The book “Money laundering phenomenon – foundations, trends, impact and governance in banking industry” explores foundation, models (methods) to measure money laundering, trends, auditing Anti-Money Laundering structures in banking industry, AML risk assessment, impact/effects of money laundering, governance, and regulatory point of view.

The book includes some models that have been used to measure the amount of money laundering, respectively the effect of money laundering in various macroeconomic variables such as economic growth, consumption, investment, government expenditure, export and import. Moreover, there are presented some trends that may dominate in the AML field for coming years.

The strengthening of international cooperation in the field of anti-money laundering have given power to international organisations to recommend mitigating measures, standards and principles and applying them in practise, this is why in the book a special chapter is dedicated to international bodies and organisations to fight money laundering phenomenon.

Due to high importance of the AML risk assessment, as a risk management mechanism framework, a specific chapter is dedicated to it.

A special attention is dedicated to the auditing of AML structures in banking and the role of audit in general to AML field.

Last, but not least, in the book a chapter is dedicated to determinants of informal economy, since money laundering and informal economy are interconnected and needs to be studied as one of the challenges for countries.

We believe our book contributes to current literature on money laundering phenomena and that it is important for bankers, policy makers, strategists, academics, and students.

Financial time series including high frequency structures like jumps, spikes and stochastic volatility are usually modeled in an ad-hoc manner by stochastic differential equations together with Levy processes. Estimating the parameters and determining the jump size distributions do not have a precise and universally accepted method. Under these circumstances, complexities and confusions usually arise. This book, approaches to this issue from a very different angle through introducing an autocorrelation one process together with finite number of Fourier series terms. Introduction of Fourier series to estimate the dynamics of the process is not done in an ad-hoc manner or as done before in dealing with seasonality. Here the moving average is transformed to a “moving and fluctuating” average by the help of Fourier series. Instead of adding jump structures to the model which makes the parameter estimation quite cumbersome, our model in discrete time can easily be transformed to a well-known mean reverting continuous time process. Moreover, our alternative model turned out to be a quite powerful and accurate forecasting technique.