Artificial Intelligence and Bank Soundness: A Done Deal? - Part 1

Banks soundness plays a crucial role in determining economic prosperity. As such, banks are under intense scrutiny to make wise decisions that enhances bank stability. Artificial Intelligence (AI) plays a significant role in changing the way banks operate and service their customers. Banks are becoming more modern and relevant in people’s life as a result. The most significant contribution of AI is it provides a lifeline for bank’s survival. The chapter provides a taxonomy of bank soundness in the face of AI through the lens of CAMELS where C (Capital), A(Asset), M(Management), E(Earnings), L(Liquidity), S(Sensitivity). The taxonomy partitions opportunities from the main strand of CAMELS into distinct categories of 1 (C), 6(A), 17(M), 16 (E), 3(L), 6(S). It is highly evident that banks will soon extinct if they do not embed AI into their operations. As such, AI is a done deal for banks. Yet will AI contribute to bank soundness remains to be seen

Detección y prevención del lavado de activos: perspectiva desde la auditoría forense

This document seeks to expose how forensic auditing can help prevent and detect money laundering activities within organizations, taking into account a clear definition of the concept of money laundering, the fight of organizations worldwide and the application of procedures of forensic audit for the prevention and detection of these activities, for this, a descriptive methodology was developed, conducting a review of academic articles to interrelate them on the topic developed. Finally, it was obtained that money laundering is one of the crimes with the greatest impact on the economy at a global level, and whose fight is being faced by different multilateral organizations, incorporating techniques and tools from different areas of knowledge, the forensic audit It has a great variety and potential of techniques that allow the auditor to collect enough evidence to detect money laundering and thus be able to correct control structures that lead to the strengthening of prevention mechanisms for this type of operation. The fight against money laundering must be consolidated as a global objective, where all countries cooperate to counteract its effects on the population and local economies.En el presente documento se define el concepto de lavado de activos, se expone cómo la auditoría forense contribuye a detectar y prevenir las actividades relacionadas al interior de las organizaciones, y las gestiones de los organismos internacionales para impedirlas; para ello se adoptó una metodología de tipo descriptivo mediante la revisión de los artículos académicos que permiten interrelacionarlos con el tema. Las conclusiones señalan que el lavado de activos es uno de los delitos de mayor impacto en la economía global, que está siendo enfrentada por diferentes organismos multilaterales, incorporando técnicas y herramientas de diferentes áreas del conocimiento; en ese sentido la auditoría forense cuenta con una gran variedad de técnicas que le permiten al auditor recolectar la evidencia suficiente para detectar el delito, y de esa manera corregir las estructuras de control que den lugar al fortalecimiento de los mecanismos de prevención. Enfrentar el lavado de activos debe ser un objetivo mundial en el que todos los países cooperen con el fin de contrarrestar sus efectos en las economías locales

Auto-Encoder based Deep Representation Model for Image Anomaly Detection

Image anomaly detection is to distinguish a small portion of images that are different from the user-defined normal ones. In this work, we focus on auto-encoders based anomaly detection models, which assess the probability of anomaly by measuring reconstruction errors. One of the critical steps in image anomaly detection is to extract robust and distinguishable representations that could separate abnormal patterns from normal ones. However, current auto-encoder based methods fail to extract such distinguishable representations because their optimization objectives are not tailored for this specific task. Besides, the architectures of those models are unable to capture features that are robust to irrelevant distortions but sensitive to abnormal patterns. In this work, two auto-encoder based models are proposed to address the aforementioned issues in optimization objectives and model architectures, respectively. The first model learns to extract distinct representations for abnormal patterns by imposing sparse regularizations on the latent space during the optimization process. This sparse regularization makes the extracted abnormal features unable to be represented as sparse as the normal ones. The second model detects abnormal patterns using Asymmetric Convolution Blocks, which strengthens the crisscross part of the convolutional kernel, making the extracted features less sensitive to geometric transformations. The experimental results demonstrate the superiority of both proposed models over other auto-encoder based anomaly detection models on popular datasets. The proposed methods could also be easily incorporated into most anomaly detection methods in a plug-and-play manner

Use of Regulatory Policies in the Fight against Money Laundering in Kenya

Purpose: It was found necessary to undertake this study so as to bridge the knowledge gap as concerns the use of regulatory policies in the fight against Money Laundering in Kenya. The general objective of the study was to examine the regulatory mechanisms that Kenya has adopted in dealing with money laundering and to suggest ways of enhancing the effectiveness of these mechanisms to serve as veritable models for other African states. The specific objectives of the study were: - to investigate the factors that have influenced the adoption of money laundering practices in Kenya; to analyze the extent to which money laundering regulatory policies have been adopted by financial institutions in Kenya; and to evaluate the challenges faced in implementation of money laundering regulatory policies among financial institutions in Kenya. The Nairobi Stock Exchange report of January 2009 indicates that the total number of Banks listed on the Nairobi Stock Exchange is nine, with their headquarters strategically located in Nairobi (Appendix I). Consequently, the study focused on the Banks listed on the Nairobi Stock Exchange. The study respondents were the Compliance Heads of the listed banks. In addition, the Researcher considered two telecommunication service providers that are licensed to undertake money transfer services. These are: - Safaricom and Zain. Methods: The study utilized a combination of quantitative and qualitative techniques in the collection of secondary and primary data. A semi-structured questionnaire (having both open and closed questions) will be the main data collection instrument. The researcher also use interview schedules with open questions, aimed at meeting the objectives of the study. Primary data was analyzed by employing descriptive statistics such as percentages, mean scores and standard deviations. Statistical Package for Social Sciences was used as an aid in the analysis. The researcher preferred SPSS because of its ability to cover a wide range of the most common statistical and graphical data analysis. Computation of frequencies in tables, charts and bar graphs was used in data presentation. The information was presented and discussed as per the objectives and research questions of the study.Results and analysis: Findings of the study show that the factors influencing adoption of money laundering practices in Kenya include the legal framework, corporate governance policies in institutions, quality of human capital, Information and Communication Technology (ICT), and innovations in the economy. The findings further show that the challenges faced in implementation of money laundering regulatory policies among financial institutions in Kenya include the following:- structural challenges, which include that liberalized and cash based economy, different legal systems among countries, different banks applying different money laundering policies, unstable neighboring regime and  parallel banking and alternative remittance avenues (corruption); Legal and institutional framework challenges; difficulty in obtaining due diligence documents from customers; and perceived cost of implementing an AML regime. Keywords: Regulatory policies, Money laundering, Blacklisting, Correspondent Banking, Financial Action Task Force, Financial Exclusion, Know Your Customer Concept, Money Laundering, Politically Exposed Persons, Shell Bank

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