Fraud Detection Using Discrete Stochastic Gradient Descent
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Solution Overview
Problem
Retailers face financial losses and operational inefficiencies due to fraudulent transactions, such as returns of items not purchased or returned with false receipts, which existing systems struggle to detect in real-time effectively.
Innovation Solution
A fraud detection system utilizing a computing device configured to apply a modified strategy based on a discrete stochastic gradient descent algorithm to identify fraudulent transactions, allowing for real-time determination and prevention of fraudulent activities during the return process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fraud detection methods are used, then system complexity is low, but fraud detection precision is insufficient
Solution Approach 1:
The patent introduces a discrete stochastic gradient descent algorithm as an intermediary computational mechanism that processes transaction data through multiple iterations. This algorithm acts as a mediator between raw transaction data and fraud detection decisions, enabling sophisticated pattern recognition without requiring complex hardware infrastructure. The algorithm iteratively adjusts parameters to optimize fraud detection accuracy while maintaining implementation feasibility.
Solution Approach 2:
The system performs preliminary actions by pre-processing transaction data and pre-calculating risk indicators before the actual fraud detection decision is made. The discrete stochastic gradient descent algorithm performs preliminary iterative optimization to establish baseline fraud probabilities, which then inform the final detection decision. This preliminary computational action enhances detection precision without increasing real-time processing complexity.
2Productivity
If real-time fraud detection is implemented, then productivity is improved, but loss of time for processing increases
Solution Approach 1:
The patent applies partial action by implementing fraud detection only for transactions that meet specific risk criteria rather than analyzing every transaction in full detail. The discrete stochastic gradient descent algorithm performs iterative processing on a subset of high-risk features and parameters, rather than exhaustively analyzing all transaction data. This selective approach maintains real-time detection capability while minimizing processing time overhead.
Solution Approach 2:
The system uses periodic action through iterative batches of the discrete stochastic gradient descent algorithm, processing transaction data in cycles rather than continuously. Each iteration processes a batch of transactions and updates fraud detection models periodically, enabling real-time detection while allowing computational resources to be efficiently managed in discrete time intervals rather than continuous processing.
3Reliability
If comprehensive fraud analysis is performed, then reliability of fraud detection is improved, but device complexity increases
Solution Approach 1:
The patent segments the fraud detection analysis into distinct modular components: data collection module, discrete stochastic gradient descent processing module, feature extraction module, and decision module. Each segment handles specific aspects of the analysis independently, allowing comprehensive fraud detection through multiple analytical layers while maintaining manageable system complexity through modular architecture. The segmentation enables parallel processing and independent optimization of each analytical component.
Data Source
AI summary
This application relates to apparatus and methods for identifying fraudulent transactions. A computing device receives return data identifying the return of at least one item. The computing device obtains modified strategy data identifying at least one rule of a modified strategy. The rule may be based on the application of at least one discrete stochastic gradient descent algorithm to an initial strategy. The computing device applies the modified strategy to the received return data identifying the return of the at least one item, and determines whether the return of the at least one item is fraudulent based on the application of the modified strategy. The computing device generates fraud data identifying whether the return of the at least one item is fraudulent based on the determination, and may transmit the fraud data to another computing device to indicate whether the return is fraudulent.