Journal articles: 'Financial intelligence tools'

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Mishra, Shrutika. "Financial management and forecasting using business intelligence and big data analytic tools." International Journal of Financial Engineering 05, no. 02 (June 2018): 1850011. http://dx.doi.org/10.1142/s2424786318500111.

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This paper discusses about the latest and efficient financial tools and techniques which optimize the financial cost of the organization and predict the financial situation of the organization to flourish the business in the efficient way. Financial management basically means to accumulate funding for the enterprise at a low cost and to expend this collected funding for earning extreme profits. Thus, financial management means to plan and control the finance of the company. Financial management is usually concerned with the flow and control of money within an organization and be it either reserved or open sector. So, these tools if functional in the better way then business will reach in the utmost altitude. Recently some computational tools have been developed by the computer scientist for the efficient management and prediction for the business which is very useful for forecast and prediction in the era of digital world. The intention of this paper is to review and discuss the most significant applications of Business Intelligence in financial management and forecasting and prediction domain as well as point to new technology trends that will affect financial development.

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Mhlanga, David. "Industry 4.0 in Finance: The Impact of Artificial Intelligence (AI) on Digital Financial Inclusion." International Journal of Financial Studies 8, no. 3 (July 28, 2020): 45. http://dx.doi.org/10.3390/ijfs8030045.

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This study sought to investigate the impact of AI on digital financial inclusion. Digital financial inclusion is becoming central in the debate on how to ensure that people who are at the lower levels of the pyramid become financially active. Fintech companies are using AI and its various applications to ensure that the goal of digital financial inclusion is realized that is to ensure that low-income earners, the poor, women, youths, small businesses participate in the mainstream financial market. This study used conceptual and documentary analysis of peer-reviewed journals, reports and other authoritative documents on AI and digital financial inclusion to assess the impact of AI on digital financial inclusion. The present study discovered that AI has a strong influence on digital financial inclusion in areas related to risk detection, measurement and management, addressing the problem of information asymmetry, availing customer support and helpdesk through chatbots and fraud detection and cybersecurity. Therefore, it is recommended that financial institutions and non-financial institutions and governments across the world adopt and scale up the use of AI tools and applications as they present benefits in the quest to ensure that the vulnerable groups of people who are not financially active do participate in the formal financial market with minimum challenges and maximum benefits.

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Kayım, Furkan, and Atınç Yılmaz. "Financial Instrument Forecast with Artificial Intelligence." EMAJ: Emerging Markets Journal 11, no. 2 (December 13, 2021): 16–24. http://dx.doi.org/10.5195/emaj.2021.229.

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In ancient times, trade was carried out by barter. With the use of money and similar means, the concept of financial instruments emerged. Financial instruments are tools and documents used in the economy. Financial instruments can be foreign exchange rates, securities, crypto currency, index and funds. There are many methods used in financial instrument forecast. These methods include technical analysis methods, basic analysis methods, forecasts carried out using variables and formulas, time-series algorithms and artificial intelligence algorithms. Within the scope of this study, the importance of the use of artificial intelligence algorithms in the financial instrument forecast is studied. Since financial instruments are used as a means of investment and trade by all sections of the society, namely individuals, families, institutions, and states, it is highly important to know about their future. Financial instrument forecast can bring about profitability such as increased income welfare, more economical adjustment of maturities, creation of large finances, minimization of risks, spreading of ownership to the grassroots, and more balanced income distribution. Within the scope of this study, financial instrument forecast is carried out by applying a new methods of Long Short Term Memory (LSTM), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Autoregressive Integrated Moving Average (ARIMA) algorithms and Ensemble Classification Boosting Method. Financial instrument forecast is carried out by creating a network compromising LSTM and RNN algorithm, an LSTM layer, and an RNN output layer. With the ensemble classification boosting method, a new method that gives a more successful result compared to the other algorithm forecast results was applied. At the conclusion of the study, alternative algorithm forecast results were competed against each other and the algorithm that gave the most successful forecast was suggested. The success rate of the forecast results was increased by comparing the results with different time intervals and training data sets. Furthermore, a new method was developed using the ensemble classification boosting method, and this method yielded a more successful result than the most successful algorithm result.

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Dalip, Andrew. "Intelligence and Corruption." Journal of Intelligence, Conflict, and Warfare 3, no. 3 (January 31, 2021): 34–54. http://dx.doi.org/10.21810/jicw.v3i3.2516.

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The Journal of Intelligence, Conflict, and Warfare is pleased to publish the following thought piece from one of our esteemed Speakers from the 2020 West Coast Security Conference. The author, Mr. Dalip, is a lawyer working in the financial crime and corruption sphere. From 2015 to 2018, Mr. Dalip was a chairman at the Steering Group Planning Committee for the Caribbean Financial Action Task Force (CFATF); and from 2014 to 2018, he was a special legal advisor to the Ministry of Attorney General Trinidad and Tobago. The intersection between corruption and intelligence is gaining increased focus. Foreign intelligence services have an anti-corruption role at the strategic level through Intelligence Risk Assessments and at the operational level during post-conflict operations. Intelligence assessments of the effectiveness of non-kinetic tools on target countries also guide implementation and policy changes. The roles of security intelligence and foreign intelligence services are, however, no longer always discrete, particularly in the context of non-state actors. Foreign intelligence services would benefit from the skill sets of security intelligence agencies in detecting corruption related predicate offences, both in performing their core roles and supporting law enforcement operations. This includes the use of financial intelligence as well as other key open source intelligence resulting from anti-money laundering frameworks, the development of which has been driven globally by the Financial Action Task Force. In performing these roles, intelligence agencies must also be mindful of their own vulnerability to corruption.

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Mohapatra, Badri Narayan, Bhagwat Nagargoje, Prajwal Zurunge, and Suraj More. "ARTIFICIAL INTELLIGENCE IN STOCK MARKET INVESTMENT." Journal of Engineering Science 28, no. 3 (September 2021): 96–100. http://dx.doi.org/10.52326/jes.utm.2021.28(3).08.

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This study investigates the selection of stock from huge stock markets and by using good selection tools so that it will give a good return value. It helps investor to find an easy decision regarding their investment in stock market individually with effective collection of trading activities. Many artificial intelligence (AI) techniques are untested in the financial crisis scenario. This research really helpful to the investor in the stock selection and stock purchase decision. AI is also a one of the hottest topic for most industries, researchers and investors. The financial market is easy to analyze with multiple charts, due to the application of artificial intelligence.

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Salim Madhi, Mohammed, Abbas Ali Mohammed, Shaalan Shyaa Mayea, Krar Muhsin Thajil, Saadulldeen Ali Hussein, and Ali Salah Hasan. "THE ROLE OF ARTIFICIAL INTELLIGENCE IN IMPROVING THE FINANCIAL EFFICIENCY OF BANKS: AN APPLIED STUDY OF A SAMPLE OF INDIVIDUALS WORKING AT AL-RAFIDAIN AND ALRASHEED BANK IN DHIQAR." INTERNATIONAL JOURNAL OF RESEARCH IN SOCIAL SCIENCES & HUMANITIES 12, no. 04 (2022): 991–1010. http://dx.doi.org/10.37648/ijrssh.v12i04.052.

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This research aims to identify the strengths of artificial intelligence, which appear through the adoption of its tools and its role in improving the financial efficiency of government banks in Iraq. As the presence of artificial intelligence is one of the most important components of banks in the course of development, allowing them the ability to optimize their financial efficiency, and in light of the uncertain conditions experienced by organizations, the presence of artificial intelligence is expected to have a prominent role in improving the financial efficiency of banks. Government at present. The conceptual framework of the current study was built on two main variables: artificial intelligence as an independent variable, financial efficiency as a dependent variable. The main question of the study was formulated as follows: "What is the role of artificial intelligence in financial efficiency in the Iraqi banking sector? What is the role of digital transformation in that relationship?" This study was applied in the governmental banking sector in Iraq in Al-Rafidain and Al-Rasheed Banks in DhiQar and their subsidiaries.

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Asandimitra, Nadia, and Achmad Kautsar. "THE INFLUENCE OF FINANCIAL INFORMATION, FINANCIAL SELF EFFICACY, AND EMOTIONAL INTELLIGENCE TO FINANCIAL MANAGEMENT BEHAVIOR OF FEMALE LECTURER." Humanities & Social Sciences Reviews 7, no. 6 (January 7, 2020): 1112–24. http://dx.doi.org/10.18510/hssr.2019.76160.

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Purpose of the study: The purpose of the study was to compare the financial information, financial self-efficacy and emotional intelligence on the financial management of women lecturer in state and private university. Methodology: This study was designed as a conclusive causality study. The study population was female lectures of state and private universities in Indonesia. From the population, there are two hundred (200) female lectures from a state university and private universities have selected as a sample of study by quota sampling method. The data collection techniques used in this research are interviews and surveys. Multiple regressions was chosen to get results with the SPSS tools. Main Findings: There is an influence of financial knowledge, financial self-efficacy, financial literacy, and emotional intelligence to the financial management behavior of female lecturers at state universities while there is no influence of financial attitude, financial literacy, and emotional intelligence to the financial management [behavior] of female university lecturers in private universities. Applications of this study: The results of this study will be beneficial for financial institutions and governments that usually hold education and training programs for their customers to increase financial knowledge so as to increase the confidence of their customers (including lecturers) in their ability to manage finance. Furthermore, this knowledge will be conveyed back to the students of the lecturer in the learning process about finance, so that it will indirectly increase the financial literacy of their students and society at large. Novelty/Originality of this study: Many researches about financial behavior topics have analyzed financial information factors’ influence on financial management behavior, but few of them have included psychological factors such as financial self-efficacy and emotional intelligence. This distinguishes this research compared to other studies of financial behavior as it analyzes the two effects of psychological factors on financial management behavior. Another novelty of this study is the selection of female lecturer as research object as their characteristic as well-informed and well-educated about financial management that has not observed by previous studies.

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Amini, Mehran, Sara Salimi, Farid Yousefinejad, Mohammad J. Tarokh, and Sayyed M. Haybatollahi. "The implication of business intelligence in risk management: a case study in agricultural insurance." Journal of Data, Information and Management 3, no. 2 (May 22, 2021): 155–66. http://dx.doi.org/10.1007/s42488-021-00050-6.

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AbstractThe increasing data scales in today’s business sectors coupled with the necessity of risk management raise the importance of business intelligence tools as an integrated solution for the insurance industry. These tools have mostly been used to achieve effective risk management. Although methods of risk management in the insurance industry have been proposed many years ago, the research effort has primarily been focused on predictive analyses. This study aimed to investigate the role of business intelligence as a solution to illustrate its potential in risk management particularly for decision-makers in agricultural insurance. We hypothesized that this would make a preferable decision in uncertain conditions. Sample data from the online transaction process system of Iran agricultural insurance fund were preprocessed in SQL server. Multidimensional online analytical processing architecture was analyzed using Targit business intelligence tool. Our results identified financial risks that lead to a framework of controlling risk based on business intelligence in the agricultural insurance fund.

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Butenko, E. D. "Artificial intelligence in banks today: Experience and perspectives." Digest Finance 25, no. 2 (June 29, 2020): 230–42. http://dx.doi.org/10.24891/df.25.2.230.

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Subject. This article discusses the application of tools of information processing, introduction of modern technologies, including tools of digital transformation, which today is of current importance for the financial sector. Objectives. The article aims to investigate the problems of application of artificial intelligence systems, real and virtual worlds pairing. Methods. For the study, I used the methods of logical and statistical analyses. Results In this article, I present my own scheme of multilevel structure of application of artificial intelligence systems in banks today, tomorrow and in the long-term perspective. Conclusions. Artificial intelligence systems in business will lead to fundamental changes in customer service and radical improvement of business efficiency through the use of modern technologies.

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Prigoda, L. V., M. V. Alikaeva, and Z. Cekerevac. "Banking ecosystems and marketplaces: digitalization trends." New Technologies 16, no. 6 (February 20, 2021): 132–38. http://dx.doi.org/10.47370/2072-0920-2020-16-6-132-138.

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The article considers peculiarities of the activities of the main participants in the financial market, in particular banking in the context of digitalization and the introduction of artificial intelligence tools. At present, artificial intelligence (AI) technologies have a significant impact on human life, both in the process of instant transfers and in conversational interfaces. This affects the financial services sector, and its members are the most active in introducing disruptive AI innovations. Therefore, in order to increase the level of competitiveness, modern banks should act as locomotives in addressing issues of implementation, use of digital technologies and acceleration of methods of remote work. The COVID-19 pandemic has made its own adjustments to the concept of interaction of financial institutions with customers, for most of whom remote services have become an integral part of everyday life. The increasing demand for telecommuting services of financial institutions stimulates the creation of digital platforms that take into account both the processes of global digitalization and the changed demands of consumers in the context of a pandemic. This article provides an analytical overview of trends, obstacles and prospects for the integration of financial ecosystems and marketplaces in the Russian market. The necessity of using an integrated approach in developing the rules for the functioning of financial ecosystems in the formation of an adequate development strategy, which will ensure the creation of a fair competitive environment in the financial market, has been substantiated. The aim of the research is to identify the main trends and patterns in the financial services market, as well as to determine the vector for further development of financial ecosystems formed using artificial intelligence tools. To achieve this goal such general scientific methods as theoretical generalization, analysis and synthesis, comparative analysis, systems approach, etc. have been used.

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Baranes, Amos, Rimona Palas, Eli Shnaider, and Arthur Yosef. "Identifying financial ratios associated with companies’ performance using fuzzy logic tools." Journal of Intelligent & Fuzzy Systems 40, no. 1 (January 4, 2021): 117–29. http://dx.doi.org/10.3233/jifs-190109.

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This study introduces computerized model for evaluation of corporate performance for companies traded in the main world stock markets. The main contribution of this study is to utilize a “Soft Regression” modeling tool, which is a soft computing tool based on fuzzy logic in financial statement analysis. Specifically, the tool is used to identify the most important financial ratios explaining the performance (as reflected by Operating Income Margin) of publicly traded companies, belonging to the manufacturing industries 2000–3999. We used data extracted from the XBRL database for years 2012 to 2016. The main results and conclusions of the study are: 1. The study identified relevant financial ratios for the manufacturing industry. It also revealed the relative importance of the various categories of financial ratios. 2. Detailed comparison of the results for 2012 and for 2016 indicated high degree of consistency and stability over time. 3. Not all financial ratios are equally relevant for all industries. 4. Proxy variables belonging to the same category of financial ratios are interchangeable in our model. It does not matter, which of the ratios belonging to the same category are used, the results are very similar for both, 2012 and for 2016. 5. All the resulting indicators imply that the model is highly reliable and robust. The main contribution of this study is to present a soft computing modeling tool based on fuzzy logic which is intuitive, stable and not based on restrictive assumptions.

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Sharbek, Nermin. "How Traditional Financial Institutions have adapted to Artificial Intelligence, Machine Learning and FinTech?" Proceedings of the International Conference on Business Excellence 16, no. 1 (August 1, 2022): 837–48. http://dx.doi.org/10.2478/picbe-2022-0078.

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Abstract This article analyzes the implications of the financial sector’s recent adoption of artificial intelligence (AI) and machine learning (ML). It identifies the advantages of these technologies in terms of fraud protection, cost savings, and efficiency, while also highlighting worries about conventional financial institutions’, such as banks, insurance and reinsurance institutions, and inability to compete with Fintech firms. The report contributes to the conversation about the influence of modern technology by distilling and classifying the tools deployed in established conventional institutions that resulted in continual development and simplification of internal procedures as well as client service delivery. Given the increased competition in the financial industry, Fintech businesses are critical for conventional financial institutions to stay afloat in today’s changing world. The study is directed at researchers who are still in the early phases of investigating the artificial intelligence field in the financial sector. The study reviews prior research that documents the changes occurring inside financial institutions from a global perspective as a result of Fintech and the integration of new technologies. The goals of the paper are fulfilled by (1) furthering theoretical studies on the issue, (2) increasing awareness of the financial industry’s developments, and (3) gathering proof of the influence artificial intelligence and machine learning have had so far.

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Budree, Adheesh, and Bonginkosi P. Gina. "Factors That Drive the Selection of Business Intelligence Tools in South African Financial Services Providers." International Journal of Business Intelligence and Data Mining 1, no. 1 (2023): 1. http://dx.doi.org/10.1504/ijbidm.2023.10044714.

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Ferranti, J. M., M. K. Langman, D. Tanaka, J. McCall, and A. Ahmad. "Bridging the gap: leveraging business intelligence tools in support of patient safety and financial effectiveness." Journal of the American Medical Informatics Association 17, no. 2 (February 26, 2010): 136–43. http://dx.doi.org/10.1136/jamia.2009.002220.

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Childs, Susan. "Financial Controls in the Healthcare Practice: Using Emotional Intelligence to Trust and Verify." Healthcare Administration Leadership & Management Journal 1, no. 1 (February 8, 2023): 28–31. http://dx.doi.org/10.55834/halmj.2735548074.

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While a physician’s priority is always patient care, it is just as important to keep your finger on the pulse of the practice’s financial activities. There are benchmarks and key performance indicators to monitor and paying attention to the reports from the practice management system, will provide tools to assess the practice’s current and future success. Vulnerability results from too much trust. This article discusses the use of emotional intelligence for physicians and the administrative staff to help mitigate financial risk in the healthcare practice.

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Sahrom Abu, Md, Siti Rahayu Selamat, Aswami Ariffin, and Robiah Yusof. "Cyber Threat Intelligence – Issue and Challenges." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 1 (April 1, 2018): 371. http://dx.doi.org/10.11591/ijeecs.v10.i1.pp371-379.

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Today threat landscape evolving at the rapid rate with many organization continuously face complex and malicious cyber threats. Cybercriminal equipped by better skill, organized and well-funded than before. Cyber Threat Intelligence (CTI) has become a hot topic and being under consideration for many organization to counter the rise of cyber-attacks. The aim of this paper is to review the existing research related to CTI. Through the literature review process, the most basic question of what CTI is examines by comparing existing definitions to find common ground or disagreements. It is found that both organization and vendors lack a complete understanding of what information is considered to be CTI, hence more research is needed in order to define CTI. This paper also identified current CTI product and services that include threat intelligence data feeds, threat intelligence standards and tools that being used in CTI. There is an effort by specific industry to shared only relevance threat intelligence data feeds such as Financial Services Information Sharing and Analysis Center (FS-ISAC) that collaborate on critical security threats facing by global financial services sector only. While research and development center such as MITRE working in developing a standards format (e.g.; STIX, TAXII, CybOX) for threat intelligence sharing to solve interoperability issue between threat sharing peers. Based on the review for CTI definition, standards and tools, this paper identifies four research challenges in cyber threat intelligence and analyses contemporary work carried out in each. With an organization flooded with voluminous of threat data, the requirement for qualified threat data analyst to fully utilize CTI and turn the data into actionable intelligence become more important than ever. The data quality is not a new issue but with the growing adoption of CTI, further research in this area is needed.

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Leonel, Laís Domingues, Mateus Henrique Balan, Dorel Soares Ramos, Erik Eduardo Rego, and Rodrigo Ferreira de Mello. "Financial Risk Control of Hydro Generation Systems through Market Intelligence and Stochastic Optimization." Energies 14, no. 19 (October 5, 2021): 6368. http://dx.doi.org/10.3390/en14196368.

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In the competitive electricity wholesale market, decisions regarding hydro generators are generally made under uncertain conditions, such as pool price, hydrological affluence, and other players’ strategies. From this perspective, this work presents a computational model formulation with associated market intelligence and game theory tools to support a decision-making process in a competitive environment. The idea behind using a market intelligence tool is to apply a stochastic optimization model with an associated conditional value at risk metric defining a utility function, which calculates the weight that the agents attribute to each stochastic variable associated with the problem to be faced. Subsequently, this utility function is used to emulate the other agents’ strategies based on their previous decisions. The final step finds the Nash equilibrium solution between a player and their competitors. The methodology is applied to the monthly allocation of firm energy by hydro generators under the current Brazilian regulatory framework. The results show a change in the generators’ behavior over the years, from risk-neutral agents seeking to maximize their return with 88% of decisions based on spot price forecasts in 2015, to risk-averse agents with 100% of decisions following a factor that is directly impacted by the hydrological affluence forecasts in 2018.

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Zalieckaitė, Laima, and Indrė Miniotaitė. "Ataskaitų generavimo priemonių taikymas bankuose." Informacijos mokslai 58 (January 1, 2011): 110–25. http://dx.doi.org/10.15388/im.2011.0.3119.

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Besikeičianti ir sudėtinga išorinė verslo aplinka organizacijas, iš jų ir bankus, verčia aktyviai ir sparčiai reaguoti į rinkos pokyčius, ieškoti naujų galimybių, o tam reikalinga kokybiška ir operatyvi informacija, efektyvios rezultatų pateikimo ir analizės priemonės. Dinamiška rinkos situacija verčia bankus efektyviai valdyti ir vertinti rinkos riziką, t. y. turėti priemonių, leidžiančių įvairiais pjūviais analizuoti informaciją ir valdyti finansinės veiklos rodiklius. Maža to, valdoma įvairialypės informacijos šaltinių gausa lemia, kad duomenys tampa prieštaringi ir netikslūs. Šiame straipsnyje analizuojamas verslo įžvalgos technologijų sudedamosios dalies – ataskaitų generavimo priemonių, kurios padeda priimti efektyvius sprendimus ir sėkmingai spręsti iškylančias verslo problemas, taikymas bankuose.Pagrindiniai žodžiai: verslo įžvalgos technologijos, ataskaitų generavimo priemonės, ekspertinių sistemų technologija, verslo įžvalgos sistemos.Application of Report generation tools to banksLaima Zalieckaitė, Indrė Miniotaitė SummaryThe dynamic market situation is forcing banks to effectively manage and assess market risks, to have means to analyze various aspects of information management and financial performance, considering that the diverse sources of information, results, data are contradictory and inaccurate.The business intelligence technology is a tool that can help solve these problems. The report generation process is a very important tool for business intelligence technology. Bank managers of all levels rely on information in the reports when making business analysis and reports.The report generation process, changes in factor analysis show that a new approach to the environment of the bank accounts of the process generated, is qualitative changes and their implementation measures.Change is largely due to banks’ external environment and internal environment changes in the bank. Banks need a single universal set of data with all employees to analyze the data it required a cut. Unified data environment would allow a precise and clear definition of the data, and not to misinterpretation. Business intelligence systems based on the data storage, installation, and require the bank accounts of the generation business intelligence tools for installation. This would allow existing data to discover new patterns and possibilities to improve their performance.Following a thorough analysis of the reporting and assessment tools, these systems are classified in many ways, this paper reports the authors consists of generating the classification method. Summary of the classification method and the bank employees’ needs, the article defines the generation of reports should be supported by business intelligence system environment.As the report generation tools support a wide business intelligence systems (packages) spectrum are organizations, including banks, faced with problems when buying business intelligence systems. This article is for business intelligence systems, the selection criteria for banks in the environment, a bank employee needs and business intelligence systems vendor survey.According to the criteria of the banks analyzed and most suitable for the SAP BusinessObjects platform. However, to successfully implement a business intelligence system should be to determine which report generation tools will be implemented SAP BusinessObjects environment. It was therefore carried out a reporting tool features SAP BusinessObjects environment analysis and report generation steps up development bank model.Keywords: report generation tools, expert systems’ technology business intelligence systems.

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Amirzadeh, Rasoul, Asef Nazari, and Dhananjay Thiruvady. "Applying Artificial Intelligence in Cryptocurrency Markets: A Survey." Algorithms 15, no. 11 (November 14, 2022): 428. http://dx.doi.org/10.3390/a15110428.

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The total capital in cryptocurrency markets is around two trillion dollars in 2022, which is almost the same as Apple’s market capitalisation at the same time. Increasingly, cryptocurrencies have become established in financial markets with an enormous number of transactions and trades happening every day. Similar to other financial systems, price prediction is one of the main challenges in cryptocurrency trading. Therefore, the application of artificial intelligence, as one of the tools of prediction, has emerged as a recently popular subject of investigation in the cryptocurrency domain. Since machine learning models, as opposed to traditional financial models, demonstrate satisfactory performance in quantitative finance, they seem ideal for coping with the price prediction problem in the complex and volatile cryptocurrency market. There have been several studies that have focused on applying machine learning for price and movement prediction and portfolio management in cryptocurrency markets, though these methods and models are in their early stages. This survey paper aims to review the current research trends in applications of supervised and reinforcement learning models in cryptocurrency price prediction. This study also highlights potential research gaps and possible areas for improvement. In addition, it emphasises potential challenges and research directions that will be of interest in the artificial intelligence and machine learning communities focusing on cryptocurrencies.

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Kekytė, Ieva, and Viktorija Stasytytė. "Comparative Analysis of Investment Decision Models." Mokslas - Lietuvos ateitis 9, no. 2 (June 2, 2017): 197–208. http://dx.doi.org/10.3846/mla.2017.1023.

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Rapid development of financial markets resulted new challenges for both investors and investment issues. This increased demand for innovative, modern investment and portfolio management decisions adequate for market conditions. Financial market receives special attention, creating new models, includes financial risk management and investment decision support systems.Researchers recognize the need to deal with financial problems using models consistent with the reality and based on sophisticated quantitative analysis technique. Thus, role mathematical modeling in finance becomes important. This article deals with various investments decision-making models, which include forecasting, optimization, stochatic processes, artificial intelligence, etc., and become useful tools for investment decisions.

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Manta, Otilia. "Financing and Fiscality in the Context of Artificial Intelligence at the Global Level." European Journal of Marketing and Economics 3, no. 1 (January 1, 2020): 31. http://dx.doi.org/10.26417/ejme.v3i1.p31-47.

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The current financing models, as well as the fiscal models, are based on the current resources available at both the financial system and the fiscal system, but in close interdependence with those existing at the global level, the technology being one of them. Moreover, we consider that increasingly in the resource hierarchy, the place of the human factor is replaced by artificial intelligence (regardless of whether we are talking about industrial robots or intelligent technologies as is the case in the banking financial field). The new ways of approaching and coordinating finances aim to increase the degree of flexibility of financial networks and harmonize the results of those financial institutions that master and use complex but complementary technologies in order to obtain a final product or services optimal and with direct connection to its beneficiary. The defining elements for any financing and control model, regardless of whether we think of Fintech or other programs such as Fiscalis , are given by the following characteristics: digitization (artificial intelligence tools are crucial for digitizing financial services and fiscal), mobilization (virtual space offers not only the possibility but especially the platform for achieving the mobility of services), disintermediation (virtual space offers the possibility of direct access without intermediaries) and automation (through the financial services existing on the online platforms, the beneficiary of the service and the service provider optimizes its time and cost in favor of making the service profitable).

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Manjaly, Joel, Ranjana Mary Varghese, and Philip Varughese. "Artificial Intelligence in the Banking Sector – A Critical Analysis." Shanlax International Journal of Management 8, S1-Feb (February 26, 2021): 210–16. http://dx.doi.org/10.34293/management.v8is1-feb.3778.

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“Computers will overtake humans with AI in the next 100 years. When that happens, we need to make sure the computers have goals aligned with ours.” The pros and cons of AI is evident in this statement made by Stephen Hawking.The last decade had witnessed tremendous changes in how each industry functions. The rapid growth of technology, internet and infrastructure has fuelled this disruption at a 10X speed.Talk of the town in digital disruption is Artificial Intelligence. Number of mentions of AI or Machine Learning in earning calls by public company executives shows an exponentially rising trend since 2015 as per the data by CBInsights. AI has brought in groundbreaking changes in the global banking industry. The future of AI in banking is enormous as the power of advanced data analytics can combat fraudulent bank transactions and improve compliance. AI technologies reduce costs in the banking sector by increasing productivity. According to Open Text survey of financial services professionals, 80% of banks are highly aware about the potential benefits that AI can bring to the business. What are the potential benefits of AI in financial institutions? Does adopting AI come with risks and costs? What are the regulatory constraints which could be the impediments for implementing AI in the Banking sector? This conceptual paper deals with risks, rewards, use cases and ways to adopt AI in the banking sector. This article also tries to identify the paybacks and also the key uses of some of the tools which are used by both financial institutions and central banks. It also indicates the main constraints of the technology and its likely consequences for the correct functioning of the financial system.

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Saymeh, Abdulaziz, Harbi Arikat, Firas Hashem, and Ashraf Al-khalieh. "Intellectual Capital Effectiveness of Jordanian Banks Financial Performance." WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS 18 (March 16, 2021): 552–68. http://dx.doi.org/10.37394/23207.2021.18.56.

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Subject research intended to realize the effect of intellectual capital on the outcomes of Jordan’s banks listed on Amman Stock Exchange (ASE). Researchers had relyed on Pulic’s model to realize the reaserch objectives, the researchers had analyzed the banks’ historical financial statements. The study group consisted of all the banks listed on ASE for (2012-2018) period. Researchers had used the descriptive statistics and the basic fundamental analysese tools to mesure the effect of ideological capital as well as financial intelligence on the financial performance of sample banks [1]. This research had revealed a statistically significant positive effect of intellectual capital on the performance of the sample banks represented by the return on assets, while the research indicated that there was no significant effect of intellectual capital on the assets returns of ASE banks

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Marin, Rafael, and Pollyana Notargiacomo. "Multiagent Intelligent Tutoring System for Financial Literacy." International Journal of Web-Based Learning and Teaching Technologies 17, no. 7 (November 2022): 1–13. http://dx.doi.org/10.4018/ijwltt.288035.

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Financial literacy is a theme that integrates public policies for the social development of a country and an element to be worked on from different aspects to improve people’s living standards, providing well-being. In this context, Stima is proposed, a system capable of acquiring knowledge from experts and tutors to help students to follow the bases of financial planning and decision-making. It aims to relate different approaches to artificial intelligence and institute a language that, through syntactic, lexical, and semantic analyzes, executed by different agents within the model, makes it possible to define financial profiles and recommend financial planning. The knowledge stored is used to propose financial monitoring standards and provides tools to assist financial decision-making. A set of eight profiles, with three indicators each, was configured by experts inside a prototype, and a volunteer student was accompanied for four months giving validations to the system.

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Khanchel, Hanen, Mohamed Afif Lakhoua, and Karim Ben Kahla. "Operation of the Information Monitoring System for the Optimization of the Processing Time and the Dissemination of Financial Data." Business and Management Research 7, no. 3 (September 18, 2018): 1. http://dx.doi.org/10.5430/bmr.v7n3p1.

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In this paper, we propose the concept of "information monitoring system "; we indicate its usefulness in the process of informative anticipatory system, in the face of the problem of financial information overload caused by the use of ICT. We present a grid of analysis of the accounting process in an indebted company. The concept is useful when the information intelligence is oriented "exploitation of the information anticipatory" for the anticipation, these being embedded in large financial data. Our experimentation on the problem studied will show us that these tools are effective to optimize the time of processing and dissemination of financial information for decision-making.

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Laktionova, O. E. "Business analytics in managing the links of the financial system in the digital economy." Financial Analytics: Science and Experience 13, no. 4 (November 13, 2020): 414–29. http://dx.doi.org/10.24891/fa.13.4.414.

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Subject. The introduction of projects for digital economy development necessitates the improvement of the management efficiency of various elements of the financial system. Objectives. The aim of the study is to justify the need to use business analytics tools in managing the financial system in the digital economy, to show that in the model of financial outsourcing it is possible to apply the tools of predicative analysis, which is performed based on digital analytics platforms. Methods. The study rests on the use of one of the methods of predicative analysis, i.e. the method of correlation analysis. Results. Business analytics tools enable to unveil the most significant factors in the development of the income part of local budgets. The correlation matrix obtained through the analytical digital platform demonstrate that the strongest correlation in the income part of local budgets belongs not only to tax revenues, but also to inter-budget transfers. Business analytics tools help provide recommendations for increasing the amount of tax revenues to local budgets. The management cost can be reduced by using financial and tax outsourcing models. The paper shows that the use of modern predicative analysis tools will increase the management efficiency and enhance the effect of indirect financing by reducing the cost of tax administration. Conclusions. To accelerate the transition to the digital economy and to the digital region, it is important to actively use business analytics techniques and the methodology for modeling the innovative intelligent systems for decision-making, including the use of artificial intelligence tools.

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Kirillova, Elena Anatolyevna, Varvara Vladimirovna Bogdan, Elena Victorovna Blinkova, Teymur Zulfugarzade, and Kseniya Vasilyevna Yunusova. "The Main Features of the Use of Digital Technologies in the Financial and Banking Sector." Webology 18, Special Issue 04 (September 30, 2021): 1326–41. http://dx.doi.org/10.14704/web/v18si04/web18201.

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The relevance of the research is dictated by the introduction of innovations in banking operations. Classical financial and credit institutions are transformed into high-tech platforms that can create new profit algorithms, using artificial intelligence, Big Data technology, and a global information base. The purpose of this study is to analyze the demand for digital services in the banking sector by customers and to propose criteria for determining the degree of digitalization of banks. The statistical method and the method of evaluating the activities of organizations taking into account the fact of digitalization have been used. To obtain objective results, various tools have been used to analyze the information space of the Internet: tools for analyzing search queries "Google Trends" and "Yandex Wordstat" to determine the relevance of providing digital services to customers. The technical part of the study, which is directly related to obtaining information from the Internet using both software tools and "manually", was conducted in the period from 2016 to 2021. The results of the study show the connection between the introduction of innovations and the reform of the financial and banking sector. The data was collected from 150 respondents who are experts in the implementation of digital technologies – artificial intelligence, Big Data, blockchain in the field of financial activity. A confirmatory analysis has been conducted to assess the reliability and validity of the digital technologies used in the financial and banking sector. The study proposes a method for assessing the degree of digitalization of banks: by the level of automation of the main processes; by the number of services provided online; by the speed of operations; by the availability of online services around the clock; by the range of digital technologies used; by the volume of online sales and the volume of transactions conducted using digital technologies.

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Mitrache, Gabriel Razvan. "Big data and AI: a potential solution to end the conundrum of too big to fail financial institutions?" Proceedings of the International Conference on Business Excellence 15, no. 1 (December 1, 2021): 317–27. http://dx.doi.org/10.2478/picbe-2021-0030.

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Abstract The “too big to fail” institutions are a widespread concern, especially in the financial world. Their failure can create severe economic downturns and social turmoil. In past bank failures, governments intervened with public funds to save such institutions from collapse to avoid economic downturns. Since, measures have been put in place to prevent bank failures and limit the utilisation of public funds. However, failures cannot be prevented and risks of affecting the economy are always present in the case of too big to fail institutions. This article explores the possibilities offered by recent advancements in the fields of Big Data and Artificial Intelligence, widely implemented by the financial institutions themselves, as tools to be used by authorities in ending the too big to fail conundrum. The adequate implementation of these technological capabilities will contribute to the areas already targeted by governments – reducing the probability of failure and providing tools to limit negative externalities and spillover effects – and will also introduce a new capability that could address the too big to fail matter. Since financial institutions are, in their essence, data hubs, now in a digitalised format, the possibilities to automate tasks and provide insight for decisions should address the issue. The actual transfer of assets and liabilities to institutions that can carry on the activity, currently need years to be handle:. Big Data and Artificial Intelligence technologies could make such operations a matter of hours or days.

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Golić, Zorica. "FINANCE AND ARTIFICIAL INTELLIGENCE: THE FIFTH INDUSTRIAL REVOLUTION AND ITS IMPACT ON THE FINANCIAL SECTOR." ЗБОРНИК РАДОВА ЕКОНОМСКОГ ФАКУЛТЕТА У ИСТОЧНОМ САРАЈЕВУ 8, no. 19 (February 10, 2020): 67. http://dx.doi.org/10.7251/zrefis1919067g.

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A true artificial intelligence (AI) system is something that "learns" from the data it stores, in order to perform tasks and solve problems that typically require human intelligence - either with the help of a human expert or independently. The area of AI is an interdisciplinary field, which has been designated as a strategic area in the European Union (EU) approach and a key driver of economic development that can bring solutions to many social challenges and problems. Due to its nature and its tendency to be digitally advanced and smarter with analytics, the financial sector is one of the early adopters of AI and expects multiple benefits from its application, that is, the ability to provide better service in the shortest time possible and at a lower cost. AI in the financial sector is based on an understanding of the business needs of financial organizations, institutions and markets and the ability to connect with technological capabilities. They are powerful tools that completely transform this sector. The basic idea of this paper is to consider where the real value of AI in the financial sector is, i.e. what are the practical aspects and business implications of AI in the financial sector globally. It is common knowledge that evolving technologies have always had a strong impact on the sectors in which they are applied because they give them the opportunity to improve existing manufacturing processes, services, customer experiences, operate more efficiently, achieve cost savings, etc. The aim of this paper is to identify areas of application of AI in the financial sector, and to explore leading AI applications that are changing the financial ecosystem, transforming the financial sector and that have the potential to significantly improve many of its functions. The paper further highlights other implications of AI implementation in the financial sector such as employment - job creation and termination of existing AI-influenced employment, the scope and potential of application in developing countries, the problem of regulation and use in the best interests of man, and the importance of properly managing specific AI risks.

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Prof. Saravanan K and Pooja Shri K. "Artificial Intelligence – A Revolutionizing Factor in E-Commerce." International Journal for Modern Trends in Science and Technology 06, no. 9S (October 12, 2020): 14–19. http://dx.doi.org/10.46501/ijmtst0609s03.

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The future of industries is currently more dependent upon its presence on online platforms. This online presence not only visualizes, promotes, or advertises your brand but also helps in gaining huge customers. Also, customers are now gradually turning their interests towards online shopping which are easier, time-saving, and more personalized compared to the conventional practice of visiting physical stores. So one of the most popularizing and crucial tools used by e-commerce brands to attract people is through their artificial intelligence services. AI is constantly changing and updating the world of e-commerce in terms of its customer service and experience. Effective utilization of AI can aid in identifying concealed insights, trend forecasting, and beneficial financial decision making. AI has influenced the traditional way of replenishment and merchandising by simply using data analytics to indicate which product has to be replenished and which has to be discounted. According to a recent report of "Business Insider" predicts that about 85% of the customer services will be handled by AI-powered bots which can immediately respond to calls, chats, and emails with almost no human intervention. This paper encompasses the various AI tools empowered by the e-retail brands to attract their customers, the various ways by which AI influences both the retailer and the customer, and successful e-retail brands that employed AI for their advancement. In addition to this, the paper discusses how AI is going to dominate the e-commerce venture in the near future.

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Zhylin, Mykhailo, Svitlana Makarenko, Nadia Kolohryvova, Andrii Bursa, and Yaroslav Tsekhmister. "Risk Factors for Depressive Disorders after Coming through COVID-19 and Emotional Intelligence of the Individual." Journal of Intellectual Disability - Diagnosis and Treatment 10, no. 5 (October 14, 2022): 248–58. http://dx.doi.org/10.6000/2292-2598.2022.10.05.6.

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Background: COVID-19 has caused many new challenges for humanity worldwide. The pandemic united society from different regions of the planet in the experience of experiencing the epidemic, particularly complications after the disease, including the development of depression and increased anxiety. The study aimed to identify risk factors for depression among people who came through moderate and severe coronavirus infection and to substantiate the role of emotional intelligence as a factor that prevents depressive disorders. Methods: The author’s questionnaire, Beck’s Depression Inventory (BDI-II), Emotional Intelligence Test (EmIn), and narrative analysis were used for this purpose. Results: The separate groups of respondents, distributed according to their socio-economic status, were studied for their level of general emotional intelligence. High indicators of emotional intelligence of public sector employees who are in constant social interaction were recorded. A group of entrepreneurs focused on solving pragmatic financial and economic problems had low emotional intelligence. Severe depression symptoms were also the most common among a group of entrepreneurs. A decreased level of emotional intelligence in groups of female public sector employees and increased depressive symptoms were empirically found. The physiological factor was the most significant in contributing to depression. Conclusions: The main advantage of the study is the empirical justification of the role of internal anti-stress regulation mechanisms, with the development of emotional intelligence as one of the tools. Prospects for further research include improving diagnostic tools and studying the longer-term consequences of coronavirus disease, particularly in different groups of respondents.

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Mihajlović, Iris, Cvijeta Djevojić, and Marino Stanković. "Key Drivers of Internal Market Changes and Innovative Tools Towards an Efficient Business Climate." WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL 16 (May 13, 2021): 224–43. http://dx.doi.org/10.37394/23203.2021.16.19.

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This paper has an emphasis on analytical approach to certain key factors of internal marketing. Examining the strength of their impact (financial and non-financial) on the employee motivation levels, in September 2019 the conducted survey comprised 300 respondents (sample size), employees - internal customers, nurses and hospital staff. Data were collected based on a survey of employee satisfaction, area of their jobs. Respondents were employees / hospital staff of the regional hospital center in the Republic of Croatia. Analyzed areas had been previously sequenced and grouped in accordance with key factors that corresponded to the areas of internal marketing with intensities of their impacts on the level of the satisfaction, motivation and the employee productivity. In the analysis, inferential statistics methods (Z-test, Chi-Square test) were used to answer the question of whether internal marketing instruments affect employment motivation, and to what extent is the response positive, to what extent they affect motivation, and indirectly, to work productivity. Questions related to salary, satisfaction with the basic salary, type of employment, and type of work provided answers about employee motivation with regard to the financial factor of internal marketing, and questions related to when employees use a break at work gave us the answer about the free time to which the employee is entitled to, and which affects the level of his satisfaction. The questions concentrated on work experiences of employees in the organization presented their loyalty to the non-profit organization. Main domains that represented key incentives throughout the interactive empowerments of key factors analyzed are education, participation in professional lectures and seminars, conferences, and additional training for application of innovative tools. Results confirm basic attitudes about employment in non-profit organizations, i.e. that the financial factor is not decisive in choosing employment. Results show narrow connection of loyalty with employee’s motivation as non-financial factor of internal marketing, showing in that manner the interest of employees for achievements supported by the internal confidence and permanent positive behavior or the attitude. These research results contribute to improving the elements of internal marketing. Internal customers' attitudes and behavior (i.e., their satisfaction and commitment) is affected by the organizational atmosphere component. It is closely tied to internal customers' creativity and productivity. Therefore, it is an essential element of organizational performance.

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Artym-Drohomyretska, Z., N. Harmatiy, L. Krytska, and S. Harmatii. "Statistical analysis of activity of insurance companies of Ukraine by cluster analysis tools." Galic'kij ekonomičnij visnik 74, no. 1 (2022): 7–15. http://dx.doi.org/10.33108/galicianvisnyk_tntu2022.01.007.

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The financial and economic activity of the leading insurance companies of Ukraine is analyzed, as the global pandemic COVID-19 has made significant adjustments in the activities without exaggeration of all companies, both nationally and globally. Therefore, the analysis of the insurance companies of the national economy is very important issue, because the accumulation of financial resources of insurance companies can be used as domestic investment in the national economy. The surveyed insurance companies: Alliance, Asuka, Arsenal Insurance, Alpha Insurance, Uniqa during the crisis period of 2019–2021, managed to maintain their position in the market of insurance services, and even improved their financial results, because insurance services are now more relevant than ever. The development of financial resources of insurance companies can be one of the levers of financing and investing in strategically important aspects of consumer life, such as health insurance, both locally (communities, regions) and more globally nationally, such as life insurance in general and insurance for example risky professions such as medics, including primary care, rescuers, military. In our opinion, more in-depth research of the insurance market of the national economy will make it possible to prepare the change in legislation and structure the economic activities and cooperation of national insurers, in order to improve the activities and monitoring of state regulatory institutions. In order to do this, in this paper we propose to use the tools of cluster analysis, using modern software with elements of artificial intelligence. The financial results of the main national insurance companies are studied, and due to modern tools of cluster analysis, we have clustered the studied insurance companies, using modern information programs Matlab, have made calculations in user-friendly interface. and have constructed the dendrogram that clearly represents the clusters formed. Modeling based on cluster analysis makes it possible to combine leading insurance companies into clusters of financial performance, which will allow and strengthen synergies between national insurers, which in turn will strengthen the exchange of experience, and possibly customer bases between existing insurance companies, and it is convenient for investors to consider companies united in insurance groups in order to invest investment resources.

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Artym-Drohomyretska, Z., N. Harmatiy, L. Krytska, and S. Harmatii. "Statistical analysis of activity of insurance companies of Ukraine by cluster analysis tools." Galic'kij ekonomičnij visnik 74, no. 1 (2022): 7–15. http://dx.doi.org/10.33108/galicianvisnyk_tntu2022.01.007.

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The financial and economic activity of the leading insurance companies of Ukraine is analyzed, as the global pandemic COVID-19 has made significant adjustments in the activities without exaggeration of all companies, both nationally and globally. Therefore, the analysis of the insurance companies of the national economy is very important issue, because the accumulation of financial resources of insurance companies can be used as domestic investment in the national economy. The surveyed insurance companies: Alliance, Asuka, Arsenal Insurance, Alpha Insurance, Uniqa during the crisis period of 2019–2021, managed to maintain their position in the market of insurance services, and even improved their financial results, because insurance services are now more relevant than ever. The development of financial resources of insurance companies can be one of the levers of financing and investing in strategically important aspects of consumer life, such as health insurance, both locally (communities, regions) and more globally nationally, such as life insurance in general and insurance for example risky professions such as medics, including primary care, rescuers, military. In our opinion, more in-depth research of the insurance market of the national economy will make it possible to prepare the change in legislation and structure the economic activities and cooperation of national insurers, in order to improve the activities and monitoring of state regulatory institutions. In order to do this, in this paper we propose to use the tools of cluster analysis, using modern software with elements of artificial intelligence. The financial results of the main national insurance companies are studied, and due to modern tools of cluster analysis, we have clustered the studied insurance companies, using modern information programs Matlab, have made calculations in user-friendly interface. and have constructed the dendrogram that clearly represents the clusters formed. Modeling based on cluster analysis makes it possible to combine leading insurance companies into clusters of financial performance, which will allow and strengthen synergies between national insurers, which in turn will strengthen the exchange of experience, and possibly customer bases between existing insurance companies, and it is convenient for investors to consider companies united in insurance groups in order to invest investment resources.

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Krykhovets-Khomyak, Lilia. "ECONOMICS OF INTELLIGENCE IN THE CONTEXT OF A TRANSDISCIPLINARY APPROACH." Economic Analysis, no. 32(3) (2022): 22–30. http://dx.doi.org/10.35774/econa2022.03.022.

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Introduction. The versatility of the components of the human intelligence system, applied aspects of the mechanisms of formation of activity behavior of the individual in terms of its economic choices are considered. Research methods. The basis of methodological research is the methods of empirical and theoretical research: the use of a systematic approach in the analysis of theoretical foundations and practice in the field of behavioral economics. To achieve this goal, the following research methods were used: system and logical analysis, the method of analogies, systematization and generalization. Results. The versatility of the essence of the category "human intelligence" is investigated. The trinity model of human intelligence, which determines the influences, individual and collective life priorities, choices, including economic ones, is examined in more detail. The neural and psychological aspects of the brain and the tools that influence our human beliefs, desires, needs, financial choices, shape actions and personal economic behavior in general are considered. The essential characteristic of the definition of the economy of intelligence is given. The role and essence of human intelligence in the context of modern research of the transdisciplinary approach to economic choices of behavior of subjects in the conditions of interaction are substantiated. Perspectives. Further research on various aspects of human intelligence is important in the context of election economics, financial thinking, and entrepreneurship, in terms of a qualitatively new institutional plane of development of relations between educational services in the current realities of socio-economic development and economic reset at the national, regional and local levels..

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Arce, Jaqueline Alejandra Haces. "The Use Of Market Intelligence Tools To Optimize The Economic Impact Of A Chronic Disease In Mexico." American Journal of Business Education (AJBE) 2, no. 1 (January 1, 2009): 125–26. http://dx.doi.org/10.19030/ajbe.v2i1.4029.

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The pharmaceutical products market in Mexico involves four main actors: health providers, health payers, patients and pharmaceutical companies. The relationship between health providers and the pharmaceutical companies that supply the products required to prevent and treat illnesses is of particular interest. This relationship can be described within the frame of the treatment of diabetes mellitus (DM), a major health issue in most countries, and an urgent one in Mexico. The financial impact of the treatment of diabetes could be optimized through the use of the tools provided by market intelligence systems and best clinical practices. These results would also have an impact on major health metrics for the relevant population.

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Jaramillo-Morán, Miguel A., Daniel Fernández-Martínez, Agustín García-García, and Diego Carmona-Fernández. "Improving Artificial Intelligence Forecasting Models Performance with Data Preprocessing: European Union Allowance Prices Case Study." Energies 14, no. 23 (November 23, 2021): 7845. http://dx.doi.org/10.3390/en14237845.

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European Union Allowances (EUAs) are rights to emit CO2 that may be sold or bought by enterprises. They were originally created to try to reduce greenhouse gas emissions, although they have become assets that may be used by financial intermediaries to seek for new business opportunities. Therefore, forecasting the time evolution of their price is very important for agents involved in their selling or buying. Neural Networks, an artificial intelligence paradigm, have been proved to be accurate and reliable tools for time series forecasting, and have been widely used to predict economic and energetic variables; two of them are used in this work, the Multilayer Preceptron (MLP) and the Long Short-Term Memories (LSTM), along with another artificial intelligence algorithm (XGBoost). They are combined with two preprocessing tools, decomposition of the time series into its trend and fluctuation and decomposition into Intrinsic Mode Functions (IMF) by the Empirical Mode Decomposition (EMD). The price prediction is obtained by adding those from each subseries. These two tools are combined with the three forecasting tools to provide 20 future predictions of EUA prices. The best results are provided by MLP-EMD, which is able to achieve a Mean Absolute Percentage Error (MAPE) of 2.91% for the first predicted datum and 5.65% for the twentieth, with a mean value of 4.44%.

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Srinivasan, Sujatha, and T. Thirumalai Kumari. "Big data analytics tools a review." International Journal of Engineering & Technology 7, no. 3.3 (June 8, 2018): 685. http://dx.doi.org/10.14419/ijet.v7i2.33.15476.

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Big data is the hottest trending term all over the globe and the internet. Big organizations are trying to make use of the large amounts of data collected and stored by them in big memory storages. Further large amounts of data is being produced every millisecond all over the world from users of computing devices, from satellites of all kinds, from scientific research, from governments, from big organizations that deal with huge number of customers especially financial institutions and many more. These data lie there for exploration and exploitation to gain more knowledge or rather intelligence and turning out them into wisdom for better decision making. Traditional data mining tools are not able to handle this big data. Hadoop and MapReduce are the first of the kind of tools that are being used to handle big data. Additional data mining and machine learning capabilities have been added to Hadoop and MapReduce through various plug-ins by different open source as well as vendor tools for big data analytics (BDA). Further big organizations have and are in the process of creating BDA tools most of which come with a price tag. This study gives a short review of the available BDA tools taking into consideration different characteristics of these tools. Possible solutions for existing challenges related to big data analytics are discussed.

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Seralina, N. "INFORMATION SYSTEMS FOR MACHINE INTELLIGENCE TO AUTOMATED SOFTWARE TESTING." Herald of Kazakh-British technical university 18, no. 1 (March 1, 2021): 157–61. http://dx.doi.org/10.55452/1998-6688-2021-18-1-157-161.

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The methods of development software develop rapidly. The testing of software has a great role in developing a good product. Many technologies assembled into all aspects of performance, based on software testing. Many advanced automation tools use in a set of test design and validation tests based on the artificial intelligence. The important thing is to focus on changes, to work on basis of collective reasoning of the test command and other commands analogues. The methods of the quality testing are based on the information provided in the modern digital world. The business is relying on new fast processes to provide automatic testing of software. Applying approaches of solutions to financial organization allows increase the transparency of all steps of software development. These steps can help systems show more percentage of the test case rate, can save time and money, but also effectively solves the problem of scaling the process and errors. In this paper, we research information systems for machine intelligence to automated software testing. The aim is divided to tasks: the importance of artificial intelligence, the necessary stage of Software Development - Testing and Quality Controlling System, research of main automation tools. We concluded that use of intellectual intelligence and machine learning: allows automating the repeating process and usage of the database; delivers superb intellectual product; adapts to the progressive algorithm of learning; adds more depth analysis of multiple objects; allows retrieving the maximum amount of data from the databases.

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Kotsyruba, V., and V. Dachkovsky. "METHODOLOGY OF JUSTIFICATION OF TACTICAL AND TECHNICAL REQUIREMENTS FOR TECHNICAL INTELLIGENCE MEANS." Collection of scientific works of Odesa Military Academy, no. 17 (August 31, 2022): 37–47. http://dx.doi.org/10.37129/2313-7509.2022.17.37-47.

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On the basis of the analysis of logistics functions, the measures that are its technical component are separated. It has been established that technical intelligence is one of the most important measures for the restoration of malfunctioning (damaged) samples of weapons and military equipment. The set of tasks that are solved during the conduct of technical intelligence is disclosed and problematic issues in the practice of restoring weapons and military equipment are formulated. The analysis of the latest research and publications allowed us to come to the conclusion that the problematic issues raised in the theory and practice of restoring weapons and military equipment have been considered, and therefore not fully resolved. Therefore, the purpose of the article is to highlight the developed methodology for substantiating the tactical and technical requirements for the means involved in conducting technical intelligence. A structural and logical diagram of the process of forming tactical and technical requirements for technical intelligence tools has been developed. At the same time, it is proposed to take into account a number of restrictions on the design, development and serial production of promising technical intelligence tools. Among the main restrictions on the development of technical intelligence means, first of all, financial costs should be included. The creation of unified means of technical intelligence at a low cost of their production is one of the directions for improving the efficiency of the system of restoring weapons and military equipment. The developed technique is based on the use of a set of probability indicators. As a direction of further research, the practical implementation of the developed methodology by calculating and substantiating the tactical and technical requirements for means of logistical support, namely means for conducting technical intelligence, storing and transporting material means, has been determined. Keywords: justification of requirements, tactical and technical requirements, weapons and military equipment, means of technical intelligence.

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Joksimovic, Aleksandra. "Sanctions as tools in the US foreign policy after the cold war." Medjunarodni problemi 58, no. 4 (2006): 469–91. http://dx.doi.org/10.2298/medjp0604469j.

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In searching for various opportunities to act in pursuing its foreign policy and endeavors to achieve a dominant role in the global processes USA has developed a broad range of instruments including a financial assistance as a way to be given support for its positions, intelligence activities, its public diplomacy, unilateral implementation of sanctions and even military interventions. The paper devotes special attention to one of these instruments - sanctions, which USA implemented in the last decade of the 20th century more than ever before. The author explores the forms and mechanisms for implementation of sanctions, the impact and effects they produce on the countries they are directed against, but also on the third parties or the countries that have been involved in the process by concurrence of events and finally on USA as the very initiator of imposing them.

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Russo, Dario, and Gianluca Mura. "The Financial Data Services Domain: From Taxonomies to Ontologies." International Journal of Applied Research in Management and Economics 5, no. 1 (March 20, 2022): 14–26. http://dx.doi.org/10.33422/ijarme.v5i1.747.

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There are many different types of instruments and hundreds of different markets for investment, leading to an extremely large and hard-to-define universe of financial data. The related commercial offer is extremely heterogeneous and complex. In this scenario, it is difficult to source the most appropriate financial services providers. In the past, eProcurement was mainly focused on the use of ERP management tools to record and examine previous buying decisions and expenditure data. In recent years, machine learning and artificial intelligence have been applied to procurement workflows, introducing computation of external or third party unstructured data to achieve a higher level of market knowledge and decision automation. In order to exploit the possibilities provided by these new technologies to the full extent possible, theoretical models for understanding large amounts of unstructured data are essential. In this research-in-progress paper we propose a taxonomy of financial data services and depict the related prototype ontological model, providing a possible conceptualisation and specification of the domain of interest potentially useful for the development of applications based on semantic technologies.

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Prikhno, Iryna, Valentyna Kuksa, and Ivaylo Mihaylov. "The Use of Information Technology in Financial Management." SHS Web of Conferences 100 (2021): 01007. http://dx.doi.org/10.1051/shsconf/202110001007.

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The article explores the concept of information technology and clarifies the essence of information technology in finance. The types of information technology in finance have been studied and their classification according to business entities has been carried out. A brief description of the main software products, the purpose of which is to ensure the implementation of the process of automation of financial research is given. The main tools for managing household (or person) finance using modern information technology are presented. The implementation of e-government in Ukraine is analyzed. The evaluation of the effectiveness of e-government implementation with the help of E-Government Development Index in Eastern European countries and in Ukraine is performed. A detailed analysis of the E-Government Development Index in Ukraine using a system of indicators has been studied. Digital technologies in the economy in general and in finance in particular have been studied separately. The indicators of world indexes digital economy development for Ukraine and for Eastern European countries are analyzed. The advantages and problems of the modern cryptocurrency market are clarified. The main directions of using artificial intelligence in finance are determine.

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Johan, Suwinto. "Will Data Protection Act Change the Use of Data in Indonesia Financial Services?" Lambung Mangkurat Law Journal 7, no. 1 (February 26, 2022): 1–13. http://dx.doi.org/10.32801/lamlaj.v7i1.297.

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This data science examines a variety of data in order to aid humans in making complex decisions. This science aide’s management in making complex decisions. Artificial intelligence, machine learning, big data, and algorithms all fall under the category. Data science is growing in popularity as a result of the increasing reliance on technology by businesses such as social media companies and financial technology companies. Financial technology companies create applications that allow for the collection of consumer information. This information is transformed into a set of decision-making management tools. This information was easily obtained prior to the Personal Data Protection (PDP) Act's enactment. This tool can assist management in becoming more efficient and effective in their operations. Additionally, this tool can be used to make complex management decisions, such as credit decisions for financial institutions and product marketing to consumers through appropriate advertising. The objective of this research is to examine use of data for business purposes after the enactment of the PDP Act. This study employs a descriptive and legal normative method. This research concludes that enacting the PDP Act will reduce the effectiveness of information processing. However, distinct information protection laws must be developed to improve consumer data protection. Additionally, public education about personal data protection needs to be strengthened. The PDP Act should regulate consumer protection issues and establish independent data protection institutions

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Schwarz, Klaus, Franziska Schwarz, and Reiner Creutzburg. "Conception and implementation of professional laboratory exercises in the field of open source intelligence (OSINT)." Electronic Imaging 2020, no. 3 (January 26, 2020): 278–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.3.mobmu-277.

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A large amount of personal and very incriminating data is currently stored on websites, apps and social media platforms. Users often update these data daily, and this data is open source. This information can become evidence for citizens, governments, and businesses to use in solving real financial, employment, and crime problems with the help of a professional information collector. To respond to this new situation, it is important to have well-trained staff. The fact that many authorities and companies work with very sensitive data makes it necessary to train their employees in Open Source Intelligence (OSINT). Motivated by these facts, a practical training concept is developed that enables the creation of practical exercises. The focus is on the practical implementation of OSINT tools and methods. In the new course, participants learn legitimate and effective ways to find, collect, and analyze this data from the Internet. We have developed an introductory course for a Master level program in Open Source Intelligence (OSINT). Students learn up-to-date, hands-on skills, techniques, and tools that law enforcement, private detectives, cyber attackers, and defenders use to search the vast amount of information on the Internet, analyze the results, and build on interesting data to find other areas for investigation. Our goal is to provide the OSINT knowledge base for students to succeed in their field, whether they are cyber defenders, threat intelligence analysts, private detectives, insurance inspectors, intelligence analysts, law enforcement, or just someone curious about OSINT. Throughout the course that consists of 11 exercises, students will participate in numerous hands-on exercises using the OSINT tools and techniques that form the basis for collecting free data from the Internet.

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Verbitska, Inesa. "TOOLS OF BUDGET AND TAX REGULATION OF THE ECONOMY." Economic discourse, no. 3-4 (December 30, 2021): 7–13. http://dx.doi.org/10.36742/2410-0919-2021-2-1.

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Introduction. Today, budget and tax instruments play an important role in implementing the policy of state regulation of economic development. The implementation of effective economic policy involves the use of fiscal instruments that contribute to long-term financial and economic goals. Given this, the choice of fiscal instruments, the time of their implementation and their combination - these are important issues, so to address them it is necessary to conduct a balanced assessment of the financial and economic condition of the economy. Methods. In the process of writing the article used general scientific techniques and methods of economic research. The theoretical and methodological basis of intelligence is the analysis of scientific works of domestic and foreign scientists on the study of tools of budget and tax regulation of the economy, Internet resources. The article uses general and special research methods, in particular: general and special – to ensure the achievement of this goal; dialectical, abstract and logical – to substantiate theoretical positions and conclusions. Results. The article analyzes the theoretical aspects of the concept of «fiscal regulation». The decisive role of budgetary and tax instruments in the development of the state economy is stated. It is determined that in the vast majority of cases, fiscal instruments are considered by scholars as a general system of budgetary and fiscal instruments. It is proved that effective budget and tax regulation helps to ensure the way out of crisis processes of the state. The results of the study allow us to state the existence of a significant number of fiscal instruments. Discussion. Further research will focus on the study of budget and tax tools as the main element of the system of regulation of economic development of the state. Keywords: budget and tax regulation, budget instrument, tax instrument, economic development.

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Yuan, Xiaoping, Chengxia Shi, and Zhihong Wang. "The Optimization of Hospital Financial Management Based on Cloud Technology and Wireless Network Technology in the Context of Artificial Intelligence." Wireless Communications and Mobile Computing 2022 (May 18, 2022): 1–11. http://dx.doi.org/10.1155/2022/9998311.

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In the reign of Internet technology, cloud technology has emerged as one of the powerful tools in computing with many advantages in the bag. Cloud technology is defined as online access to all types of computing applications. The applications range from access to data, servers, software, databases to storage and retrieval of data. The data can be accessed from anywhere, anytime. The user does not need to own the hardware in this case. Clouds have multiple data centers located at various places. On-demand data service is the best part of cloud technology. This technology is made use in many industries. In this research, we will study how cloud technology and wireless networking technology are applied to optimize hospital financial management. The healthcare industry faces various challenges in financial management such as budgeting, growth planning, and cost-effectiveness. Considering this scenario, it is evident that there is a solid need to optimize its financial management. The cloud computing technology driven by artificial intelligence (AI) is deployed in carrying out repetitive tasks and automation, resulting in increased productivity. In this research, the proposed system uses wireless networking systems and cloud technology to optimize hospital financial management. The model works with the implementation of a hybrid of fuzzy neural networks. The proposed model is analyzed with the existing fuzzy logic and neural network models, and it is found that the proposed model has obtained an accuracy of 97.56% with minimum resources for successful financial management at hospitals.

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Chalicheemala, Dhanush, and Dinesh Chalicheemala. "What is Open-Source Intelligence and How it Can Prevent Frauds." International Journal for Research in Applied Science and Engineering Technology 10, no. 8 (August 31, 2022): 1081–85. http://dx.doi.org/10.22214/ijraset.2022.46268.

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Abstract: This paper introduces the concept of Open Source Intelligence (OSINT) and How OSINT can prevent Frauds .The Estimated Data Consumption from 2021 to 2024 by finances online could increase from 74 zettabytes to 149 zettabytes [1] and most of this data is Publicly available. OSINT is an intelligence that is Produced by collecting, processing, analysing and correlating the information available publicly. Crimes such as fraud, illicit trade, and security are developing in the digital era of the twenty-first century, and new methods and procedures are emerging that can make them tougher to detect and investigate. OSINT investigations gather insights from open source data (OSD), revealing information on possible business partners, clients, suppliers, and workers. With the assistance of next-generation OSINT tools, corporate investigations teams and anti-fraud specialists can make the rapid and correct choices necessary to avoid reputational and financial harm

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Lee, Wanbil W. "Tools adapted to Ethical Analysis of Data Bias." HKIE Transactions 29, no. 3 (September 30, 2022): 200–209. http://dx.doi.org/10.33430/v29n3thie-2022-0037.

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Data Bias, a bias embedded in data during collection, storing, and use, and in the apps used by a human, is an emerging issue of data privacy exemplified by Artificial Intelligence bias (AI bias). This issue is becoming gradually an added vulnerability to data ethics, an added threat to data security, and an added burden to data protection. It has an effect to induce a reduction in data protection expenditure, and is crucial to the success of any creative endeavours in the data-driven technology-intensive era, including Engineering, exemplified by AI bias, and AI bias is bias created when biased data creeps in during design, development, and training of AI algorithms. AI indeed culminates in a phenomenon in which the populace jumps, yet a sober minority steers away from because of the pervasive cyber-threats that AI bias raises. At issue is not data bias per se, nor the multi-dimensional issues induced by human bias, which are usually complex and slippery, but a need for a method to enable a holistic view covering the technical, financial, legal, social, ethical, and ecological aspects of a given problem, action, policy, or decision. Recommendable is a method composed of the Ethical Matrix Algorithm and Hexa-dimension Metric Algorithm (Lee, forthcoming) based respectively on the Ethical Matrix and Hexa-dimension Metric (Lee, 2021).

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Lee, Sungnam. "The Fintech Revolution and the Future of the Insurance Business: A Study on Artificial Intelligence utilization risks and legal issues." Korean Insurance Law Association 16, no. 3 (October 31, 2022): 91–138. http://dx.doi.org/10.36248/kdps.2022.16.3.091.

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In this study, the future of the insurance industry and legal tasks related to digitalization and artificial intelligence were examined. What is artificial intelligence technology? Among them, we reviewed what the insurance industry can do and what it is doing, and looked at legal issues and what to change in the future. First of all, the development status of digitalization and artificial intelligence was investigated, and the impact on the insurance industry and the opportunities and risks brought by digitalization and artificial intelligence were examined. Second, if digitalization and the development of artificial intelligence replace existing human tasks, it should be considered whether to treat such artificial intelligence robots or tools as simple tools or give legal effects by giving similar status to humans. Third, the emergence of digitalization and artificial intelligence is expected to have an impact on various areas of insurance work, especially focusing on insurance recruitment work, and legal discussions related to it were attempted. The introduction of digitalization and artificial intelligence is expected to have an impact on the overall financial transaction or insurance transaction. The emergence of new technologies implies new opportunities and risks at the same time. It will also lead to changes in the existing socio-economic and cultural institutions. Legal significance in changes in the insurance environment is related to which process digitalization and artificial intelligence play a role in which of the conventional insurance tasks, and that is the problem caused. It is necessary to guarantee the right to access various channels centered on consumers by drastic deregulation to promote digitalization. Accordingly, insurance companies need to develop a model that allows consumers to actively select suitable, convenient means, and methods for themselves at each stage of insurance subscription. With the rise of the platform as a new business model, it will affect product counseling and recommendation, product description, subscription receipt, notification receipt, premium receipt, and insurance policy issuance, so in-depth discussions are needed on whether to accept the platform as a new recruitment channel or as a simple information provider or advisory business. In seeking legal changes through the introduction of digitalization and artificial intelligence, whether or not to grant a legal personality to tools or platforms equipped with artificial intelligence, which is the starting point of the most basic discussion, needs to be carefully introduced in consideration of future technological developments and social needs. As a legal discussion due to the advent of digitalization and artificial intelligence, the legal effects of AI intervention and operation were examined. At each transaction stage, various notifications and explanations that insurance recruiters must perform before signing insurance contracts, automation of subscription receipt and approval, and legal improvement should be promoted. Meanwhile, with the development of artificial intelligence technology, unmanned transportation such as robot dogs, robot disabled assistants, drones, trucks, aircraft, ships, etc., virtual assets, digital currency, metaverse, and NFT (non-fungible token) will be developed and utilized. It is necessary to solve the legal problems that these various artificial intelligence tools can create and guarantee measures through the development of new insurance products. Dr Stephen Hawking said, “The advent of powerful artificial intelligence will be the best or the worst. What could happen to mankind. We don’t know which one.” However, the reality is that artificial intelligence technology has been developed and used for each task. With history evolving in time, society is causing significant changes.

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Journal articles on the topic 'Financial intelligence tools'

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