The impact of the Fourth Anti-Money Laundering Directive on the valuation of EU banks

Premti, Arjan; Jafarinejad, Mohammad; Balani, Henry

doi:10.1016/j.ribaf.2021.101397

Cited by 13 publications

(5 citation statements)

References 40 publications

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“…The RSME computes the square root of the average of the squares of all errors, providing an estimate for how far off a model's prediction is from the true value. RMSE measures both the bias of the model and the variance so that it can provide deeper insights into how well a model is performing, whereas MSE just focuses on measuring bias Premti, Jafarinejad, and Balani 2021). Generally, RMSE is preferable to MSE because it pays more attention to large deviations than small ones, making it especially useful when evaluating forecasting algorithms beneficial in project management (Premti, Jafarinejad, and Balani 2021).…”

Section: Mse and Rmsementioning

confidence: 99%

“…of a significant amount of transactions, this does not necessarily indicate that financial institutions are doing a good job at detecting and reporting money laundering activities. Financial institutions face an array of challenges in identifying such activities because of their sheer volume, lack of resources or personnel trained to recognize suspicious activity, and inadequate systems and procedures(Premti, Jafarinejad, and Balani 2021;Coombs-Goodfellow and Lokanan 2018;Kurum 2020). …”

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confidence: 99%

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Predicting Suspicious Money Laundering Transactions using Machine Learning Algorithms

Lokanan

Maddhesia

2023

Preprint

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This study employs machine learning techniques to identify key drivers of suspicious activity reporting. The data for this study comes from all suspicious activities reported to the California government in 2018. In total, there were 45,000 records of data that represent various features. The paper uses linear regression along with Lasso, Ridge, and Elastic Net to perform feature regularization and address overfitting with the data. Other probabilistic and non-linear algorithms, namely, support vector machines, random forests, XGBoost, and CatBoost, were used to deal with the complexity of the data. The results from the mean squared and root mean squared errors indicate that the ensemble tree-based algorithm performed better than the statistical and probabilistic models. The findings revealed that filings from regulators, the type of products, and customers' relationships with the institutions were the top contributors to SAR filings. Through the evaluation of a vast amount of data, this study provides valuable insights for identifying suspicious activities in financial transactions and has the potential to significantly improve suspicious transaction monitoring.

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Section: Mse and Rmsementioning

confidence: 99%

mentioning

confidence: 99%

Predicting Suspicious Money Laundering Transactions using Machine Learning Algorithms

Lokanan

Maddhesia

2023

Preprint

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“…Legal regulation is also very important. The most recent, Fourth Anti-Money Laundering Directive (or 4AMLD) adopted by the European Union states that the identification of illegal money laundering in a country exposes companies to very severe fines and sanctions, or even to certain restrictions on companies' financial activities, which in the long run could have a negative impact on the countries' macroeconomic indicators (Koster, 2020;Raweh et al, 2017;Premti et al, 2021).…”

Section: Features Of the Money Laundering Processmentioning

confidence: 99%

“…The Financial Action Task Force (FATF) furthermore highlights the potential money laundering threats and their impact on any financial organization or its products (Kuznecova, 2021). With targeted existing and future FATF directives and focused antimoney laundering approaches by financial organizations within the European Union, it could be possible to make it easier for any financial institution to identify money laundering cases, and by following focused and already defined rules and precautions, ensure that money laundering cases are not missed or if they have occurred in the past, are presented in the future (Premti et al, 2021;Moneyval, 2018).…”

Section: Prevention Of Money-laundering Processmentioning

confidence: 99%

Efficiency of Anti-Money Laundering: The Case of Northern European Countries

Truskauskas

Taujanskaitė

2022

International Scientific Conference „Business and Management“

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As money laundering is a highly threatening and dangerous activity, its damage is only discussed when the consequences of these illegal activities become public, thus such kinds of investigations are particularly relevant. By fol-lowing public anti-money laundering (AML) directives and internal financial legislation of specific countries, it is pos-sible to improve any money laundering situation, anticipate potential risks and avoid various financial downturns. This paper examines the evolution of money laundering, the motives behind them, the potential economic consequences and highlights the need to suppress these activities to promote higher standards of AML, which could have a posi-tive impact on the countries’ economies. In the empirical part, according to the main macroeconomic indicators, 10 Northern European countries are analysed and the effectiveness of anti-money laundering processes, are identified. The analysis is made using several research steps, obtained results are compared and discussed. Methods used: scientific literature analysis, comparative analysis, data systematization, statistical data analysis, COPRAS, and cluster analysis. Conclusions and future research areas are mentioned.

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“…Further, Balani (2019) investigated the influence of AML legislation on bank stock prices in the USA. In an event study, Premti et al (2021) examined how the announcement of the Fourth AML Directive impacted European Bank’s performance. Further, Kodongo (2018) assessed the influence of financial regulation on financial inclusion in Kenya, whereas Anarfo et al (2020) investigated the impact of financial regulation on financial inclusion in sub-Sahara African.…”

Section: Introductionmentioning

confidence: 99%

Anti-money laundering regulations and financial inclusion: empirical evidence across the globe

Ofoeda

2022

JFRC

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Purpose This study aims to examine the impact of anti-money laundering (AML) regulations on financial inclusion using a comprehensive measure of AML regulations developed by the Basel Institute on Governance. Again, this study investigates the existence of threshold effects in the AML regulations–financial inclusion nexus. Design/methodology/approach This study uses panel data across 212 economies (developed, developing and Africa) of the globe-spanning from 2012 to 2019. This study uses the dynamic panel threshold estimation technique proposed by Seo et al. (2019). Findings In general, the results indicate that AML regulations promote financial inclusion across the globe. However, AML regulations spur financial inclusion below the threshold of AML regulations, whereas, above the thresholds, AML regulations have damaging effects on financial inclusion. Further, the author finds that AML regulations have a detrimental impact on financial inclusion for developed economies. In contrast, AML regulations promote financial inclusion at all levels of AML regulations for African countries. Practical implications The findings of this study imply that countries must make conscious efforts in combating the incidence of money laundering by establishing sound AML regulatory regimes as a means of promoting financial inclusiveness. However, there is a need for regulators to ensure cost-effective and efficient implementation of AML regulations. Originality/value The value of this paper is its contribution to literature as it is a major attempt in empirically assessing the impact of AML regulations on financial inclusion. Again, to the best of the author’s knowledge, this is the first study to examine the non-linear relationship between AML regulations and financial inclusion.

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The impact of the Fourth Anti-Money Laundering Directive on the valuation of EU banks

Cited by 13 publications

References 40 publications

Predicting Suspicious Money Laundering Transactions using Machine Learning Algorithms

Predicting Suspicious Money Laundering Transactions using Machine Learning Algorithms

Efficiency of Anti-Money Laundering: The Case of Northern European Countries

Anti-money laundering regulations and financial inclusion: empirical evidence across the globe

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