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Traditional AML methods struggle with dynamic schemes, outdated data, and manual burden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