Modelos de aprendizaje automático aplicados a la detección de transacciones sospechosas de lavado de activos en entidades financieras: Una revisión sistemática de la literatura

El lavado de activos es uno de los delitos que viene afectando a la economía del país. Grandes cantidades de dinero se lavan todos los años. Según Daniel Linares, intendente de Análisis Operativo de la UIF estimó que entre junio de 2016 y mayo de 2017 el monto investigado aumentó en 125%. Por esta razón, este estudio tiene como objetivo identificar modelos de aprendizaje automático propuestos, diseñados o implementados para el apoyo en la detección de transacciones sospechosas de lavado de activos en entidades financieras. Para lograr identificar los modelos de aprendizaje automático se realizó una revisión sistemática de la literatura de las investigaciones publicadas en las diferentes librerías digitales indexadas. De un total de 485 artículos revisados, se identificaron 20 artículos que hacen referencia a los modelos de aprendizaje automático. Cabe destacar que los modelos de aprendizaje automático son comúnmente utilizados para apoyar en la detección de transacciones sospechosas de lavado de activos por su adecuación al entorno cambiante, siendo esto una de sus ventajas sobre los sistemas tradicionales de monitorización. Actualmente existen diversidad de métodos, algoritmos y técnicas de aprendizaje automático aplicados para lograr este fin, siendo los algoritmos de agrupación los que mayormente se utilizan según los estudios seleccionados.Trabajo de investigaciónLIMAEscuela Profesional de Ingeniería de SistemasTecnología de información e innovación tecnológic

Quick survey of graph-based fraud detection methods

In general, anomaly detection is the problem of distinguishing between normal data samples with well defined patterns or signatures and those that do not conform to the expected profiles. Financial transactions, customer reviews, social media posts are all characterized by relational information. In these networks, fraudulent behaviour may appear as a distinctive graph edge, such as spam message, a node or a larger subgraph structure, such as when a group of clients engage in money laundering schemes. Most commonly, these networks are represented as attributed graphs, with numerical features complementing relational information. We present a survey on anomaly detection techniques used for fraud detection that exploit both the graph structure underlying the data and the contextual information contained in the attributes

Enhancing Transaction Monitoring Controls to Detect Money Laundering Using Machine Learning

Money laundering has become a great economic problem with huge consequences on society and financial institutions in the last decade. Current anti-money laundering (AML) procedures within the industry are either inefficient due to criminals’ increasingly sophisticated approaches or technological advancements. This paper provides an extended abstract to identify and analyze the machine learning methods to detect money laundering through transaction monitoring in the literature. Moreover, the paper identifies research gaps and based on the observed limitations, suggests future research directions and areas in need of improvements

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

Detection of shell companies in financial institutions using dynamic social network

Shell companies work in financial interaction with other companies to commit several crimes such as concealing resources of illicit origin (money laundering), tax fraud (tax evasion), corruption, bribery, and drug trafficking, among others. This interaction can be represented by a set of nodes and connections that show the multiple relationships between entities over time. The current article proposes to detect transactions related to shell companies in financial systems, using legal person attributes and incorporating self and group comparisons into dynamic social networks. The months of June 2019, September 2020, and November 2021 are taken as evaluation periods to test the proposed methodology. Our methodology performs better than the traditional rules method, yielding balanced accuracies and true positive rates above 0.9 and 0.85, respectively. The false-positive rate was also lower in the proposed model than in the rule system for most evaluation periods. The latter translates into a reduction in costs by compliance investigations

Money laundering and terrorism financing detection using neural networks and an abnormality indicator

This study proposes a comprehensive model that helps improve self-comparisons and group-comparisons for customers to detect suspicious transactions related to money laundering (ML) and terrorism financing (FT) in financial systems. The self-comparison is improved by establishing a more comprehensive know your customer (KYC) policy, adding non-transactional characteristics to obtain a set of variables that can be classified into four categories: inherent, product, transactional, and geographic. The group-comparison involving the clustering process is improved by using an innovative transaction abnormality indicator, based on the variance of the variables. To illustrate the way this methodology works, random samples were extracted from the data warehouse of an important financial institution in Mexico. To train the algorithms, 26,751 and 3527 transactions and their features, involving natural and legal persons, respectively, were selected randomly from January 2020. To measure the prediction accuracy, test sets of 1000 and 600 transactions were selected randomly for natural and legal persons, respectively, from February 2020. The proposed model manages to decrease the proportion of false positives and increase accuracy when compared to the rule-based system. On reducing the false positive rate, the company’s costs for investigating suspicious customers also decrease significantly

Aplicação de técnicas de descoberta do conhecimento em investigações de lavagem de dinheiro.

Lavagem de dinheiro é um método utilizado por criminosos para dar aparência lícita a recursos obtidos de maneira ilícita. Estimativas de entidades mundialmente reconhecidas apontam que tal atividade é responsável por algo entre 2 e 5% do PIB mundial e está se tornando cada vez mais sofisticada. Pela dificuldade de identificação utilizando métodos tradicionais de investigação, a tecnologia tem desempenhado um papel importante nesse processo. Busca-se com este trabalho identificar as técnicas de descoberta do conhecimento aplicadas nas investigações da lavagem de dinheiro, o que foi conseguido através de uma revisão sistemática de literatura. As técnicas encontradas serão utilizadas em uma pesquisa experimental que visa compará-las quanto à eficácia na identificação de relacionamentos em uma rede de transações bancárias provenientes de uma investigação real de lavagem de dinheiro