Relevant bibliographies by topics / Financial intelligence tools

Academic literature on the topic 'Financial intelligence tools'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "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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Dissertations / Theses on the topic "Financial intelligence tools"

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Gina, Bonginkosi Phila. "Factors That Drive the Selection of Business Intelligence Tools in South African Financial Services Providers." Master's thesis, Faculty of Commerce, 2021. http://hdl.handle.net/11427/33741.

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Innovation and technology advancements in information systems (IS) result in multiple product offerings and business intelligence (BI) software tools in the market to implement business intelligence systems (BIS). As a result, a high proportion of organisations fail to employ appropriate and suitable software tools meeting organisational needs, resulting in a prime number of BI solution failures and abandoned projects are therefore recorded. Due to such project failures, benefits associated with BI are not realised hence organisations loose enormous investments on BI solutions and competitive advantage. The study aims at discovering and exploring critical factors influencing the selection of BI tools when embarking on the selection process. This is a quantitative research study and questionnaire surveyed data was collected from 92 participants working in South African financial services providers listed on the Johannesburg Stock Exchange (JSE) appearing in the top 100 based on market capitalization. The data was analysed quantitative by employing the use of SPSS and SmartPLS-3 software's to test the significance of influential factors using the proposed conceptual model that emerged from the literature. The findings showed that a combination of domain technical and non-technical factors is critical. Therefore, software tool technical factors (functionality, ease of use, compatibility, availability of an integrated hardware/software package, and availability of source code), vendor technical factors (availability of technical support, technical skills, quality of product, availability of user manual for important information, tutorial for learning and troubleshooting guide, and experience in using product developed by the same vendor), and opinion non-technical factors (end-users, subordinates, outside personnel acquaintances, and improvement in customer service) emerged as significant combination of influential factors to be considered. The study contributes to both academia and industry by providing influential determinants for software tool selection. It is hoped that the findings presented will contribute to a greater understanding of factors influencing the selection of BI tools to researchers and practitioners alike. Furthermore, organisations seeking to select and deliver appropriate BI tools will be better equipped to drive such endeavours.
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Li, Jin. "FGP : a genetic programming based tool for financial forecasting." Thesis, University of Essex, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343550.

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Tasola, Kullander Petter. "Information visualization as an interactive business intelligence tool for improved management and self-assessment of financial brokers in private banking." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-255002.

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With an increase in storage capacity, many organizations strive to collect as much data as possible. The data can then be aggregated and visualized in order to provide a strategic advantage, commonly referred to as Business Intelligence (BI). Several sectors are contemporarily unfamiliar with the full potential of BI visualizations, among them the financial sector.In this report, an experimental design-oriented research study, set out to explore the affordances and challenges with designing an information visualization to improve both management and self-assessment of financial brokers in private banking at a large-scale bank.To explore this area, a prototype was iteratively developed based on information gathered from interviews and evaluations on two private banking managers and a panel of UX professionals at the bank. The final prototype was then evaluated by the two managers and five of their financial brokers through a combination of a task analysis and semi-structured interviews. The results concluded that the proposed visualization improved several aspects for financial management including business overview and workload balancing. However, the proposed tool was not deemed useful in self-assessment terms as financial brokers’ performance is so largely dependent on the current market state.
Tack vare den ständigt ökande lagringskapaciteten så strävar idag många organisationer efter att samla på sig så mycket data som möjligt om sin verksamhet. Denna data kan sedan aggregeras och visualiseras för att förbättra företagets strategi, vilket kallas Business Intelligence (BI). Idag känner en del verksamheter inte till den sanna potentialen av informationsvisualisering inom BI, däribland äldre banker.Denna rapport är en experimentell, design-orienterad forskningsstudie med syftet att utforska möjligheterna och utmaningarna med att designa en informationsvisualisering för att förbättra både management och själv-utvärdering av börsmäklare inom private banking, på en storskalig, svensk bank.För att utforska detta område så utvecklades en prototyp iterativt, baserat på information som löpande samlats under semi-strukturerade intervjuer och utvärderingar. Prototypen utvärderades slutligen av två chefer och fem av deras anställda börsmäklare genom en kombination av task analysis och semi-strukturerade intervjuer. Resultaten visar att den föreslagna visualiseringen förbättrade ett flertal aspekter inom finansiellt management, bl.a verksamhetsöverblick och balansering av arbetsbörda. Den föreslagna prototypen var dock inte användbar sett till själv-utvärdering eftersom börsmäklares prestationer är så starkt kopplat till marknadens dagliga skick.
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Santos, Joana Inês Fernandes dos. "Combate ao branqueamento de capitais e ao financiamento do terrorismo: ferramentas tecnológicas utilizadas." Master's thesis, 2018. http://hdl.handle.net/10071/18318.

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O Branqueamento de Capitais e o Terrorismo são temáticas que estão cada vez mais presentes no nosso dia-a-dia, principalmente o terrorismo. Como tal torna-se importante perceber que temáticas são estas, como são combatidas e que entidades estão por detrás disto. A presente tese tem enfoque nas ferramentas tecnológicas utilizadas no combate do Branqueamento de Capitais e ao Financiamento do Terrorismo, mais concretamente nos sistemas que utilizam agentes inteligentes, existentes no mercado e no papel que o Banco de Portugal tem nesta temática, como supervisor. Para auxílio da investigação recorreu-se à metodologia qualitativa utilizando como método de recolha de dados: a análise documental e a realização de uma entrevista a um especialista na área de Segurança de Redes e Computadores e Sistemas Distribuídos. Na presente dissertação aborda-se um protótipo de um Sistema Inteligente Anti-Branqueamento de Capitais feito pela Universidade de Hong Kong e propõem-se um Sistema Inteligente melhorado, que tem por base este protótipo. Nesta proposta de Sistema Inteligente Anti-Branqueamento de Capitais, é explicada a sua estrutura e componentes, o seu funcionamento, formas de o proteger e quais as vantagens e desvantagens deste sistema face aos sistemas inteligentes existentes no mercado.
Money laundering and terrorism are issues that are more and more present in our lives. Specially terrorism. As such, it becomes important to understand what exactly are these issues, how they can be fought and who or what is behind them. This thesis is focused on the technological tools used by financial institutions, in particular the systems that use intelligent agents, and what exactly is available in the market and the supervising role of Banco the Portugal. This investigation was carried out using the qualitative method and document analysis was used to collect data. An interview to a specialist in Network Security and Distributed Systems was also used. In this dissertation an Intelligent Anti-Money Laundering System developed by the University of Hong Kong is discussed and an improved system, based on the referred prototype is proposed and its structure, architecture and components are explained, as well as the way it functions, the best ways to protect it and the advantages and disadvantages of such system versus the systems that are available today.
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Books on the topic "Financial intelligence tools"

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Billio, Monica, and Simone Varotto. A New World Post COVID-19. Venice: Fondazione Università Ca’ Foscari, 2020. http://dx.doi.org/10.30687/978-88-6969-442-4.

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Pandemics are disruptive events that have profound consequences for society and the economy. This volume aims to present an analysis of the economic impact of COVID-19 and its likely consequences for our future. This is achieved by drawing from the expertise of authors who specialise in a wide range of fields including fiscal and monetary policy, banking, financial markets, pensions and insurance, artificial intelligence and big data, climate change, labour market, travel, tourism and politics, among others. We asked contributing authors to write their chapters for a non-technical audience so that their message could reach beyond academia and professional economists to policy makers and the wider society. The material in this volume draws from the latest research and provides a wealth of ideas for further investigations and opportunities for reflection. This also makes it an ideal learning tool for economics and finance students wishing to gain a deeper understanding of how COVID-19 could influence their disciplines.
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Competitor Intelligence: Strategy, Tools and Techniques for Competitive Advantage (Financial Times Management). Pitman Publishing, 1999.

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Iori, Giulia, and James Porter. Agent-based Modeling for Financial Markets. Edited by Shu-Heng Chen, Mak Kaboudan, and Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.43.

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This chapter discusses a step in the evolution of agent-based model (ABM) research in finance. Agent-based modeling has concentrated on the development of stylized market models, which have been extremely useful for understanding how complex macro-scale phenomena emerge from micro-rules. In order to further develop ABMs from proof of concept into robust tools for policy makers, to control and forecast complex real-world financial markets, it is essential to permit agents to behave as active data-gathering decision makers with sophisticated learning capabilities. The main focus of this chapter is to show how agent based models (ABMs) in financial markets have evolved from simple zero- intelligence agents that follow arbitrary rules of thumb into sophisticated agents described by microfounded rules of behavior. The chapter then briefly looks at the challenges posed by and approaches to model calibration and provides examples of how ABMs have been successful at offering useful insights for policy making.
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Kvint, Vladimir. Strategizing of the Digital Kuzbass Region. Kemerovo State University, 2021. http://dx.doi.org/10.21603/978-5-8353-2796-6.

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In the collective monograph "Strategizing of the Digital Kuzbass Region" digitalization of the Kuzbass economy is considered as one of the most important tools for implementation of the Strategy of socio-economic development of Kuzbass region for the period up to 2035. The monograph substantiates developing and promoting a digital platform for monitoring and controlling the implementation of strategic priorities, as well as programs for digitalization of some industries and sectors of Kuzbass region economy, including coal industry, tourism, financial sector, media system. Perspectives for artificial intelligence and other avant-garde technologies in the economy of Kuzbass region are also discussed in the monograth.
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Book chapters on the topic "Financial intelligence tools"

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Reddy, Chavva Subba, Ravi Sankar Sangam, and B. Srinivasa Rao. "A Survey on Business Intelligence Tools for Marketing, Financial, and Transportation Services." In Smart Intelligent Computing and Applications, 495–504. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1927-3_53.

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Kotios, Dimitrios, Georgios Makridis, Silvio Walser, Dimosthenis Kyriazis, and Vittorio Monferrino. "Personalized Finance Management for SMEs." In Big Data and Artificial Intelligence in Digital Finance, 215–32. Cham: Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-030-94590-9_12.

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AbstractThis chapter presents Business Financial Management (BFM) tools for Small Medium Enterprises (SMEs). The presented tools represent a game changer as they shift away from a one-size-fits-all approach to banking services and put emphasis on delivering a personalized SME experience and an improved bank client’s digital experience. An SME customer-centric approach, which ensures that the particularities of the SME are taken care of as much as possible, is presented. Through a comprehensive view of SMEs’ finances and operations, paired with state-of-the-art ML/DL models, the presented BFM tools act as a 24/7 concierge. They also operate as a virtual smart advisor that delivers in a simple, efficient, and engaging way business insights to the SME at the right time, i.e., when needed most. Deeper and better insights that empower SMEs contribute toward SMEs’ financial health and business growth, ultimately resulting in high-performance SMEs.
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Koprivec, Filip, Gregor Kržmanc, Maja Škrjanc, Klemen Kenda, and Erik Novak. "Screening Tool for Anti-money Laundering Supervision." In Big Data and Artificial Intelligence in Digital Finance, 233–51. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-94590-9_13.

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AbstractEfficient screening of transactions provides an empowering tool for anti-money laundering procedures and actions. Automatic classification and detection of anomalous behaviours and transaction structures enable faster and more effective action on the side of the supervisory authority. This chapter introduces research achievements and tools developed to streamline transaction monitoring and ease domain experts with automatic and semi-automatic filtering of risky transaction typologies. Presented tools are integrated as part of PAMLS (Platform for Anti-Money Laundering Supervision) to streamline and automate the discovery of risky behaviours in bank transaction data enriched with relevant company information. Enriched transactional data is pseudo-anonymized with respect to the legal and regulatory framework. Screening tool as a part of PAMLS platform automatically detects and marks specific predefined scenarios using newly developed state-of-the-art AI method tailored specifically to time-evolving transaction graphs in transaction data. Easy-to-use tools, early warning system and subsequent parameterized queries with additional white-listed scenarios provide domain experts with additional data to easily explore suggested dangerous transaction groups and make more informed decisions and further action, be it at a level of a specific financial institution or a cluster of them.
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Igor, Fedun, Stetsenko Sergii, Tsyfra Tatiana, Vershygora Dmytro, Valchuk Bohdan, and Andriiv Valentyna. "Innovative Software Tools for Effective Management of Financial and Economic Activities of the Organization." In Impact of Artificial Intelligence, and the Fourth Industrial Revolution on Business Success, 17–38. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08093-7_2.

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Wu, Jia, and Miklos Vasarhelyi. "XBRL: A New Tool For Electronic Financial Reporting." In Business Intelligence Techniques, 73–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24700-5_5.

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Karathanasopoulos, Andreas, Peter W. Middleton, Konstantinos Theofilatos, and Efstratios Georgopoulos. "GEPTrader: A New Standalone Tool for Constructing Trading Strategies with Gene Expression Programming." In Artificial Intelligence in Financial Markets, 107–21. London: Palgrave Macmillan UK, 2016. http://dx.doi.org/10.1057/978-1-137-48880-0_4.

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Sánchez-Cervantes, José Luis, Giner Alor-Hernández, María del Pilar Salas-Zárate, Jorge Luis García-Alcaraz, and Lisbeth Rodríguez-Mazahua. "FINALGRANT: A Financial Linked Data Graph Analysis and Recommendation Tool." In Exploring Intelligent Decision Support Systems, 3–26. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74002-7_1.

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Škrinjarić, Tihana. "Grey model as a tool in dynamic portfolio selection." In Artificial Intelligence and Big Data for Financial Risk Management, 1–16. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003144410-1.

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De Luca, Francesco, Stefania Fensore, and Enrica Meschieri. "A Statistical Tool as a Decision Support in Enterprise Financial Crisis." In Advances in Intelligent Systems and Computing, 75–82. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99698-1_9.

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Aschi, Massimiliano, Susanna Bonura, Nicola Masi, Domenico Messina, and Davide Profeta. "Cybersecurity and Fraud Detection in Financial Transactions." In Big Data and Artificial Intelligence in Digital Finance, 269–78. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94590-9_15.

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AbstractFrauds in financial services are an ever-increasing phenomenon, and cybercrime generates multimillion revenues, therefore even a small improvement in fraud detection rates would generate significant savings. This chapter arises from the need to overcome the limitations of the rule-based systems to block potentially fraudulent transactions. After mentioning the limitations of rule-based approach, this chapter explains how machine learning is able to address many of these limitations and, more effectively, identify risky transactions. A novel AI-based fraud detection system – built over a Data Science and Machine Learning – is presented for the pre-processing of transaction data and model training in a batch layer (to periodically retrain the predictive model with new data) while in a stream layer, the real-time fraud detection is handled based on new input transaction data. The architecture presented makes this solution a valuable tool for supporting fraud analysts and for automating the fraud detection processes.
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Conference papers on the topic "Financial intelligence tools"

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Branquinho, Augusto A. B., Carlos R. Lopes, and Augusto C. E. Baffa. "Probabilistic Planning for Multiple Stocks of Financial Markets." In 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2016. http://dx.doi.org/10.1109/ictai.2016.0083.

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Liu, April Hua, and Xiaoyue Fang. "A Dirichlet Policy Reuse Approach for Financial Markets." In 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2021. http://dx.doi.org/10.1109/ictai52525.2021.00103.

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Alyamani, Alaa, and Mohammed Alsalem. "Customer Relationship Management Based on Business Intelligence : Proposed Framework." In 3rd International Conference on Administrative & Financial Sciences. Cihan University - Erbil, 2021. http://dx.doi.org/10.24086/afs2020/paper.216.

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It is known currently that the processes of transaction and interaction in the various points and channels between the organization and their customers have resulted in huge amounts of data. These data have great importance if we analyzed properly. By analyzing these data can determine the appropriate ways of dealing with customers and identify their needs and preferences in order to enable the organizations to develop and maintain their relationships with them. Data storing and analytics technologies and applications such as business intelligence tools have been the major interest of researchers and practitioners due to the benefits that can provide for CRM applications. The purposes of this study are to discuss the usage of business intelligence in CRM in order to improve the activities of dealing with customers. As well as, this study proposed a general framework of CRM based on business intelligence technologies which can be applied in the clothing factory . An extensive review of literature has been conducted to identify trends and patterns of academic research in the context of usages business intelligence for CRM. The reviewed studies confirm the high importance of using business intelligence, and its main role in obtaining important insights and knowledge between large amounts of data.
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Tang, Zhongjun, and Jing Xiao. "Tools for a New Demand Forecasting Paradigm "Individual Demand Forecasting'." In 2009 International Conference on Business Intelligence and Financial Engineering (BIFE). IEEE, 2009. http://dx.doi.org/10.1109/bife.2009.76.

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Dempster, M. A. H. "Dynamic stochastic programming tools for individual asset liability management." In 2014 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr). IEEE, 2014. http://dx.doi.org/10.1109/cifer.2014.6924044.

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Li, Xiuquan, and Zhido Deng. "A Machine Learning Approach to Predict Turning Points for Chaotic Financial Time Series." In 19th IEEE International Conference on Tools with Artificial Intelligence(ICTAI 2007). IEEE, 2007. http://dx.doi.org/10.1109/ictai.2007.105.

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Bai, Lu, Lixin Cui, Yue Wang, Yuhang Jiao, and Edwin R. Hancock. "A Quantum-inspired Entropic Kernel for Multiple Financial Time Series Analysis." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/614.

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Network representations are powerful tools for the analysis of time-varying financial complex systems consisting of multiple co-evolving financial time series, e.g., stock prices, etc. In this work, we develop a new kernel-based similarity measure between dynamic time-varying financial networks. Our ideas is to transform each original financial network into quantum-based entropy time series and compute the similarity measure based on the classical dynamic time warping framework associated with the entropy time series. The proposed method bridges the gap between graph kernels and the classical dynamic time warping framework for multiple financial time series analysis. Experiments on time-varying networks abstracted from financial time series of New York Stock Exchange (NYSE) database demonstrate that our approach can effectively discriminate the abrupt structural changes in terms of the extreme financial events.
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Birznieks, Janis, and Lasma Licite-Kurbe. "Analysis of the Introduction of Business Intelligence and Data Warehousing into Businesses in Latvia." In 22nd International Scientific Conference. “Economic Science for Rural Development 2021”. Latvia University of Life Sciences and Technologies. Faculty of Economics and Social Development, 2021. http://dx.doi.org/10.22616/esrd.2021.55.028.

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The market of global business intelligence technologies reached EUR 18.3 billion in 2017 and is expected to reach EUR 22.8 billion in the near future, as such technologies provide companies with a number of benefits: new information for business decision-making, real-time financial reporting and manual work automation. Nevertheless, many companies around the world do not achieve the desired results of applying business intelligence and data warehousing technologies. The research aims to develop scenarios for applying business intelligence and data warehousing tools in entrepreneurship in Latvia based on an examination of the tools. The research has found that the companies examined in the case study have introduced business intelligence along with data warehousing; however, there are differences in applying the business intelligence and the level of its advancement. Overall, a business intelligence system makes core and support operations and processes faster, as well as reduces costs and requires less human resources. However, problems were identified concerning a lack of motivation in employees to learn new technologies. The scenario analysis concluded that large companies should perform as many administrative and technological processes related to the mentioned technologies as possible themselves rather than outsource them, which allows them to save funds on the development of such technologies and improve the company’s data culture.
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Palmieri, Alessandro, and Blerina Nazeraj. "OPEN BANKING AND COMPETITION: AN INTRICATE RELATIONSHIP." In International Jean Monnet Module Conference of EU and Comparative Competition Law Issues "Competition Law (in Pandemic Times): Challenges and Reforms. Faculty of Law, Josip Juraj Strossmayer University of Osijek, 2021. http://dx.doi.org/10.25234/eclic/18822.

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Open banking – promoted in the European Union by the access to account rule contained in the Directive (EU) 2015/2366 on payment services in the internal market (PSD2) – is supposed to enhance consumer’s welfare and to foster competition. However, many observers are fearful about the negative effects of the entry into the market of the so-called BigTech giants. Unless incumbent banks are able to rise above the technological challenges, the risk is that, in the long run, BigTech firms could dominate the market, by virtue of their great ability to collect data on consumer preferences, and to process them with sophisticated tools, such as Artificial Intelligence and Machine Learning techniques; not to mention the possible benefits arising from the cross-subsidisation. This paper aims at analysing the controversial relationship between open banking and competition. In this framework, many aspects must be clarified, such as the definition of the relevant markets; the identification of the dominant entities; the relationship with the essential facility doctrine. The specific competition problems encountered in the financial sector need to be inscribed in the context of the more general debate around access to data in the digital sphere. The evolving scenario poses a serious challenge to regulators, calling them to strike the right balance between fostering innovation and preserving financial stability. The appraisal intends not only to cover EU law and policy, but also to make a comparison with other legal systems. In this respect, something noteworthy is taking place in the United States where, as of today, consumers’ access to financial data sharing has been largely dependent on private-sector efforts. Indeed, Section 1033 of the Dodd-Frank Wall Street Reform and Consumer Protection Act (passed in the aftermath of the financial crisis of 2008) provides that, subject to rules prescribed by the Bureau of Consumer Financial Protection (CFPB), a consumer financial services provider must make available to a consumer information, in its control or possession, concerning the consumer financial product or service that the consumer obtained from the provider. This provision, which dates back to 2010, has never been implemented. However, on 22 October 2020, the CFBP has announced its intention to regulate open banking, issuing an advanced notice of proposed rulemaking. In light of their investigation, the authors advocate the adaptation of the current strategies to the modified conditions and, in some instances, the creation of novel mechanisms, more suitable to face unprecedented threats.
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Dalianis, P. J., and D. Katafygis. "Facilitating Conventional Intelligent Techniques in the Fault Diagnosis of a Modern Financial Terminal Machine." In EUROCON 2005 - The International Conference on "Computer as a Tool". IEEE, 2005. http://dx.doi.org/10.1109/eurcon.2005.1629901.

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Reports on the topic "Financial intelligence tools"

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Iffat, Idris. Effectiveness of Different Methods to the Counter Financing of Terrorism. Institute of Development Studies, April 2022. http://dx.doi.org/10.19088/k4d.2022.091.

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Countering financing of terrorism (CFT) has been a core component of counter terrorism strategies since the 9/11 attacks on the US in 2001. Key CFT measures are criminalisation of terrorism financing; sanctions and assets freezing/seizure; and use of financial intelligence. CFT assessments focus on implementation of these measures, rather than on impact in terms of preventing terrorist activity. This rapid review therefore looks at the effectiveness of different CFT measures. It draws on a mixture of academic and grey literature, including policy papers and reports from agencies involved in CFT implementation. While there is available literature on terrorism financing (how groups raise funds), and on the various approaches to CFT as well as implementation assessment, the review found very little on the impact of CFT in preventing terrorism. Reflecting this, it was also difficult to identify specific examples of CFT impact and effectiveness. The limited literature on the latter suggests that, while CFT measures can hamper terrorists/terrorist groups, they cannot stop them entirely. Despite this, CFT remains a useful tool for governments in the fight against terrorism/their efforts to counter terrorism. However, the current CFT model needs to be reformed to address significant changes in both the terrorist threat and terrorism financing environment.
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Perdigão, Rui A. P. Beyond Quantum Security with Emerging Pathways in Information Physics and Complexity. Synergistic Manifolds, June 2022. http://dx.doi.org/10.46337/220602.

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Information security and associated vulnerabilities have long been a pressing challenge, from the fundamental scientific backstage to the frontline across the most diverse sectors of society. At the tip of the iceberg of this problem, the citizens immediately feel that the reservation of privacy and the degradation of the quality and security of the information and communication on which they depend for the day-to-day activities, already of crucial relevance, are at stake. Naturally though, the challenges do not end there. There is a whole infrastructure for storing information, processing and communication, whose security and reliability depend on key sectors gearing modern society – such as emergency communication systems (medical, civil and environmental protection, among others), transportation and geographic information, the financial communications systems at the backbone of day-to-day transactions, the information and telecommunications systems in general. And crucially the entire defence ecosystem that in essence is a stalwart in preventing our civilisation to self-annihilate in full fulfilment of the second principle of thermodynamics. The relevance of the problem further encompasses the preservation of crucial values such as the right to information, security and integrity of democratic processes, internal administration, justice, defence and sovereignty, ranging from the well-being of the citizen to the security of the nation and beyond. In the present communication, we take a look at how to scientifically and technically empower society to address these challenges, with the hope and pragmatism enabled by our emerging pathways in information physics and complexity. Edging beyond classical and quantum frontiers and their vulnerabilities to unveil new principles, methodologies and technologies at the core of the next generation system dynamic intelligence and security. To illustrate the concepts and tools, rather than going down the road of engineered systems that we can ultimately control, we take aim at the bewildering complexity of nature, deciphering new secrets in the mathematical codex underlying its complex coevolutionary phenomena that so heavily impact our lives, and ultimately bringing out novel insights, methods and technologies that propel information physics and security beyond quantum frontiers.
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