In this situation, they can access Big Data through a variety of data organizations, including consulting firms, relevant government agencies, and relevant private institutions. Big Data is drastically altering financial firms’ business strategies and financial management. Academics and experts in this fascinating field are focusing on big data methods, particularly risk control methods, financial market analysis, the creation of new sentiment indicators in finance from social media, the creation of information tools in various creative ways, and the proposal of new financial business models. The data assists businesses in analyzing the risks they believe are most significant in optimizing earnings. For risk analysis, big data is becoming increasingly relevant. Managing such database poses difficulties. Big Data is used to manage financial datasets in order to split them into distinct risk groups. To produce quicker and more objective estimations, Big Data approaches should constantly be utilized to manage data. The internet is one of the major data platforms. The number of searches for information on search engines increases two weeks before a profit is announced and the volatility of search volumes assists in the forecast of future volatility.[8]
Rapid improvements resulted in a significant rise in the volume of data gathered. The problems of storing, organizing, and comprehending such massive amounts of data have resulted in the creation of new technologies in domains, particularly with engineering and artificial intelligence. Various bodies all across the world must keep track on current and future economic conditions. In order to shape successful policies that improve expansion while maintaining public welfare, policymakers require readily available macroeconomic data. On the other hand, the important economic indicators on which they make their decisions are made seldom and transmitted with significant delays.
With little knowledge, economists can only estimate economic conditions today, in the future, and in the recent past. Economists may now use data science methodologies to tackle any problem. Machine learning algorithms can mine unstructured data for fresh insights. Recent technological advancements have resulted in a huge rise in the number of devices that generate data about human and economic activity. Due to the amount and diversity of such data, obtaining and interpreting is difficult. When individuals and businesses must use financial data, dependability is a big problem. It is critical that such information is kept private and confidentially recorded in a database. Field experts and data analysts must collaborate closely at all levels of the data science chain.
The volume of economic and financial data is increasing at an unprecedented and amazing rate. This massive amount of data is available in complicated, multidimensional formats, necessitating the development of new instruments for economic analysis. Traditional methodologies, in reality, do not scale well to vast. Simple activities become increasingly difficult. In large data contexts, traditional hypothesis testing, should be utilized carefully. It is thus excellent practice for economists to determine what can be treated as a forecasting problem and to expend all statistical and economic effort on complicated structural concerns. Deep learning has resulted in significant advancements in a range of applications where standard machine learning approaches have failed. Deep learning algorithms have the benefit of being able to evaluate data of different character. [9]
The phrase “Big Data” was first used often in session. Simply searching for the keyword big data will reveal that quantity of big data-related publications has expanded dramatically over the previous decade. Because of its long-standing dedication to big data analytics, the financial services industry was analyzed as a particular instance. Data mining and other unstructured analytic approaches, on the other hand, are relatively new in this market and may utilize non-numerical sources. The rise of the industry and its dedication to big data are important motivators for this study.
Finally, there are areas in accounting and financial services where analytics may put one in a better position. A larger picture of big data may be found in the literature, which encompasses data, bringing threats, and possibilities. Visual analytics in management accounting identifies irregular and potentially fraudulent accounts payable activities. Auditors’ roles are expanding these days. Big data management necessitates a number of techniques, including data warehousing and maintaining data privacy while creating and deploying new applications. Big data is extremely promising and advantageous for financial service providers. In financial settings, risk is extremely significant. Big data and associated general analytics are utilized to create risk indicators for portfolios based on genetic algorithms.[10]
Accounting data is a source of financial data. The gathering, storage, processing, transmission, and analysis of accounting data is referred to as accounting data processing. Companies must create new data analysis methods in order to capitalize on big data. This is due to the fact that standard models are best suited for processing structured data rather than big data, which encompasses a wide range of data kinds. As a result, business data management solutions necessitate some data science. Human resources are few in businesses, making it difficult to create new data analysis methods. Financial management principles, functions, modes, and techniques may be utilized to minimize costs, mitigate risks, enhance management efficiency, and boost business value.[11]
Methodology
This was a literature review using a systematic review and analysis of published studies. Data were collected from a variety of national and international sources. Articles related to the title of the review are included in this report. Several publications were removed as a consequence of the secondary evaluation. The remaining full-text publications were read and assessed against the inclusion and exclusion criteria. As a result, the references were submitted to a thorough assessment and analysis. The studies that were possibly suitable for this systematic review were chosen in three steps: title only, abstract only, and publications. Articles chosen should have been authored in English and published in peer-reviewed publications.
Results
The overall problem has a lot to do with resources and their distribution, combined with the fact that the educated population base in developing countries is much smaller than in developed countries. Growing an educated population is a slow process, often taking two or three generations, or even longer. Therefore, in the short term, we need to maximize the use of available scientific human resources. At the graduate level, training abroad is important because many countries do not have adequate training or research programs. The only long-term solution is to improve their status and living conditions in their country of residence. We also need to understand that at least part of the responsibility for getting support and specific funding from the government lies with academia. We should not just rely on them to support us. Members of the government and senior civil servants are, for the most part, not scientists. In order to create an atmosphere of support for scientists, one need to go out to explain the importance of work and what the one want to achieve. Particularly in the pure sciences, research priorities can be dependent on staff availability. In many developing countries, there may not be a wide range of people with equal ability in all areas of science, but there may be individuals or groups who excel in certain areas. Government policies are intended to support such individuals and groups. This is because they are the focal point of growth, providing an opportunity for other activities to be encouraged. In addition, they help to raise the scientific profile of the country abroad. Keeping in touch with the mainstream of ideas and developments is a major challenge for scientists in most developing countries if they are to achieve a high level of scientific excellence. Study visits allow to attend international conferences, keep in touch, observe developments in other countries, and see the relevance and level of work to the excellent work of other countries. This often yields new information and new ideas for further research. Visiting researchers from developed countries can also provide valuable contacts. In particular, they can serve as a catalyst for technology transfer, experienced advice, and new work. Directors of research institutes in developed countries are sometimes reluctant to send their staff abroad because they believe it would be a loss for their own research programs. Some of the research problems in developing countries can only be solved over time, such as hiring more trained personnel, providing additional resources, and strengthening the overall infrastructure of the country. While nothing beats spending money in the long run, there are some areas that can be improved quickly and at relatively low cost, such as improving the relative status of scientists, eliminating unnecessary bureaucracy, and encouraging the importation of scientific materials. In addition to direct funding for specific projects, there will continue to be a need for international support in the form of fellowships, visiting scientists, and facilities for training at reputable centers. In the future, regional collaborative projects may need to be developed if many developing countries wish to participate in the more expensive areas of advanced research.[12]
In our research, issue resolution was linked to the capacity to bring together specialized experts with diverse scientific interests. Furthermore, successful problem solvers came up with answers to situations that were cutting-edge or outside their area of expertise. This demonstrates how openness in research may result in knowledge transfer or transformation from one scientific discipline to another. We also discovered that while addressing diverse issues, problem solvers frequently turn to information from previously produced solutions, indicating that knowledge transfer is a highly successful process. Finally, essential motivation drove effective problem solvers to participate in issue solving initiatives.[13]
Big data sets are also utilized in the financial sector. It has had a significant impact not just on several fields of research and society, but also on the financial sector.[14]
Discussion
We have shown in this study that by opening up contemporary scientific challenges and broadcasting problem information to a varied community of solvers, we may attain effective speed of solution. The most contentious finding was that self-assessment of the gap between a problem and one’s own area of knowledge had a positive and substantial influence on the likelihood of developing a successful solution. As a result, openness and sharing of knowledge about challenges across fields is critical for scientific development. In certain circumstances, it is also beneficial to bring together various issue solvers and have them collaborate to find answers. It is logical to believe that an open source environment with openness, access, and cooperation will produce even better answers in the process of scientific problem solving. Obtaining such open and “external” engagement in scientific issue solving, on the other hand, might be a significant barrier. For fear of revealing their own research programs and activities, many research institutes may be hesitant to disclose issues to outsiders. However, as our findings demonstrate, openness in the process of solving scientific challenges may lead to novel ideas, raise the chance of research program success, and, ultimately, boost research productivity.[15] Furthermore, universities are emphasizing on education rather than research. We know that one of the most essential variables influencing researchers to do more study is motivation. Similarly, other authors underlined the importance of motivation and incentives in encouraging young researchers. Furthermore, research has demonstrated that extrinsic incentive and advancement opportunities have a major influence on research output. According to the findings of this study, one of the primary issues confronting research in many sectors throughout the world is a lack of training, mentorship, and supervision. The data gathering procedure was demanding in terms of organizational hurdles and challenges in acquiring data.[16]
Conclusion
According to the research’s findings, the primary issues experienced by the study include a lack of money, enthusiasm, mentorship, data gathering. Courses, cooperation, and networking to develop research capability were critical. It was also stated that increasing the transparency of the research process was necessary in order to foster great research. Motivation, as well as successful learning and training, must be considered. It should be mentioned that the uncertainty inherent in data sharing may be mitigated by fostering trust across interested parties, which is critical.
Modern technologies are being used by large corporations to satisfy the needs of digital transformation. Data quality and regulatory standards are also seen as critical considerations. Despite the fact that all financial goods and services are completely data-enabled, research on big data and finance has yet to achieve its highpoint. Researchers and financial specialists, in particular, must research and provide acceptable answers to the issues of handling large data. Managing such massive data sets can be costly and problematic in some situations. As a result, future research may concentrate on making massive data sets more accessible to small businesses. Finally, future research should empirically emphasize the rising difficulties of big data in finance already described. As Big Data is a thriving and relatively new study subject, future research should incorporate conference presentations.
Reference:
BIG DATA OPPORTUNITIES FOR ACCOUNTING AND FINANCE PRACTICE AND RESEARCH: BIG DATA IN ACCOUNTING AND FINANCE, SOPHIE COCKCROFT, MARK RUSSELL, FEBRUARY 2018,
BIG DATA, BIG CHANGE: IN THE FINANCIAL MANAGEMENT, MING KE, YUXIN SHI, OPEN JOURNAL OF ACCOUNTING, 2014, 3, 77-82
CHALLENGES CONFRONTING SCIENTIFIC RESEARCH: A SYSTEMATIC REVIEW, DR. ABDULLAH AEDH, DR. NAHID KHALIL ELFAKI, INTERNATIONAL JOURNAL OF TREND IN SCIENTIFIC RESEARCH AND DEVELOPMENT (IJTSRD), MAY-JUN 2019
CURRENT LANDSCAPE AND INFLUENCE OF BIG DATA ON FINANCE, MD. MORSHADUL HASAN, JÓZSEF POPP, JUDIT OLÁH, JOURNAL OF BIG DATA, HTTPS://DOI.ORG/10.1186/S40537-020-00291-Z
DATA SCIENCE TECHNOLOGIES IN ECONOMICS AND FINANCE: A GENTLE WALK-IN, LUCA BARBAGLIA, SERGIO CONSOLI, SEBASTIANO MANZAN, DIEGO, REFORGIATO RECUPERO, MICHAELA SAISANA, AND LUCA TIOZZO PEZZOLI, JUNE 2021
PROBLEMS OF SCIENTIFIC RESEARCH IN DEVELOPING COUNTRIES, BY P.B. VOSE AND A. CERVELLINI, IAEA BULLETIN, VOL.25, NO. 2
THE VALUE OF OPENNESS IN SCIENTIFIC PROBLEM SOLVING, KARIM R. LAKHANI, LARS BO JEPPESEN, PETER A. LOHSE, JILL A. PANETTA, HARVARD BUSINESS SCHOOL, SOLDIERS FIELD, COPENHAGEN BUSINESS SCHOOL, 2007
[1] PROBLEMS OF SCIENTIFIC RESEARCH IN DEVELOPING COUNTRIES, BY P.B. VOSE AND A. CERVELLINI, IAEA BULLETIN, VOL.25, NO. 2
[2] THE VALUE OF OPENNESS IN SCIENTIFIC PROBLEM SOLVING, KARIM R. LAKHANI, LARS BO JEPPESEN, PETER A. LOHSE, JILL A. PANETTA, HARVARD BUSINESS SCHOOL, SOLDIERS FIELD, COPENHAGEN BUSINESS SCHOOL, 2007
[3] THE VALUE OF OPENNESS IN SCIENTIFIC PROBLEM SOLVING, KARIM R. LAKHANI, LARS BO JEPPESEN, PETER A. LOHSE, JILL A. PANETTA, HARVARD BUSINESS SCHOOL, SOLDIERS FIELD, COPENHAGEN BUSINESS SCHOOL, 2007
[4] CURRENT LANDSCAPE AND INFLUENCE OF BIG DATA ON FINANCE, MD. MORSHADUL HASAN, JÓZSEF POPP, JUDIT OLÁH, JOURNAL OF BIG DATA, HTTPS://DOI.ORG/10.1186/S40537-020-00291-Z
[5] BIG DATA, BIG CHANGE: IN THE FINANCIAL MANAGEMENT, MING KE, YUXIN SHI, OPEN JOURNAL OF ACCOUNTING, 2014, 3, 77-82
[6] CHALLENGES CONFRONTING SCIENTIFIC RESEARCH: A SYSTEMATIC REVIEW, DR. ABDULLAH AEDH, DR. NAHID KHALIL ELFAKI, INTERNATIONAL JOURNAL OF TREND IN SCIENTIFIC RESEARCH AND DEVELOPMENT (IJTSRD), MAY-JUN 2019
[7] CURRENT LANDSCAPE AND INFLUENCE OF BIG DATA ON FINANCE, MD. MORSHADUL HASAN, JÓZSEF POPP, JUDIT OLÁH, JOURNAL OF BIG DATA, HTTPS://DOI.ORG/10.1186/S40537-020-00291-Z
[8] CURRENT LANDSCAPE AND INFLUENCE OF BIG DATA ON FINANCE, MD. MORSHADUL HASAN, JÓZSEF POPP, JUDIT OLÁH, JOURNAL OF BIG DATA, HTTPS://DOI.ORG/10.1186/S40537-020-00291-Z
[9] DATA SCIENCE TECHNOLOGIES IN ECONOMICS AND FINANCE: A GENTLE WALK-IN, LUCA BARBAGLIA, SERGIO CONSOLI, SEBASTIANO MANZAN, DIEGO, REFORGIATO RECUPERO, MICHAELA SAISANA, AND LUCA TIOZZO PEZZOLI, JUNE 2021
[10] BIG DATA OPPORTUNITIES FOR ACCOUNTING AND FINANCE PRACTICE AND RESEARCH: BIG DATA IN ACCOUNTING AND FINANCE, SOPHIE COCKCROFT, MARK RUSSELL, FEBRUARY 2018,
[11] BIG DATA, BIG CHANGE: IN THE FINANCIAL MANAGEMENT, MING KE, YUXIN SHI, OPEN JOURNAL OF ACCOUNTING, 2014, 3, 77-82
[12] PROBLEMS OF SCIENTIFIC RESEARCH IN DEVELOPING COUNTRIES, BY P.B. VOSE AND A. CERVELLINI, IAEA BULLETIN, VOL.25, NO. 2
[13] THE VALUE OF OPENNESS IN SCIENTIFIC PROBLEM SOLVING, KARIM R. LAKHANI, LARS BO JEPPESEN, PETER A. LOHSE, JILL A. PANETTA, HARVARD BUSINESS SCHOOL, SOLDIERS FIELD, COPENHAGEN BUSINESS SCHOOL, 2007
[14] CURRENT LANDSCAPE AND INFLUENCE OF BIG DATA ON FINANCE, MD. MORSHADUL HASAN, JÓZSEF POPP, JUDIT OLÁH, JOURNAL OF BIG DATA, HTTPS://DOI.ORG/10.1186/S40537-020-00291-Z
[15] THE VALUE OF OPENNESS IN SCIENTIFIC PROBLEM SOLVING, KARIM R. LAKHANI, LARS BO JEPPESEN, PETER A. LOHSE, JILL A. PANETTA, HARVARD BUSINESS SCHOOL, SOLDIERS FIELD, COPENHAGEN BUSINESS SCHOOL, 2007
[16] CHALLENGES CONFRONTING SCIENTIFIC RESEARCH: A SYSTEMATIC REVIEW, DR. ABDULLAH AEDH, DR. NAHID KHALIL ELFAKI, INTERNATIONAL JOURNAL OF TREND IN SCIENTIFIC RESEARCH AND DEVELOPMENT (IJTSRD), MAY-JUN 2019,