Suspicious activity reporting using dynamic bayesian networks

AbstractSuspicious activity reporting has been a crucial part of anti-money laundering systems. Financial transactions are considered suspicious when they deviate from the regular behavior of their customers. Money launderers pay special attention to keep their transactions as normal as possible to disguise their illicit nature. This may deceive the classical deviation based statistical methods for finding anomalies. This study presents an approach, called SARDBN (Suspicious Activity Reporting using Dynamic Bayesian Network), that employs a combination of clustering and dynamic Bayesian network (DBN) to identify anomalies in sequence of transactions. SARDBN applies DBN to capture patterns in a customer’s monthly transactional sequences as well as to compute an anomaly index called AIRE (Anomaly Index using Rank and Entropy). AIRE measures the degree of anomaly in a transaction and is compared against a pre-defined threshold to mark the transaction as normal or suspicious. The presented approach is tested on a real dataset of more than 8 million banking transactions and has shown promising results

Money Laundering Detection Framework to Link the Disparate and Evolving Schemes

Money launderers hide traces of their transactions with the involvement of entities that participate in sophisticated schemes. Money laundering detection requires unraveling concealed connections among multiple but seemingly unrelated human money laundering networks, ties among actors of those schemes, and amounts of funds transferred among those entities. The link among small networks, either financial or social, is the primary factor that facilitates money laundering. Hence, the analysis of relations among money laundering networks is required to present the full structure of complex schemes. We propose a framework that uses sequence matching, case-based analysis, social network analysis, and complex event processing to detect money laundering. Our framework captures an ongoing single scheme as an event, and associations among such ongoing sequence of events to capture complex relationships among evolving money laundering schemes. The framework can detect associated multiple money laundering networks even in the absence of some evidence. We validated the accuracy of detecting evolving money laundering schemes using a multi-phases test methodology. Our test used data generated from real-life cases, and extrapolated to generate more data from real-life schemes generator that we implemented. Keywords: Anti Money Laundering, Social Network Analysis, Complex Event Processin

Money Laundering Detection Framework to Link the Disparate and Evolving Schemes

Money launderers hide traces of their transactions with the involvement of entities that participate in sophisticated schemes. Money laundering detection requires unraveling concealed connections among multiple but seemingly unrelated human money laundering networks, ties among actors of those schemes, and amounts of funds transferred among those entities. The link among small networks, either financial or social, is the primary factor that facilitates money laundering. Hence, the analysis of relations among money laundering networks is required to present the full structure of complex schemes. We propose a framework that uses sequence matching, case-based analysis, social network analysis, and complex event processing to detect money laundering. Our framework captures an ongoing single scheme as an event, and associations among such ongoing sequence of events to capture complex relationships among evolving money laundering schemes. The framework can detect associated multiple money laundering networks even in the absence of some evidence. We validated the accuracy of detecting evolving money laundering schemes using a multi-phases test methodology. Our test used data generated from real-life cases, and extrapolated to generate more data from real-life schemes generator that we implemented

Occupational Fraud Detection Through Visualization

Occupational fraud affects many companies worldwide causing them economic loss and liability issues towards their customers and other involved entities. Detecting internal fraud in a company requires significant effort and, unfortunately cannot be entirely prevented. The internal auditors have to process a huge amount of data produced by diverse systems, which are in most cases in textual form, with little automated support. In this paper, we exploit the advantages of information visualization and present a system that aims to detect occupational fraud in systems which involve a pair of entities (e.g., an employee and a client) and periodic activity. The main visualization is based on a spiral system on which the events are drawn appropriately according to their time-stamp. Suspicious events are considered those which appear along the same radius or on close radii of the spiral. Before producing the visualization, the system ranks both involved entities according to the specifications of the internal auditor and generates a video file of the activity such that events with strong evidence of fraud appear first in the video. The system is also equipped with several different visualizations and mechanisms in order to meet the requirements of an internal fraud detection system

Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcity

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsEvery year, criminals launder billions of dollars acquired from serious felonies (e.g. terrorism, drug smuggling, or human trafficking), harming countless people and economies. Cryptocurrencies, in particular, have developed as a haven for money laundering activity. Machine Learning can be used to detect these illicit patterns. However, labels are so scarce that traditional supervised algorithms are inapplicable. This research addresses money laundering detection assuming minimal access to labels. The results show that existing state-of-the-art solutions using unsupervised anomaly detection methods are inadequate to detect the illicit patterns in a real Bitcoin transaction dataset. The proposed active learning solution, however, is capable of matching the performance of a fully supervised baseline by using just 5% of the labels. This solution mimics a typical real-life situation in which a limited number of labels can be acquired through manual annotation by experts

Machine learning methods to detect money laundering in the bitcoin blockchain in the presence of label scarcity

Lorenz, J., Silva, M. I., Aparício, D., Ascensão, J. T., & Bizarro, P. (2020). Machine learning methods to detect money laundering in the bitcoin blockchain in the presence of label scarcity. In ICAIF 2020 - 1st ACM International Conference on AI in Finance (pp. 1-8). [3422549] (ICAIF 2020 - 1st ACM International Conference on AI in Finance). Association for Computing Machinery, Inc. https://doi.org/10.1145/3383455.3422549Every year, criminals launder billions of dollars acquired from serious felonies (e.g., terrorism, drug smuggling, or human trafficking), harming countless people and economies. Cryptocurrencies, in particular, have developed as a haven for money laundering activity. Machine Learning can be used to detect these illicit patterns. However, labels are so scarce that traditional supervised algorithms are inapplicable. Here, we address money laundering detection assuming minimal access to labels. First, we show that existing state-of-the-art solutions using unsupervised anomaly detection methods are inadequate to detect the illicit patterns in a real Bitcoin transaction dataset. Then, we show that our proposed active learning solution is capable of matching the performance of a fully supervised baseline by using just 5% of the labels. This solution mimics a typical real-life situation in which a limited number of labels can be acquired through manual annotation by experts.publishersversionpublishe

AI-powered Fraud Detection in Decentralized Finance: A Project Life Cycle Perspective

In recent years, blockchain technology has introduced decentralized finance (DeFi) as an alternative to traditional financial systems. DeFi aims to create a transparent and efficient financial ecosystem using smart contracts and emerging decentralized applications. However, the growing popularity of DeFi has made it a target for fraudulent activities, resulting in losses of billions of dollars due to various types of frauds. To address these issues, researchers have explored the potential of artificial intelligence (AI) approaches to detect such fraudulent activities. Yet, there is a lack of a systematic survey to organize and summarize those existing works and to identify the future research opportunities. In this survey, we provide a systematic taxonomy of various frauds in the DeFi ecosystem, categorized by the different stages of a DeFi project's life cycle: project development, introduction, growth, maturity, and decline. This taxonomy is based on our finding: many frauds have strong correlations in the stage of the DeFi project. According to the taxonomy, we review existing AI-powered detection methods, including statistical modeling, natural language processing and other machine learning techniques, etc. We find that fraud detection in different stages employs distinct types of methods and observe the commendable performance of tree-based and graph-related models in tackling fraud detection tasks. By analyzing the challenges and trends, we present the findings to provide proactive suggestion and guide future research in DeFi fraud detection. We believe that this survey is able to support researchers, practitioners, and regulators in establishing a secure and trustworthy DeFi ecosystem.Comment: 38 pages, update reference

Máquinas de soporte vectorial y árboles de clasificación para la detección de operaciones sospechosas de lavado de activos

Money laundering is a crime that brings a large number of negative consequences to society in general. Anti-money laundering systems have been developed to mitigate this problem in financial institutions, which is where it is mainly presented. This causes a new problem: the false positives obtained from these systems, which represent financial losses for the financial entities, as well as time and focus, since they do not deal with the real unusual operations. The main detection methods of unusual operations of money laundering found in the literature are evaluated to determine which techniques offer the best results and from these generate a new model that improves the registered indicators. From a process of review and replication of anomalies detection methodologies found in the literature, a new model that presents better metrics when classifying operations as normal and unusual could be generated, this may represent way to reduce the false positive rates in their anti-money laundering systems in financial institutions.El lavado de activos es un delito que trae consigo un gran número de consecuencias negativas a la sociedad en general. Para mitigar este problema en las entidades financieras, que es donde principalmente se presenta, se han desarrollado sistemas anti lavado de dinero. Lo anterior origina un nuevo problema: los falsos positivos que se obtienen a partir de dichos sistemas, los cuales representan para las entidades financieras pérdidas de dinero, tiempo y foco, al no tratar las verdaderas operaciones inusuales. Se evalúan los principales métodos de detección de operaciones inusuales de lavado de activos que se encuentran en la literatura, para determinar cuáles técnicas ofrecen los mejores resultados y a partir de estas generar un nuevo modelo que mejore los indicadores registrados. A partir de un proceso de revisión y replicación de metodologías de detección de anomalías encontradas en la literatura, se pudo generar un nuevo modelo que presenta mejores métricas a la hora de clasificar operaciones como normales e inusuales, lo cual puede representar para las entidades financieras una manera de disminuir las tasas de falsos positivos en sus sistemas anti lavado

The Economic Impact of Deficient Anti-Money Laundering Program to a Multinational Bank

Money laundering is a financial crime that threatens the stability of a country\u27s financial sector. The purpose of this qualitative case study was to explore the strategies that compliance officers used to improve the AML program in a multinational bank in the northeastern United States. The target population was purposefully selected using bank compliance officers because they have experience with the strategies to improve the AML program. The normative neo-institutional theory framed the discussion of this study. Data were collected from interviews with 10 AML compliance officers and the achieved data. The Krippendorff method of content analysis was used to analyze the data. Six themes emerged from the findings including strategies to improve AML compliance in a multinational bank and the economic consequences of inadequate AML programs. The findings of the study show that advanced technology, employee trainings and management oversight are essential to improve AML program. The results of these analyses suggested the pervasive economic and social repercussions of money laundering on the multinational bank. The findings of the study may contribute to positive social change by identifying strategies that banking leaders could incorporate in the AML programs to reduce the risk of bank failures, promote the bank\u27s participation in social development projects, and provide employment opportunities to the community members