Overview

Fraud Detection using Machine Learning involves deploying a machine learning (ML) model and an example dataset of credit card transactions to train the model to recognize fraud patterns. This approach enables systems to identify fraudulent activities with high accuracy using algorithms like Gradient Boosting, LSTM, Autoencoders, and Contrastive Learning.

Gradient Boosting Machine for Credit Card Fraud Detection

  • Input: A credit card dataset with 300,000 instances from the UCI ML Repository.
  • Parameters: Number of iterations, loss function, weak learner, and sampling ratio are selected.
  • Optimization of LightGBM (LGBM): The number of estimators/boosted trees influences performance; varying numbers are evaluated to find optimal count. Max tree depth is limited to prevent overfitting, and tree leaves are moderated for complexity.
  • Evaluation: Model predicts if transactions are fraudulent using precision, recall, and accuracy metrics.

Long Short-Term Memory (LSTM)

Definition: A special type of artificial Recurrent Neural Network (RNN) architecture used to model deep learning time-series information.

Architecture: Consists of a memory cell updated by three gates: input, forget, and output. These gates regulate information flow into and out of the cell.

Network Structure: Features 5 input neurons (representing dataset features) and one output neuron for fraud status. A hidden layer with 15 neurons analyzes network structure.

Implementation Steps:
1
Data Preparation

Reshape dataset into three-dimensional tensor (samples, timesteps, features)

2
Define Parameters

Set learning parameters like memory size, learning rate, batch size, and epochs

3
Configure LSTM

Define LSTM cell and set tensor variables for weight and bias vectors

4
Dataset Split

Divide dataset into training, validation, and testing sets

5
Loss Function

Compute output using SoftMax activation and define cross-entropy loss function

6
Training & Prediction

Use Adam optimization to minimize loss, backpropagate, and predict test results

Autoencoder

Definition: A feed-forward multilayer neural network that reproduces input data on the output layer.

  • Oversampling: A technique to create specific class samples to balance an unbalanced dataset.
  • Denoised Autoencoder: A variation that learns to remove noise and reconstruct undistorted input.
  • Classification: A deep fully connected neural network model is used for final fraud classification.
  • Cross-Entropy Concepts: Used with SoftMax for multi-classification, providing better training performance by measuring information unpredictability.

Contrastive Learning

Definition: A self-supervised learning approach that learns representations by distinguishing between similar and dissimilar data points.

Mechanism: Maximizes similarity between positive pairs (augmented views of the same point) and minimizes similarity between negative pairs (different points).

Implementation: Preprocess data with augmentation to create pairs, define encoder architecture to map data to latent space, train using contrastive loss function (like InfoNCE), backpropagate the loss, update parameters with optimizer, and evaluate on downstream tasks.