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 methodologies against money laundering in non-banking correspondents

Las actividades de lavado de activos son el resultado de la corrupción, actividades ilegales y crimen organizado que afectan la dinámica social e involucra, directa e indirectamente a varias comunidades a través de diferentes mecanismos de blanqueo de dinero ilícito. En este artículo, proponemos un enfoque de aprendizaje automático para el análisis de actividades sospechosas en corresponsales bancarios, un tipo de agente financiero que desarrolla transacciones financieras para clientes bancarios específicos. Este artículo utiliza varios algoritmos para identificar anomalías en un conjunto de transacciones de un corresponsal bancario durante 2019 para una ciudad intermediaria en Colombia. Nuestros resultados muestran que algunas metodologías son más apropiadas que otros para este caso y facilita la identificación de las anomalías y transacciones sospechosas en este tipo de intermediario financiero.#lavadoDeActivos#TransaccionesFinancierasThe activities of money laundering are a result of corruption, illegal activities, and organized crime that affect social dynamics and involved, directly and indirectly, several communities through different mechanisms to launder illegal money. In this article, we propose a machine learning approach to the analysis of suspicious activities in nonbanking correspondents, a type of financial agent that develops some financial transactions for specific banking customers. This article uses several algorithms to identify anomalies in a transaction set of a nonbanking correspondent during 2019 for an intermediary city in Colombia. Our results show that some methodologies are more appropriate than others for this case and facilitate to identify the anomalies and suspicious transactions in this kind of financial intermediary

Fighting money laundering with technology: a case study of Bank X in the UK

This paper presents a longitudinal interpretive case study of a UK bank’s efforts to combat Money Laundering (ML) by expanding the scope of its profiling of ML behaviour. The concept of structural coupling, taken from systems theory, is used to reflect on the bank’s approach to theorize about the nature of ML-profiling. The paper offers a practical contribution by laying a path towards the improvement of money laundering detection in an organizational context while a set of evaluation measures is extracted from the case study. Generalizing from the case of the bank, the paper presents a systems-oriented conceptual framework for ML monitoring

SchemaLine: Timeline visualization for sensemaking

Timeline visualization is an important tool for sense making. It allows analysts to examine information in chronological order and to identify temporal patterns and relationships. However, many existing timeline visualization methods are not designed for the dynamic and iterative nature of the sense making process and the various analysis activities it involves. In this paper, we introduce a novel timeline visualization, Schema Line, to address these deficiencies. Schema Line is designed to group notes into analyst-determined schema, using a layout algorithm to produce compact but aesthetically pleasing timeline visualization, and includes fluid user interactions to support sense making activities. It enables interactive temporal schemata construction with seamless integration with visual data exploration and note taking. Our preliminary evaluation results show that the participants found the new method easy to learn and use, and its features effective for the sense making activities for which it was designed

A framework for the forensic investigation of unstructured email relationship data

Our continued reliance on email communications ensures that it remains a major source of evidence during a digital investigation. Emails comprise both structured and unstructured data. Structured data provides qualitative information to the forensics examiner and is typically viewed through existing tools. Unstructured data is more complex as it comprises information associated with social networks, such as relationships within the network, identification of key actors and power relations, and there are currently no standardised tools for its forensic analysis. Moreover, email investigations may involve many hundreds of actors and thousands of messages. This paper posits a framework for the forensic investigation of email data. In particular, it focuses on the triage and analysis of unstructured data to identify key actors and relationships within an email network. This paper demonstrates the applicability of the approach by applying relevant stages of the framework to the Enron email corpus. The paper illustrates the advantage of triaging this data to identify (and discount) actors and potential sources of further evidence. It then applies social network analysis techniques to key actors within the data set. This paper posits that visualisation of unstructured data can greatly aid the examiner in their analysis of evidence discovered during an investigation