Navigating the Complexity of Money Laundering: Anti–money Laundering Advancements with AI/ML Insights

Figure 1:

Figure 2:

Figure 3:

Figure 4:

Figure 5:

Figure 6:

Figure 7:

Performance metrics of different models for debit card classification

Model	Accuracy	Precision	Recall	F1 score	ROC_AUC score
MLP (DL)	0.65	0.55	0.42	0.48	0.60
CNN (DL)	0.68	0.60	0.54	0.57	0.65
Random forest classifier	0.69	0.63	0.50	0.55	0.68
Logistic regression	0.68	0.59	0.56	0.58	0.66
Gradient boosting	0.70	0.68	0.42	0.52	0.65
KNN	0.61	0.50	0.50	0.51	0.60

Traditional AML methods struggle with dynamic schemes, outdated data, and manual burden

Traditional methods	Challenges
Watchlists and blacklists [15]	Limited effectiveness in identifying novel or evolving money-laundering schemes, reliance on static lists
Transaction monitoring [15]	Overreliance on historical data, potential to miss emerging patterns, and high manual review workload
CDD [16]	Difficulty in maintaining up-to-date customer profiles, potential for false negatives in risk assessments
Manual investigations [16]	Are labor-intensive, prone to human error, and may result in delays in identifying suspicious activities

Confusion matrix illustrating the predicted versus actual classifications of financial transactions

	Predicted nonfraud	Predicted fraud
Actual nonfraud	3,689	62
Actual fraud	62	123

Performance metrics of different models for year and state

Algorithms	MSE	R2	MAE
Elastic net regressor	3.25	0.10	1.085
LASSO regression	3.28	0.10	0.840
Random forest	2.50	0.60	0.800
Gradient boosting regressor	2.90	0.24	1.010
Linear regression	3.20	0.50	1.060

The rising cost of AML shortcomings, urging stricter compliance across diverse sectors

Company name/bank	Fine	Year	Reason for fine
Binance Holdings Ltd (US)	$4 billion	2023	Breaches of Bank Secrecy Act, failure to register as money transmitter, violations of the International Emergency Economic Powers Act
Crown Resorts Ltd (Australia)	$450 million	2023	Past infractions of Australian AML regulations at casinos
Deutsche Bank (Germany)	$186 million	2023	Insufficient efforts to remedy money-laundering control and other weaknesses
Bank of Queensland (Australia)	$50 million (potential)	2023	Breaches of prudential norms and AML regulations
William Hill & Mr Green (UK)	£19.2 million	2023	Violations of AML and social responsibility regulations
Guaranty Trust Bank UK Ltd	£7.6 million	2023	Serious flaws in AML procedures and controls
ADM Investor Services International Ltd (UK)	£6.47 million	2023	Inadequate AML procedures and controls
In Touch Games Ltd (UK)	£6.1 million	2023	Failing to adequately handle money-laundering and social responsibility issues
HSBC (Mexico and Colombia)	$1.9 billion (£1.2 billion)	2023	Inadequate controls against money laundering
Credit Suisse Group (US)	$536 million	2009	Money-laundering allegations
Lloyds Banking Group PLC (UK)	$350 million	2009	Money-laundering allegations
ING Bank Group (the Netherlands)	$619 million	2012	Facilitating illegal movement of billions through the US banking system
Standard Bank PLC (UK)	$7.6 million	2014	Shortcomings in AML controls

Advanced techniques for AML: challenges in data labeling, interpretability, and evolving threats

ML algorithms/techniques	Challenges
ML algorithms [18]	Need for substantial labeled data, interpretability concerns, and potential biases in training data
Predictive analytics [19]	Dependence on accurate historical data, challenges in predicting novel or emerging techniques
NLP [20]	Handling diverse language nuances, extracting meaningful insights from vast textual data
Anomaly detection [21]	Balancing sensitivity and specificity, adapting to evolving tactics of sophisticated criminals
Big data analytics [22]	Ensuring scalability, data quality, and the need for robust infrastructure

Performance metrics of different models for credit card classification

Model	Accuracy	Precision	Recall	F1 score	ROC_AUC score
MLP (DL)	0.75	0.31	0.06	0.10	0.51
CNN (DL)	0.76	0.55	0.03	0.05	0.51
Random forest classifier	0.77	0.56	0.07	0.13	0.53
Logistic regression	0.77	0.51	0.13	0.21	0.55
Gradient boosting	0.77	0.55	0.20	0.29	0.57
KNN	0.75	0.44	0.24	0.31	0.57