Relevant bibliographies by topics / Financial crime detection

Academic literature on the topic 'Financial crime detection'

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

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Journal articles on the topic "Financial crime detection"

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Slinko, Sergey V., Pylyp S. Yepryntsev, Artem O. Shapar, Dmytro B. Sanakoiev, and Alina H. Harkusha. "Modern indicators of financial crimes detection and their prevention in Ukraine." Revista Amazonia Investiga 10, no. 44 (September 29, 2021): 61–72. http://dx.doi.org/10.34069/ai/2021.44.08.6.

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The increase in the level of financial crime is caused by the technological development, weak moni-toring of financial institutions, low level of international cooperation in terms of combating these types of crimes. The paper aims to develop effective ways to combat financial crimes in Ukraine. With the help of a systematic analysis of financial crime modern indicators, theoretical sources, prac-tical measures and international experience, the main indicators of financial crimes are identified. Such indicators are the use of false documents and other people's accounts, international payment systems; frequent and complex financial transactions of a confusing nature; unusual for a client amounts and counterparties of the transaction; incomplete or missing payment information. The study has found that the increase in the level of financial crime is influenced by the presence of off-shore zones, the low level of liability for the crime, the reluctance of financial institutions to interact with law enforcement agencies in regard to combating such crimes. It is possible to increase the level of financial crime counteraction by conducting an external audit and investigation of financial crimes, establishing international cooperation to combat such crimes, blocking suspicious financial transactions, taking preventive measures and combating related crimes.
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Slinko, Sergey V., Pylyp S. Yepryntsev, Artem O. Shapar, Dmytro B. Sanakoiev, and Alina H. Harkusha. "Modern indicators of financial crimes detection and their prevention in Ukraine." Revista Amazonia Investiga 10, no. 44 (September 29, 2021): 61–72. http://dx.doi.org/10.34069/ai/2021.44.08.6.

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The increase in the level of financial crime is caused by the technological development, weak moni-toring of financial institutions, low level of international cooperation in terms of combating these types of crimes. The paper aims to develop effective ways to combat financial crimes in Ukraine. With the help of a systematic analysis of financial crime modern indicators, theoretical sources, prac-tical measures and international experience, the main indicators of financial crimes are identified. Such indicators are the use of false documents and other people's accounts, international payment systems; frequent and complex financial transactions of a confusing nature; unusual for a client amounts and counterparties of the transaction; incomplete or missing payment information. The study has found that the increase in the level of financial crime is influenced by the presence of off-shore zones, the low level of liability for the crime, the reluctance of financial institutions to interact with law enforcement agencies in regard to combating such crimes. It is possible to increase the level of financial crime counteraction by conducting an external audit and investigation of financial crimes, establishing international cooperation to combat such crimes, blocking suspicious financial transactions, taking preventive measures and combating related crimes.
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Didimo, Walter, Giuseppe Liotta, and Fabrizio Montecchiani. "Network visualization for financial crime detection." Journal of Visual Languages & Computing 25, no. 4 (August 2014): 433–51. http://dx.doi.org/10.1016/j.jvlc.2014.01.002.

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OKSANA VIVCHAR. "ORGANIZATIONAL AND LEGAL MECHANISM OF FINANCIAL INVESTIGATIONS IN LAW ENFORCEMENT ACTIVITY: INFORMATION AND ANALYTICAL APPROACH." Herald of Khmelnytskyi National University 294, no. 3 (March 2021): 82–86. http://dx.doi.org/10.31891/2307-5740-2021-294-3-13.

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In modern conditions of turbulence of economic processes of activity of law enforcement agencies testifies that effective counteraction to crime is impossible without active use of economic knowledge. Please note that the practical effectiveness of the mechanism for detecting and investigating economic crimes depends to some extent on the study of financial information that characterizes criminal activity. Meanwhile, it should be noted that at the stage of crime detection, financial monitoring of dubious financial transactions is used, inspections and audits of financial and economic activities of economic entities are conducted. At the stage of crime investigation, the timely appointment and conduct of forensic accounting examinations, the involvement of a specialist economist in investigative actions, etc. is important. Combating modern crime involves the integration of legal and economic knowledge, as well as the interaction of operational and investigative units of law enforcement agencies, which are directly involved in detecting and investigating crimes together with experts with economic knowledge. The article examines the conceptual and categorical apparatus of financial investigations and the role of law enforcement agencies in the process of detecting criminal activity. Information and analytical support of financial investigations is substantiated. As a result of scientific research, it is identified according to the classification features of the scheme of legalization (laundering) of criminal means. On the basis of which the three-phase model of legalization (laundering) of proceeds from crime is characterized, which consists of three stages: stages, stratification, integration.
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Brody, Richard G., and Frank S. Perri. "Fraud detection suicide: the dark side of white-collar crime." Journal of Financial Crime 23, no. 4 (October 3, 2016): 786–97. http://dx.doi.org/10.1108/jfc-09-2015-0043.

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Purpose The purpose of this paper is to explore the issue of suicide, a violent act against one’s self, as it relates to white- and red-collar crimes. White-collar crime can be described as nonviolent crime committed for financial gain. Red-collar crime describes a situation where a white-collar criminal commits an act of violence, often murder, to silence someone who is in a position to report a fraud they have perpetrated. Previous research has not addressed the issue of suicide, as it relates to white- and red-collar crime. Design/methodology/approach The analysis is conceptual, focusing on the historical underpinnings of white- and red-collar crime and reviewing the evolution of white-collar criminals. Sources of information consisted of published news media, scholarly articles and articles retrieved from the web. Findings A suicide may be linked, directly or indirectly, to a financial crime. Law enforcement must be careful not to jump to conclusions, as there is a possibility that a staged suicide has occurred. Originality/value Law enforcement individuals may want to consider an additional motive when investigating a suicide, especially when the victim has some type of connection to a known fraud. This type of connection may not be readily apparent and may require a new approach on the part of a law enforcement investigation.
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Vasylchyshyn, Oleksandra, and Olena Sydorovych. "INSTITUTIONS OF CIVIL SOCIETY AS SUBJECTS OF FIGHT AGAINST FINANCIAL CRIMES." Economic Analysis, no. 30(4) (2020): 109–17. http://dx.doi.org/10.35774/econa2020.04.109.

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The article considers the legislative rights of civil society institutions to interact with the state. The influence of financial crimes on the functioning and development of the state, as well as the possibility of the influence of civil society institutions on the detection and detection of financial crimes has been studied. The activity of the State Financial Monitoring Service on detection and processing of information of risky financial transactions and its cooperation with foreign divisions of financial investigations is described. The most popular countries in which there are entities with a suspicious reputation for financial transactions are identified. It is emphasized that the human and resource potential of civil society institutions will contribute to the detection of money laundering operations. It is also determined that the legalization of criminal proceeds is a financial crime that encroaches on the established procedure for conducting financial transactions, and hence on the interests of the state.
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Kurshan, Eren, and Hongda Shen. "Graph Computing for Financial Crime and Fraud Detection: Trends, Challenges and Outlook." International Journal of Semantic Computing 14, no. 04 (December 2020): 565–89. http://dx.doi.org/10.1142/s1793351x20300022.

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The rise of digital payments has caused consequential changes in the financial crime landscape. As a result, traditional fraud detection approaches such as rule-based systems have largely become ineffective. Artificial intelligence (AI) and machine learning solutions using graph computing principles have gained significant interest in recent years. Graph-based techniques provide unique solution opportunities for financial crime detection. However, implementing such solutions at industrial-scale in real-time financial transaction processing systems has brought numerous application challenges to light. In this paper, we discuss the implementation difficulties current and next-generation graph solutions face. Furthermore, financial crime and digital payments trends indicate emerging challenges in the continued effectiveness of the detection techniques. We analyze the threat landscape and argue that it provides key insights for developing graph-based solutions.
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Kim, Ae Chan, Seongkon Kim, Won Hyung Park, and Dong Hoon Lee. "Fraud and financial crime detection model using malware forensics." Multimedia Tools and Applications 68, no. 2 (April 3, 2013): 479–96. http://dx.doi.org/10.1007/s11042-013-1410-3.

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Gottschalk, Petter. "White-collar crime." International Journal of Police Science & Management 19, no. 2 (June 2017): 120–26. http://dx.doi.org/10.1177/1461355717711453.

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Policing religious organizations presents challenging situations. When there is suspicion of financial crime by white-collar criminals, secrecy and trust represent obstacles to law enforcement. This article discusses the lack of detection and neutralization techniques often applied in religious organizations. There may be too much trust, too much freedom, too much individual authority, too little scepticism, too much loyalty and too little control of the financial side in religious organizations. There may be no empirical evidence for the proposition that religion has a deterrent effect on crime, although sociologists and criminologists have long recognized potential links between religious belief and delinquent behaviour.
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Zavydnyak, I. О. "Legalization «laundering» of funds obtained by criminal route as one of the types of economic transnational crimes." Analytical and Comparative Jurisprudence, no. 4 (April 28, 2022): 313–17. http://dx.doi.org/10.24144/2788-6018.2021.04.54.

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The article considers one of the types of transnational economic crimes - legalization (laundering) of proceeds from crime. Emphasis is placed on the fact that the legalization (laundering) of proceeds from crime is a predicate offense, because it is not characteristic of any one type of organized crime, but for a very wide range of transnational crimes, because the vast majority of them are for profit and criminally acquired assets. In this regard, the process of investigating and providing legal assistance in the investigation of such economic transnational crimes has a number of features and obstacles. The need for an appropriate legal framework for international cooperation in the fight against money laundering (laundering) is indicated. Three main stages of legalization (laundering) of criminal proceeds as one of the economic transnational crimes have been identified and analyzed. Thus, the first stage of such illegal actions is: placement of cash in the normal circulation of money (the process of penetration of proceeds from crime into the economic system, which aims to free the holder of a large sum of funds from cash and its placement in national or foreign currency in cash turnover, for the next stage). The second stage of legalization (laundering) of proceeds from crime is camouflage (hiding traces and ways of spreading) or cash circulation, which includes a number of financial transactions aimed at concealing the source of money. The third stage is defined as integration (return and legalization of «laundered» money), full entry of funds into the legal economic system, where they can be used for any purpose. It is emphasized that in the process of legalization (laundering) of proceeds from crime, there are certain periods (or «bottlenecks»), during which the risk of detecting such illegal activities increases significantly. Even with the large number of options, methods and forms of money laundering, such periods cannot always be avoided, which significantly increases the risk of detection of such illegal activities by financial intelligence units or other competent authorities. Such periods include: the process of receiving cash into the financial system; movement of cash flows across the state border; transfers within the financial system and at the exit from the financial system. Given this, in order to ensure the successful functioning of the entire system of legalization (laundering) of proceeds from crime, this process is usually performed by persons who are professionally engaged in management procedures for investing legalized proceeds in legal real estate and other assets. It is noted that effective counteraction to the process of legalization (laundering) of proceeds from crime is impossible without the legal settlement of international cooperation in the investigation and collection of evidence of such economic transnational crimes.
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Dissertations / Theses on the topic "Financial crime detection"

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Canhoto, Ana Isabel. "Profiling behaviour : the social construction of categories in the detection of financial crime." Thesis, London School of Economics and Political Science (University of London), 2007. http://etheses.lse.ac.uk/2154/.

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Profiles are knowledge constructs that represent and identify a data subject. While not a new phenomenon, the use of profiling has exploded and its ubiquity is likely to increase, as a result of the widespread adoption of monitoring technology. The literature on profile development tends to refer to the practice, the technique or the technology of profiling, separately. Little has been written on how the perspectives interact with each other and, ultimately, shape the emerging behaviour profile. In order to map out the elements that impact on behaviour profiling, this thesis uses organisational semiotics, enhanced with classification theory, for key constructs. The study views profilers as agents who interpret and act on available information according to particular sets of technical, formal and informal factors and who, in the presence of incomplete or ambiguous stimuli, may fill in or distort information. Furthermore, the thesis examines how the position of the interpreter in the profiling process influences the result of the exercise. A case study conducted in a British financial institution demonstrates how technical systems and profilers acting in particular contexts influence each other in a dialectical process, whereby the characteristics of the data available impact the analysts' ability to interpret an event and, at the same time, the analysts tend to look for in the data only what they consider conceivable. The discussion centres on the influence of the type of stimuli available, the relational context and the actions of individual profilers in shaping the emerging meaning, in the context of financial crime detection. In addition, it considers the role of technical, formal and informal systems to overcome eventual variances in meaning. The thesis extends the applicability of organisational semiotics with classification theory. Inspired by models of sequential encounters, the thesis provides a methodological contribution by developing a tool for the analysis of sequential meaning making processes. A practical contribution emerges from mapping the impact of the profilers' perceptions into the emerging profile, and by suggesting mechanisms for shaping those perceptions.
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Palmeiro, João Maria Mateus. "MevaL: A Visual Machine Learning Model Evaluation Tool for Financial Crime Detection." Master's thesis, 2021. http://hdl.handle.net/10362/119698.

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Data Science and Machine Learning are two valuable allies to fight financial crime,the domain where Feedzai seeks to leverage its value proposition in support of its mission:to make banking and commerce safe. Data is at the core of both fields and this domain, sostructuring instances for visual consumption provides an effective way of understandingthe data and communicating insights.The development of a solution for each project and use case requires a careful andeffective Machine Learning Model Evaluation stage, as it is the major source of feedbackbefore deployment. The tooling for this stage available at Feedzai can be improved,accelerated, visually supported, and diversified to enable data scientists to boost theirdaily work and the quality of the models.In this work, I propose to collect and compile internal and external input, in terms ofworkflow and Model Evaluation, in a proposal hierarchically segmented by well-definedobjectives and tasks, to instantiate the proposal in a Python package, and to iteratively val-idate the package with Feedzai’s data scientists. Therefore, the first contribution is MevaL,a Python package for Model Evaluation with visual support, integrated into Feedzai’s DataScience environment by design. In fact, MevaL is already being leveraged as a visualization package on two internal reporting projects that are serving some of Feedzai’s majorclients.In addition to MevaL, the second contribution of this work is the Model EvaluationTopology developed to ensure clear communication and design of features.
A Ciência de Dados e a Aprendizagem Automática [277] são duas valiosas aliadas no combate à criminalidade económico-financeira, o domínio em que a Feedzai procura potenciar a sua proposta de valor em prol da sua missão: tornar o sistema bancário e o comércio seguros. Além disso, os dados estão no centro das duas áreas e deste domínio.Assim, a estruturação visual dos mesmos fornece uma maneira eficaz de os entender e transmitir informação.O desenvolvimento de uma solução para cada projeto e caso de uso requer um estágiocuidadoso e eficaz de Avaliação de Modelos de Aprendizagem Automática, pois esteestágio coincide com a principal fonte de retorno (feedback) antes da implementaçãoda solução. As ferramentas de Avaliação de Modelos disponíveis na Feedzai podem seraprimoradas, aceleradas, suportadas visualmente e diversificadas para permitir que oscientistas de dados impulsionem o seu trabalho diário e a qualidade destes modelos.Neste trabalho, proponho a recolha e compilação de informação interna e externa, em termos de fluxo de trabalho e Avaliação de Modelos, numa proposta hierarquicamente segmentada por objetivos e tarefas bem definidas, a instanciação desta proposta num pacote Python e a validação iterativa deste pacote em colaboração com os cientistas de dados da Feedzai. Posto isto, a primeira contribuição deste trabalho é o MevaL, um pacote Python para Avaliação de Modelos com suporte visual, integrado no ambiente de Ciência de Dados da Feedzai. Na verdade, o MevaL já está a ser utilizado como um pacote de visualização em dois projetos internos de preparação de relatórios automáticos para alguns dos principais clientes da Feedzai.Além do MevaL, a segunda contribuição deste trabalho é a Topologia de Avaliação de Modelos desenvolvida para garantir uma comunicação clara e o design enquadrado das diferentes funcionalidades.
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Botha, André Eduan. "Combating financial crime : evaluating the prospect of a whole-of-government approach." Thesis, 2018. http://hdl.handle.net/10500/24432.

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Tax crimes, money laundering and other financial crimes threaten the strategic, political and economic interests of developed and developing countries. The problems encountered by the researcher, is the broad and vague meaning of financial crime compounded by the confusing and ill-considered use of the term by law- and policymakers, politicians, government agencies, agency officials and practitioners weakening effective communication about the phenomenon. The literature shows that given the complexity and multi-faceted nature of financial crime, combating financial crime in all its facets cannot be undertaken by investigative agencies acting in isolation. This study was undertaken with the aim to describe and systematically categorise financial crime and evaluate the prospect of using a Whole-of-Government approach as a framework to harness the capacity of the existing government agencies to combat financial crime more effectively. The research was underpinned by a pragmatic paradigm allowing the researcher to apply a qualitative research methodology using an exploratory and evaluation research design. A detailed review of the literature available nationally and internationally was conducted to establish a conceptual and practical understanding of the issues under investigation. An interview schedule with predetermined questions was developed, pre-tested and administered to participants who are active practitioners involved in combating financial crime in the primary government agencies responsible for combating financial crime based on a purposive sample. The data obtained from the literature and participants were analysed, interpreted and thematically listed according to the frequency with the aim of identifying and comparing similarities and differences between the data. The data was used to develop a system to categorise financial crime systematically based on descriptors used to describe the meaning and application of the term financial crime and to propose practical methods practises and models to combat financial crime more effectively. This research indicates that financial crime can be systematically categorised according to descriptors of the unlawful conduct and that the whole of government approach is a viable approach to combat financial crime more effectively according to the available models for arranging Whole-of-Government work.
Police Science
D.Litt. et Phil. (Police Science)
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HANTKOVÁ, Zuzana. "Forenzní účetnictví a hospodářská kriminalita." Master's thesis, 2015. http://www.nusl.cz/ntk/nusl-188333.

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This thesis focuses on fraud in the entity, to methods for their detection and possibilities of improving the internal control system so that the risk of offending employee or manager has been reduced to a minimum.
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Books on the topic "Financial crime detection"

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Nossen, Richard A. The detection, investigation, and prosecution of financial crimes. 2nd ed. Richmond, VA: Thoth Books, 1993.

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The first line of defense: The role of financial institutions in detecting financial crimes : hearing before the Subcommittee on Oversight and Investigations of the Committee on Financial Services, U.S. House of Representatives, One Hundred Ninth Congress, first session, May 26, 2005. Washington: U.S. G.P.O., 2006.

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Frozen assets. New York: Soho Press, 2011.

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Computer Crime: Techniques for Preventing and Detecting Crime in Financial Institutions. McGraw-Hill, 1991.

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Financial Investigations: A Financial Approach to Detecting and Resolving Crimes. Accents Pubns Service, 1994.

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Vogel, Don. Financial Investigations: A Financial Approach To Detecting And Resolving Crimes. Diane Pub Co, 1993.

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Financial Investigations: A Financial Approach to Detecting and Resolving Crimes (Publication). Accents Pubns Service, 1993.

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Vogel, Don. Financial Investigations: A Financial Approach to Detecting & Resolving Crimes. Student Handbook. Diane Pub Co, 1994.

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Financial investigations: A financial approach to detecting and resolving crimes : instructor's guide. [Washington, DC: Internal Revenue Service, 1994.

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Financial investigations: A financial approach to detecting and resolving crimes : student workbook. [Washington, DC: Internal Revenue Service, 1994.

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Book chapters on the topic "Financial crime detection"

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Giacomo, Emilio Di, Walter Didimo, Luca Grilli, Giuseppe Liotta, and Fabrizio Montecchiani. "Visual Analytics for Financial Crime Detection at the University of Perugia." In Advanced Visual Interfaces. Supporting Artificial Intelligence and Big Data Applications, 195–200. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68007-7_14.

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Suzumura, Toyotaro, Yi Zhou, Ryo Kawahara, Nathalie Baracaldo, and Heiko Ludwig. "Federated Learning for Collaborative Financial Crimes Detection." In Federated Learning, 455–66. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96896-0_20.

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Othman, Radiah. "Financial Statement Fraud Detection and Investigation in Digital Environment." In Advances in Digital Crime, Forensics, and Cyber Terrorism, 187–214. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1558-7.ch011.

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This chapter discusses some of the stages involved in detecting and investigating financial statement fraud in the digital environment. It emphasizes the human element aspects – the fraudster and the forensic investigator, their skills and capability. The explanation focuses specifically on the context of financial statement fraud detection and investigation which requires the accounting knowledge of the investigator. It also highlights the importance of the investigator to be a skeptic and have an inquiring mind of who had the opportunity to perpetuate the fraud, how the fraud could have been perpetrated, and the motive(s) to do so.
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Frunza, Marius-Christian. "Determining the Accuracy of a Fraud-Detection Model." In Solving Modern Crime in Financial Markets, 217–32. Elsevier, 2016. http://dx.doi.org/10.1016/b978-0-12-804494-0.00016-4.

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Al-Saedy, Hasan L. "Cyber Crimes." In Handbook of Research on Threat Detection and Countermeasures in Network Security, 154–68. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-6583-5.ch009.

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The financial cost of cyber crime now has an annual cost estimated in the UK in eleven figures. In this chapter an ethic based definition of cyber crime is introduced and cyber crimes are classified. The impact of each class of cyber crime on society, individual, government and international security is highlighted. The cost of cyber crime is evaluated and a technique to prevent and mitigate the effect of these crimes on individual, government and international security and world peace is indicated. The forensic techniques and tools used in cyber crime evidence gathering and prosecuting procedure is also indicated. Finally, recommendations and suggestion are given to mitigate the impact of cyber crime on individuals, societies, world finance and international security.
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Devi, Aruna. "Cyber Crime and Cyber Security." In Detecting and Mitigating Robotic Cyber Security Risks, 160–71. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2154-9.ch011.

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Cybercrime is a multifaceted and forever changing phenomenon. It is found that Cyber criminals who are becoming more classy and stylish are making consumers of both private and public organizations their prey. To prevent attacks additional layers of defense are required. It has been observed that Cyber crime has increased in density and complexity and financial costs ever since organizations have adopted the use of computers in carrying out their business processes. An example of the case studies carried out on cyber crimes is the Parliament attack case. The main points discussed in this chapter are Cyber crime and cyber security, the unusual cyber crimes that we come across. Various prevention techniques and detection techniques like Tripwires, Honey Pots, anomaly detection system, configuration checking tools and operating system commands, various acts that have been imposed against Cyber crime and online safety tips are also discussed.
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Lenard, Mary Jane, and Pervaiz Alam. "Application of Fuzzy Logic to Fraud Detection." In Encyclopedia of Information Science and Technology, First Edition, 135–39. IGI Global, 2005. http://dx.doi.org/10.4018/978-1-59140-553-5.ch026.

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In light of recent reporting of the failures of some of the major publicly-held companies in the U.S. (e.g., Enron & WorldCom), it has become increasingly important that management, auditors, analysts, and regulators be able to assess and identify fraudulent financial reporting. The Enron and WorldCom failures illustrate that financial reporting fraud could have disastrous consequences both for stockholders and employees. These recent failures have not only adversely affected the U.S. accounting profession but have also raised serious questions about the credibility of financial statements. KPMG (2003) reports seven broad categories of fraud experienced by U.S. businesses and governments: employee fraud (60%), consumer fraud (32%), third-party fraud (25%), computer crime (18%), misconduct (15%), medical/insurance fraud (12%), and financial reporting fraud (7%). Even though it occurred with least frequency, the average cost of financial reporting fraud was the highest, at $257 million, followed by the cost of medical/insurance fraud (average cost of $33.7 million).
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Verma, Diksha, Pooja Kansra, and Sarabjit Kaur. "A Theoretical Perspective of Artificial Intelligence in Hostility of Cyber Threats in the Banking Sector." In Advanced Machine Learning Algorithms for Complex Financial Applications, 43–54. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-4483-2.ch004.

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With the progress of new know-how and technology, various cyber centers are utilized by illegal persons to augment cyber related crime. To alleviate this digital crime and cyber threats, artificial intelligence is being implemented by banks and financial institutions. A variety of avenues are offered by artificial intelligence techniques, which facilitate the banks to amplify affluence plus wealth. As per various literatures it has been observed that the expenses of cybercrime were around $ 450 million globally. Therefore, it becomes imperative to introduce certain security programs for guarding cyber threats in banking area. To battle and overcome hackers many banks are moving hands to employ artificial intelligence. Various kinds of cybercrimes are prohibited and recognized by AI-based fraud detection systems. However, execution and preservation of artificial intelligence consist of the high cost. After it, joblessness rate is also augmented by AI techniques. In this study various artificial intelligence techniques will be discussed along with their utilization and limitations.
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Anand, Abhineet, and M. Arvindhan. "Development and Various Critical Testing Operational Frameworks in Data Acquisition for Cyber Forensics." In Advances in Digital Crime, Forensics, and Cyber Terrorism, 88–102. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1558-7.ch006.

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Digital forensics is the science of preserving and analyzing digital data; this data can then be used in court cases as well as for crime detection and prevention. Digital forensics began in the 1970s and was initially used as a tool for fighting financial crime. Today, with computers and digital devices being an integral part of our professional and private lives, digital forensics are used/needed in a wide variety of disputes. Data Acquisitions is described and discuss different techniques or methodology obtain the data, facts, and figures from different resource and at a different level of the system.
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P. Z, Salma, and Maya Mohan. "Detection and Prediction of Spam Emails Using Machine Learning Models." In Handbook of Research on Cyber Crime and Information Privacy, 201–18. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-5728-0.ch011.

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One of today's important means of communication is email. The extensive use of email for communication has led to many problems. Spam emails being the most crucial among them. It is one the major issues in today's internet world. Spam emails contain mostly advertisements and offensive content, which are often sent without the recipient's request and are generally annoying, time consuming, and wasting space on the communication media's resources. It creates inconveniences and financial loss to the recipients. Hence, there is always the need to filter the spam emails and separate them from the legitimate emails. There are a lot of content-based machine learning techniques that have proven to be effective in detecting and filtering spam emails. Due to a large increase in email spamming, the emails are studied and classified as spam or not spam. In this chapter, three machine learning models, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BLSTM), are used classify the emails as spam and benign.
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Conference papers on the topic "Financial crime detection"

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Fior, Jacopo, Thomas Favale, Luca Cagliero, Danilo Giordano, Marco Mellia, Elena Baralis, Silvia Ronchiadin, Paolo Baracco, and Dario Moncalvo. "Legal Entity Disambiguation for Financial Crime Detection." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020700.

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Didimo, Walter, Giuseppe Liotta, Fabrizio Montecchiani, and Pietro Palladino. "An advanced network visualization system for financial crime detection." In 2011 IEEE Pacific Visualization Symposium (PacificVis). IEEE, 2011. http://dx.doi.org/10.1109/pacificvis.2011.5742391.

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Vosyliute, Ieva, and Nijole Maknickiene. "INVESTIGATION OF FINANCIAL FRAUD DETECTION BY USING COMPUTATIONAL INTELLIGENCE." In 12th International Scientific Conference „Business and Management 2022“. Vilnius Gediminas Technical University, 2022. http://dx.doi.org/10.3846/bm.2022.787.

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Abstract:
Due to increasing technical capabilities, financial fraud becomes more sophisticated and more difficult to detect. As there are various categories and typologies of financial fraud, different detection techniques may be applied. However, based on the data generated daily by financial organizations, a technical solution must be implemented. This paper presents a comprehensive literature review of financial fraud, the categorizations of financial fraud, and financial fraud detection with the particular focus on computational intelligence-based techniques. As outlined in the reviewed literature, money laundering is a multilayered crime involving several fraud typologies; therefore, it was selected to be analysed in this research. The purpose of the research is to investigate the synthetic dataset of the money laundering scheme to see whether additional patterns could be outlined, which would help financial organizations to recognize suspicious activity easier. To achieve this goal, computational intelligence - decision tree, was selected as a classification method to identify additional patterns. As a result, data classification provides new data parameters which are essential in improving accurate and efficient financial fraud detection.
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Kurshan, Eren, Hongda Shen, and Haojie Yu. "Financial Crime & Fraud Detection Using Graph Computing: Application Considerations & Outlook." In 2020 Second International Conference on Transdisciplinary AI (TransAI). IEEE, 2020. http://dx.doi.org/10.1109/transai49837.2020.00029.

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Tundis, Andrea, Soujanya Nemalikanti, and Max Mühlhäuser. "Fighting organized crime by automatically detecting money laundering-related financial transactions." In ARES 2021: The 16th International Conference on Availability, Reliability and Security. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3465481.3469196.

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