| --- |
| license: cc-by-4.0 |
| author : Christopher Alberto Hamonangan |
| --- |
| |
| # Overview |
|
|
| TSB-Fraud-Tabular is a synthetic tabular anomaly detection benchmark developed |
| by BhinnekaAI for evaluating machine learning methods that identify illicit |
| financial transactions and account takeovers in digital commerce platforms. |
|
|
| The benchmark focuses on financial discrepancies where fraudsters attempt to |
| evade detection by keeping transaction amounts within plausible ranges while |
| altering behavioral combinations, such as high transaction frequencies paired |
| with geographic anomalies during unusual hours. |
|
|
| Normal observations preserve expected statistical relationships between |
| transaction amounts, account balances, and user behavior. Anomalies are |
| introduced by disrupting these multivariate dependencies or injecting |
| right-skewed extreme behaviors. |
|
|
| The benchmark is intended for research on tabular anomaly detection, financial |
| fraud analysis, and the evaluation of global anomaly scoring methods. |
|
|
| ## Dataset information |
|
|
| | Property | Description | |
| | --------------------------- | ----------------------------------------------------------------- | |
| | Dataset name | TSB-Fraud-Tabular | |
| | Full name | Tabular Multi-Source Fraud Benchmark | |
| | Publisher | BhinnekaAI | |
| | Dataset Creator | Christopher Alberto Hamonangan | |
| | Domain | Digital banking, fintech, and financial fraud detection | |
| | Data modality | Structured numerical tabular data | |
| | Feature count | 5 numerical features | |
| | Data type | Synthetic | |
| | Task | Financial fraud anomaly detection | |
| | Expected anomaly proportion | Approximately 8.0% | |
| | Label convention | `0` = normal transaction; `1` = fraudulent transaction | |
| | Primary objective | Detect multivariate deviations from normal user spending profiles | |
|
|
| # Research Problem |
|
|
| Digital payment systems process millions of daily transactions involving |
| amounts, user balances, geographical locations, and temporal indicators. |
| Fraudulent transactions often do not appear as extreme outliers in individual |
| features, but rather as inconsistent multivariate patterns. |
|
|
| For example, a transaction may involve a normal amount, but originate from an |
| abnormal geographical distance during late-night hours for an account with a low |
| balance. |
|
|
| Anomaly detection methods relying strictly on individual feature thresholds |
| often fail in these scenarios. This benchmark emphasizes the detection of subtle |
| in-cluster relational anomalies. |
|
|
| The benchmark investigates: |
|
|
| - Detection of behavioral inconsistencies in financial logs. |
| - Performance under right-skewed Log-Normal economic distributions. |
| - Evaluation of global vs. local anomaly scoring methods under imbalanced |
| labels. |
|
|
| # Dataset Structure |
|
|
| The repository follows the two-directory structure used across the BhinnekaAI |
| HDS benchmark suite. |
|
|
| ### random-seed |
|
|
| | Property | Specification | |
| | --------------------- | -------------------------------------------------------------- | |
| | Directory | `1_Credit_Card_Fraud_Simulator/random-seed/` | |
| | Number of files | 5 | |
| | Observations per file | 5,000 | |
| | Naming convention | `dataset_5000_rows_seed{seed}.csv` (seeds: 11, 22, 33, 44, 55) | |
| | Intended use | Repeated-realization experiments | |
|
|
| ### 3.2. seed42 |
|
|
| | Property | Specification | |
| | ---------------------- | --------------------------------------- | |
| | Directory | `1_Credit_Card_Fraud_Simulator/seed42/` | |
| | Random seed | 42 | |
| | Number of files | 5 | |
| | File naming convention | `dataset_{size}_rows_seed42.csv` | |
|
|
| | Dataset size | Intended evaluation | |
| | -----------: | ---------------------------------------- | |
| | 100 | Small-sample behavior | |
| | 1,000 | Initial model comparison | |
| | 10,000 | Intermediate-scale evaluation | |
| | 50,000 | Large-scale evaluation | |
| | 100,000 | Maximum specified sample-size evaluation | |
|
|
| # Feature Specification |
|
|
| | Feature | Description | Data Type | |
| | ---------------------- | ---------------------------------------------- | ------------------------ | |
| | `tx_amount` | Numerical transaction amount | Continuous (Log-Normal) | |
| | `account_balance` | Current account balance of the user | Continuous | |
| | `daily_frequency` | Number of daily transactions executed | Discrete (Poisson) | |
| | `geo_distance` | Geographical distance from home location in km | Continuous (Exponential) | |
| | `night_time_indicator` | Binary indicator for late-night transactions | Binary (Binomial) | |
|
|
| # Mathematical Formulation |
|
|
| Normal transaction amounts follow a right-skewed Log-Normal distribution |
| reflecting standard economic expenditure: |
|
|
| tx_amount ~ LogNormal(μ = 5.5, σ = 0.6) |
| |
| Anomalies are generated by shifting distributions toward extreme economic values |
| while disrupting typical balance-to-amount ratios. |
| |
| # Loading the Dataset |
| |
| ```python |
| import pandas as pd |
| |
| df = pd.read_csv("seed42/dataset_10000_rows_seed42.csv") |
| X = df.iloc[:, :-1].values |
| y = df['is_fraud'].values |
| print(df.head()) |
| ``` |
