Multi-Task Fraud Detection Models for Imbalanced Transaction Data
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Solution Overview
Problem
Existing fraud detection systems in finance are vulnerable to fraudulent actors who manipulate rules-based detection methods and require extensive feature engineering, leading to inefficiencies and inaccuracies in identifying fraudulent transactions.
Innovation Solution
Implementing a multi-task learning algorithm that uses deep learning techniques to detect fraudulent transactions by exploiting commonalities and differences across tasks, with adjustments for class imbalances and employing an ensemble model to improve learning efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If rule-based fraud detection approaches are used, then the system is easy to implement and interpret, but the system becomes vulnerable to fraudulent actors who can manipulate the rules and requires extensive feature engineering
Solution Approach 1:
The patent replaces rule-based mechanical detection systems with neural network-based learning systems. The neural networks automatically learn fraud patterns from data without requiring manual rule engineering, thereby improving detection accuracy while maintaining ease of implementation through automated training processes.
Solution Approach 2:
The neural network system performs self-learning and self-adjustment by automatically training on transaction data and improving its detection capabilities over time without requiring continuous manual rule updates. The system serves itself by automatically adapting to new fraud patterns.
2Reliability
If traditional machine learning approaches with extensive feature engineering are used, then the system can achieve reasonable detection accuracy, but the system requires significant manual effort and time for feature engineering
Solution Approach 1:
The patent replaces manual feature engineering processes with automated neural network feature learning. The deep learning models automatically extract relevant features from raw transaction data, eliminating the time-consuming manual feature engineering process while maintaining or improving detection accuracy.
Solution Approach 2:
The neural networks perform preliminary automatic feature extraction and learning during the training phase, preparing the system in advance to handle fraud detection without requiring manual feature engineering for each new fraud scenario. This preliminary learning action saves significant time in deployment.
3Reliability
If deep learning models with multiple layers are used, then the system can automatically learn features and improve accuracy, but the system complexity increases
Solution Approach 1:
The patent employs an ensemble of multiple neural networks with different architectures (e.g., CNNs, RNNs, transformers) rather than a single complex model. Each network segment handles specific aspects of fraud detection, and their predictions are combined. This segmentation manages complexity while maintaining high accuracy through diverse specialized models.
Data Source
AI summary
Application of multi-task learning technique(s) to machine logic (for example, software) used to detect financial transactions that are fraudulent or at least considered likely to be fraudulent. Some embodiments include adjustments and/or additions to conventional multi-task learning techniques in order to make the multi-task learning techniques more suitable for use in fraud detection software. One example of this is compensation for class imbalances that are to be expected as between the likely-fraud and not-likely-fraud classes of data sets (for example, training data sets, runtime data sets).