Richard Self _ BA(Hons) and LLM _ University of Derby, Derby _ Department of Computing and Maths _ ResearchGate u s anti money laundering laws

This chapter uses the five V’s of big data (volume, velocity, variety, veracity, and value) to form the basis for consideration of the current status and issues relating to the introduction of big data analysis into organizations. The first three are critical to understanding the implications and consequences of available choices for the techniques, tools, and order to provide an understanding of choices that need to be made based on understanding the nature of the data sources and the content. All five V’s are invoked to evaluate some of the most critical issues involved in the choices made during the early stages of implementing a big data analytics project. Big data analytics is a comparatively new field; as such, it is important to recognize that elements are currently well along the gartner hype cycle into productive use.

The concept of the planning fallacy is used with information technology project success reference class data created by the standish group to improve the success rates of big data projects.U s anti money laundering laws international organization for standardization 27002 provides a basis considering critical issues raised by data protection regimes in relation to the sources and locations of data and processing of big data.

Big data analytics is a rapidly developing field which already shows early promising successes. There are considerable synergies between this and knowledge management: both have the goal of improving decision-making, fostering innovation, fuelling competitive edge and economic success through the acquisition and application of knowledge. Both operate in a world of increasing deluges of information, with no end in sight. Big data analytics can be seen as a threat to the practice of knowledge management: it could relegate the latter to the mists of organizational history in the rush to adopt the latest techniques and technologies. Alternatively, it can be approached as an opportunity for knowledge management in that it wrestles with many of the same issues and dilemmas as knowledge management, the key, it is argued, lies in the application of the latter’s more social and discursive construction of knowledge, a growing trend in knowledge management.U s anti money laundering laws the present paper explores the synergies, opportunities and contingencies available to both fields.

Analysing large amounts of financial information within databases can be hardly accomplished when dealing with money laundering. The main reason is the lack of digital forensics and proper database analysis procedures within the anti-money laundering strategies of financial institutions. Also, analysing single or grouped financial events related to money laundering is difficult when the know-your-customer policies in these institutions are not enforced, or even used as evidentiary instruments to gather digital evidence and track suspicious customers through the whole investigation life cycle. Even though the relevant data sources to get information from can be identified and used to create suspicious activity reports, they need to be protected from money laundering events, and by these means, prevent their confiscation.U s anti money laundering laws hence, in this article, we propose a methodology for combining digital forensics and database analysis in order to enhance money laundering detection. Additionally, in order to tackle the lack of synergy between the KYC policies and information security requirements, we enhance our previous model by analysing the FATF recommendations, the basel frameworks along with the BS ISO/IEC 27001, 27002 and 27037 standards in order to incorporate some of their best-practices into a methodology for money laundering detection model to deliver a set of requirements and activities for customer verification and financial evidence extraction before, during, and after a suspicious activity takes place.

Digital forensics is the science that identify, preserve, collect, validate, analyse, interpret, and report digital evidence that may be relevant in court to solve criminal investigations.U s anti money laundering laws conversely, money laundering is a form of crime that is compromising the internal policies in financial institutions, which is investigated by analysing large amount of transactional financial data. However, the majority of financial institutions have adopted ineffective detection procedures and extensive reporting tasks to detect money laundering without incorporating digital forensic practices to handle evidence. Thus, in this article, we propose an anti-money laundering model by combining digital forensics practices along with database tools and database analysis methodologies. As consequence, admissible suspicious activity reports (sars) can be generated, based on evidence obtained from forensically analysing database financial logs in compliance with know-your-customer policies for money laundering detection.

The financial action task force issued 40 recommendations and 9 special recommendations in order to help financial institutions implement anti–money laundering methods and techniques to protect the international financial systems from the evolving money laundering typologies, including financing terrorism and drug dealing.U s anti money laundering laws

On the other hand, the bank for international settlements (BIS) issued two frameworks to help financial institutions; in particular, the central banks, to secure their financial transactions by means of an adequate measure of capital and liquidity. The basel II framework intends to secure the surrounding requirements governing the capital adequacy in the whole banking system, including the central banks. Additionally, the basel III framework strengthens the global liquidity regulations in order to absorb shocks arising from financial and economic stress.

However, these recommendations and frameworks have some limitations such as the lack of both computer forensic practices and IT management considerations, the consequences of which may cause that the anti-money laundering evidence identified in the central bank of ecuador (BCE) cannot be admissible during criminal and civil litigations in the ecuadorian jurisdiction, and at the same time, dismissed if this evidence is required for investigating money laundering activities overseas.U s anti money laundering laws

Therefore, considering that information is an important asset that needs to be protected, money laundering-related information has to be properly managed within the BCE. Then, this anti-money laundering (AML) framework provide guidelines in order to understand what the central bank of ecuador (BCE), should be doing in terms of money laundering detection. Similarly, this AML framework incorporates the FATF recommendations and the basel frameworks on its scope, so its general principles and

Characteristics can be applied by outlining a straightforward process aligned with the IT managements objectives in terms of security and risk assessment, along with the necessary practices to assure digital evidence identification, collection, acquisition, and preservation in a forensically sound manner.