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Synthetic financial transaction dataset summary
| Parameter | Value |
|---|---|
| Number of customers | 442 |
| Number of accounts | 442 |
| Approximate number of transactions per customer | 184 |
| Approximate time period of transactions | 12 months |
| Total number of transactions | 92,824 |
| Labeled suspicious transactions | 4,054 |
| Labeled legitimate transactions | 88,770 |
Sample of two original transaction records considered for prediction by CNN and interpretation by SHAP
| Features | Suspicious transaction | Legitimate transaction |
|---|---|---|
| Transaction date | 7/12/2017 | 6/02/2018 |
| Transaction number | 339549 | 359932 |
| Transaction account | 10300015 | 10202449 |
| Transaction amount | 6,000.00 | 322.00 |
| Credit | 6,000.00 | – |
| Debit | – | 322.00 |
| Balance | 52,659.00 | 16,054.00 |
| Transaction type | Credit | Debit |
| Transaction subtype | Cash deposit | Auto-debit |
| Transaction description | Cash deposit | Health insurance |
| Transaction currency | AUD | AUD |
| Transaction location type | ATM | Online |
| Transaction location code | 448 | 222 |
| Target account | 0 | 891141 |
| Target country code | 0 | Australia |
| Target bank code | 0 | 559059 |
| Customer ID | 20000736 | 20002452 |
| Customer type | Student | Individual |
| Gender | Male | Female |
| Date of birth | 24/09/1992 | 26/05/1965 |
| Age | 28 | 55 |
| Marital status | Single | Married |
| Residence country | Australia | Australia |
| State | New South Wales | New South Wales |
| City | Sydney | New Castle |
| Postcode | 2358 | 2361 |
| Tax resident country | Australia | Australia |
| Birth country | Overseas country | Australia |
| Nationality country | Overseas country | Australia |
| Profession | Student | Laborers |
| Income category | 4000 | 77668 |
| KYC updated on date | 22/04/2017 | 13/09/2019 |
| KYC state | Active | Active |
| Risk rating | 0 | 0.463290428 |
| Account number | 10300015 | 10202449 |
| BSB number | 203901 | 201807 |
| Account created on date | 22/04/2017 | 23/08/2017 |
| Account type | Savings | Savings |
| Daily transaction limit | 3,000 | 2,000 |
| TFN | 999528645 | 968305061 |
| Statement delivery method | Not set | Online |
CNN architecture hyperparameters
| Layer | Parameters |
|---|---|
| Conv1D | Filters = 32, Kernel size = 2, Input shape = 51,980 × 40, Activation = ReLU |
| Batch normalization | Axis = −1, momentum = 0.99, center = true, scale = true |
| Dropout | 0.3 |
| Conv1D | Filters = 64, Kernel size = 2, Activation = ReLU |
| Batch normalization | Axis = −1, momentum = 0.99, center = true, scale = true |
| Dropout | 0.3 |
| Conv1D | Filters = 128, Kernel size = 2, Activation = ReLU |
| Batch normalization | Axis = −1, momentum = 0.99, center = true, scale = true |
| Dropout | 0.3 |
| Flatten | Axis = −1, momentum = 0.99, center = true, scale = true |
| Dropout | 0.3 |
| Dense | Units = 512, Activation = ReLU |
| Dropout | 0.3 |
| Dense | Units = 1, Activation = Sigmoid |
Overlapping transaction scenarios that shares the characteristics of legitimate and suspicious transactions
| S. No. | Scenario description |
|---|---|
| OL-1 | Wire transfer of money to offshore accounts from savings account |
| OL-2 | Cash withdrawal from the account in the range of AU $2,000 to AU $5,000 |
| OL-3 | Wire transfer of money from offshore account into savings account |
| OL-4 | Shopping in the range of AU $10,000 to AU $30,000 |
Hyperparameters for RF, XGBoost, and SVM
| Classifier | Hyperparameter | Value |
|---|---|---|
| RF | Number of trees in the forest | 100 |
| RF | Minimum number of data points in a node prior splitting | 2 |
| RF | Minimum number of data points allowed in a leaf node | 1 |
| RF | Maximum number of features for splitting a node | sqrt |
| RF | Method for sampling data points | True |
| RF | Class weight | 0:1, 1:100 |
| XGBoost | Minimum number of data points in a node prior splitting | 2 |
| XGBoost | Minimum number of data points allowed in a leaf node | 1 |
| XGBoost | Learning rate | 0.1 |
| XGBoost | Number of decision trees to be boosted | 100 |
| XGBoost | Subsample ratio of training data | 1 |
| XGBoost | Maximum depth | 3 |
| SVM | C | 1.0 |
| SVM | Kernel | Linear |
| SVM | Gamma | Scale |
Scenarios to develop money laundering transactions
| S. No. | Scenario description |
|---|---|
| ML-1 | Small deposits (<AU $5,000) of money through ATM by multiple people into a single account (<AU $10,000 per day) over a month. Then the same money is transferred in batches of AU $10,000 to $30,000 to multiple overseas accounts in different countries. |
| ML-2 | Small deposits (<AU $5,000) of money through ATM by multiple people into a single account (<AU $10,000 per day) over a month. Then the same money is used to buy luxurious items locally in the range of AU $10,000 to AU $90,000 (vehicles, gold, property, etc.). |
| ML-3 | Transfer of money from multiple overseas accounts from multiple countries and using the same to buy luxurious items in the range of AU $10,000 to AU $90,000 (vehicles, gold, property). |
| ML-4 | Transfer of money from multiple overseas accounts from multiple countries and withdraw the same through ATM over next couple of months in a small quantity in the range of AU $2,000 to AU $4,900. |
| ML-5 | Deposit a small amount of money in the range of AU $2,000 to AU $4,500 each month to ATM deposit machine and transfer the deposited amount online to an account in a different local bank (but same account) the next day. |
Suspicious transaction prediction results of CNN, RF, XGBoost, and SVM models
| Metrics | CNN | RF | XGBoost | SVM |
|---|---|---|---|---|
| fβ score | 78.23% | 61.97% | 62.09% | 30.86% |
| Recall | 91.01% | 59.82% | 60.14% | 29.10% |
| Precision | 34.56% | 91.53% | 87.72% | 67.47% |
| Accuracy | 92.03% | 97.95% | 97.84% | 96.20% |
| AUC | 98.00% | 79.80% | 98.40% | 83.60% |
| TPs | 1,114 | 746 | 750 | 363 |
| TNs | 24,515 | 26,532 | 26,496 | 26,426 |
| FPs | 2,109 | 69 | 105 | 175 |
| FNs | 110 | 501 | 497 | 884 |
| Training time | 70 min | 6 s | 16 s | 4.4 min |