RGCN Fraud Detector

Install Prerequisites

pip install -r requirements.txt

Model Class

FraudRGCN class in fgnn/fraud_detector.py implements model for supervised fraud detection using Relational Graph Convolutional Network (RGCN). The model is implemented with PyTorch and Deep Graph Library. Class methods support full lifecycle of the model:

• train_fg(train_transactions, params=None, test_mask=None) Model training. Trains RGCN model on transactions in train_transactions DataFrame. Default model parameters can be overloaded by passing them in a dictionary params. Model parameters are described below. When array test_mask is passed, it is used to identify test transactions in train_transactions, and the model will be trained in transductive mode by masking out fraud labels of test transactions identified with True values in test_mask.

• predict(test_transactions, k=2) Model inference. Returns fraud probabilities for transactions in test_transactions DataFrame. Parameter k is passed to dgl.khop_out_subgraph and controls number of hops used when extracting subgraph around target nodes from test_transactions.

• save_fg(model_dir) Model serialization. Saves model to a directory.

• load_fg(model_dir) Model deserialization. Loads model from a directory.

Model Parameters

The following model parameters control graph construction from a DataFrame with transactions:

• target_col column name to be used to create target nodes in the graph
• label_col column name with binary labels for target nodes (1=fraud, 0=legit)
• node_cols comma-separated list of columns that will be used to create entity (non-target) nodes in the graph
• cat_cols comma-separated list of columns that will be used as categorical features of target nodes
• num_cols comma-separated list of columns that will be used as numerical features of target nodes

Default Model Parameters

{
    'num_gpus': 0, # 1=gpu, 0=cpu
    'embedding_size': 128,  # size of node embeddings
    'n_layers': 2,  # number of graph layers
    'n_epochs': 50,  # number of training epochs
    'n_hidden': 16,  # number of hidden units
    'dropout': 0.2,  # dropout rate
    'weight_decay': 5e-6,  # L2 penalization term
    'lr': 1e-2,  # learning rate
    'target_col': 'TransactionID',  # target (transaction-id) column
    'node_cols': 'card1,card2,card3,card4,card5,card6,ProductCD,addr1,addr2,P_emaildomain,R_emaildomain',  # columns to create nodes
    'label_col': 'isFraud',  # label column
    # categorical feature columns:
    'cat_cols': 'M1,M2,M3,M4,M5,M6,M7,M8,M9,DeviceType,DeviceInfo,id_12,id_13,id_14,id_15,id_16,id_17,id_18,id_19,id_20,id_21,id_22,id_23,id_24,id_25,id_26,id_27,id_28,id_29,id_30,id_31,id_32,id_33,id_34,id_35,id_36,id_37,id_38',
    # numerical feature columns:
    'num_cols': 'TransactionAmt,dist1,dist2,id_01,id_02,id_03,id_04,id_05,id_06,id_07,id_08,id_09,id_10,id_11,C1,C2,C3,C4,C5,C6,C7,C8,C9,C10,C11,C12,C13,C14,D1,D2,D3,D4,D5,D6,D7,D8,D9,D10,D11,D12,D13,D14,D15,V1,V2,V3,V4,V5,V6,V7,V8,V9,V10,V11,V12,V13,V14,V15,V16,V17,V18,V19,V20,V21,V22,V23,V24,V25,V26,V27,V28,V29,V30,V31,V32,V33,V34,V35,V36,V37,V38,V39,V40,V41,V42,V43,V44,V45,V46,V47,V48,V49,V50,V51,V52,V53,V54,V55,V56,V57,V58,V59,V60,V61,V62,V63,V64,V65,V66,V67,V68,V69,V70,V71,V72,V73,V74,V75,V76,V77,V78,V79,V80,V81,V82,V83,V84,V85,V86,V87,V88,V89,V90,V91,V92,V93,V94,V95,V96,V97,V98,V99,V100,V101,V102,V103,V104,V105,V106,V107,V108,V109,V110,V111,V112,V113,V114,V115,V116,V117,V118,V119,V120,V121,V122,V123,V124,V125,V126,V127,V128,V129,V130,V131,V132,V133,V134,V135,V136,V137,V138,V139,V140,V141,V142,V143,V144,V145,V146,V147,V148,V149,V150,V151,V152,V153,V154,V155,V156,V157,V158,V159,V160,V161,V162,V163,V164,V165,V166,V167,V168,V169,V170,V171,V172,V173,V174,V175,V176,V177,V178,V179,V180,V181,V182,V183,V184,V185,V186,V187,V188,V189,V190,V191,V192,V193,V194,V195,V196,V197,V198,V199,V200,V201,V202,V203,V204,V205,V206,V207,V208,V209,V210,V211,V212,V213,V214,V215,V216,V217,V218,V219,V220,V221,V222,V223,V224,V225,V226,V227,V228,V229,V230,V231,V232,V233,V234,V235,V236,V237,V238,V239,V240,V241,V242,V243,V244,V245,V246,V247,V248,V249,V250,V251,V252,V253,V254,V255,V256,V257,V258,V259,V260,V261,V262,V263,V264,V265,V266,V267,V268,V269,V270,V271,V272,V273,V274,V275,V276,V277,V278,V279,V280,V281,V282,V283,V284,V285,V286,V287,V288,V289,V290,V291,V292,V293,V294,V295,V296,V297,V298,V299,V300,V301,V302,V303,V304,V305,V306,V307,V308,V309,V310,V311,V312,V313,V314,V315,V316,V317,V318,V319,V320,V321,V322,V323,V324,V325,V326,V327,V328,V329,V330,V331,V332,V333,V334,V335,V336,V337,V338,V339',
    'class_weight': 1.  # class weight for fraud label, 1/class_weight will be used as weight for legit label
}

Model Evaluation and Deployment

The model is evaluated on the IEEE CIS fraud dataset from the Kaggle competition. The evaluation code is organized in the following three notebooks:

• 01-Prepare-Data.ipynb Download the dataset, and create training and test splits. We only use competition's training data in our evaluation, since only these transactions have fraud labels. We sort transactions by timestamp (TransactionDT) column and use 80% of transactions for training, while the last 20% of transactions are used for testing.

• 02-Train-Fraud-RGCN.ipynb Train the model in inductive and transductive modes. We train each model five times.

• 03-Predict-Fraud-RGCN.ipynb Evaluate trained models and plot model performance (ROC AUC) for different k-hop values with k=[1,2,3]. Also, use inductive models to predict on test transactions in batches of ~1000 transactions, plot model performance and average latency.

• 04-SM-Deploy-RGCN.ipynb Train RGCN model as a SageMaker estimator and deploy it to an endpoint for inference.

Security

See CONTRIBUTING for more information.

License

This library is licensed under the MIT-0 License. See the LICENSE file.