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Fraud Detection in Bank Transactions [Our Reference Paper.]

This repository contains the solution for the Innotech AI Competition, focused on detecting fraudulent activities in bank transactions using advanced machine learning techniques. The project applies state-of-the-art algorithms to identify potentially fraudulent transactions with high accuracy.

Final Perpose Model(Graph Transformer)

Final Perpose Model(Graph Transformer)

Table of Contents

Methodology

The approach to fraud detection in this project involves several key steps:

  1. Data Preprocessing:

    • Handling missing values.
    • Feature engineering to create meaningful features from raw data.
    • Data normalization and scaling.

  2. Exploratory Data Analysis (EDA):

    • Visualizing the distribution of features.
    • Identifying correlations between features.
    • Detecting any anomalies in the data.

  3. Model Selection:

    • Comparison of different machine learning models including Random Forest, Gradient Boosting, and Neural Networks.
    • Use of cross-validation techniques to ensure model robustness.
    • Hyperparameter tuning to optimize model performance.

  4. Evaluation Metrics:

    • Precision, recall, F1-score, and AUC-ROC curve were used to evaluate the model performance.
    • Special emphasis on minimizing false positives to avoid disrupting legitimate transactions.

Model Architecture

  • Convolutions:
    • conv1: Edge Convolution (EdgeConv) for initial node feature aggregation.
    • conv2 & conv3: Transformer-based convolutions (TransformerConv) with skip connections for advanced feature extraction.
  • Batch Normalization:
    • Applied after each convolutional layer to stabilize and accelerate training.
  • Skip Connections:
    • Skip connections are integrated into the convolutional layers (conv2 and conv3) to retain information from previous layers.
  • Linear Layers:
    • lin1: Reduces the dimensionality of the features before the final classification.
    • lin: Final classifier layer to output node class probabilities.
  • Global Mean Pooling:
    • Aggregates node features into a graph-level representation.

Forward Pass

  1. Node Embeddings:
    • Nodes are passed through a series of convolutional layers with skip connections to refine their embeddings.
  2. Readout Layer:
    • Node features are aggregated at the graph level using global mean pooling.
  3. Classification:
    • The pooled features are passed through linear layers to predict the class of each node.

Training

  • Optimizer: Adam optimizer with a learning rate of 0.01.
  • Loss Function: Negative Log-Likelihood Loss (NLLLoss) for classification.

Dataset

The dataset used for this project was provided by the Innotech AI Competition and contains a large number of bank transaction records, including both legitimate and fraudulent transactions. The data includes features such as transaction amount, transaction time, account age, and more.

  • Dataset Source: Provided by Innotech AI Competition.
  • Number of Transactions: 100K.
  • Number of Features: 10.

Results

The model was evaluated using the test set provided by the competition, and it achieved the following results:

  • Accuracy: 0.85
  • Recall: 0.83
  • F1-Score: 0.84

Usage

You can check the jupyter files out and find the solution. check them base on this order: 1-Data Loader.ipynb 2-model-with-node_attr.ipynb

Requirments

Pytorch
Pytorch geometric
Numpy
Pandas
Matplotlib

Refrences

  • Yun, Seongjun, et al. "Graph Transformer Networks." International Conference on Learning Representations, 2020. Link to paper.