Autoencoder Anomaly Detection for Transaction Fraud
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Solution Overview
Problem
Current anomaly detection systems in transactional data face challenges due to noise and incomplete data, leading to inaccurate fraud detection in electronic payment transactions, particularly in distinguishing between legitimate and fraudulent reversion transactions.
Innovation Solution
The system employs an autoencoder-based neural network that preprocesses historical transactions to extract features, trains on legitimate transactions only, and uses reconstruction differences to detect anomalies, reducing computational costs and improving accuracy by distinguishing between legitimate and fraudulent transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional anomaly detection systems process noisy transactional data, then they attempt to detect fraudulent transactions, but they produce inaccurate detection results due to noise and incomplete data
Solution Approach 1:
The system performs preliminary action by pre-processing historical transaction data to extract meaningful features before training the autoencoder model. This includes cleaning noisy data, selecting relevant features, and creating a structured training dataset from unstructured transaction records, thereby improving detection accuracy before the actual anomaly detection process begins
Solution Approach 2:
The system extracts essential features from noisy transactional data by using an autoencoder neural network that learns to identify and extract meaningful patterns while filtering out noise. The autoencoder compresses the data into latent representations that capture the essence of legitimate transactions, enabling accurate anomaly detection despite the presence of noise and incomplete information in the original data
2Measurement precision
If the system trains on all historical transactions including fraudulent ones, then it processes more data, but it reduces detection accuracy by learning from fraudulent patterns
Solution Approach 1:
The system extracts only the legitimate transaction data from the historical dataset, separating it from fraudulent transactions. By taking out and using exclusively legitimate transactions for training, the autoencoder learns accurate patterns of normal behavior without being contaminated by fraudulent patterns, thereby improving detection accuracy
Solution Approach 2:
Instead of the conventional approach of training on both legitimate and fraudulent data to learn what fraud looks like, this system inverts the approach by training only on legitimate data and learning what normal transactions look like. Anomalies are then detected as deviations from this learned normal behavior, achieving better accuracy by excluding fraudulent patterns from the training process
3Measurement precision
If the system uses complex neural network models to improve detection accuracy, then detection precision improves, but computational costs increase
Solution Approach 1:
The system extracts the essential features of transaction data and processes only these extracted features through the neural network, rather than processing the complete high-dimensional transaction data. This feature extraction reduces the computational burden on the neural network while maintaining detection precision, as the model operates on condensed, meaningful representations of the data
Solution Approach 2:
The autoencoder creates a compressed copy (latent representation) of the input transaction data that captures the essential characteristics in a reduced dimensionality. This copying approach allows the neural network to work with smaller, more efficient data structures, reducing computational energy consumption while preserving the information needed for accurate anomaly detection
Data Source
AI summary
Systems and methods for anomaly detection includes accessing first data comprising a plurality of historical reversion transactions. A plurality of legitimate transactions are determined from the plurality of historical reversion transactions. An autoencoder is trained using the plurality of legitimate transactions to generate a trained autoencoder capable of measuring a given transaction for similarity to the plurality of legitimate transactions. A first reconstructed transaction is generated by the trained autoencoder using a first transaction. The first transaction is determined to be anomalous based on a reconstruction difference between the first transaction and the first reconstructed transaction.