Store Risk Evaluation Using Spatial and Temporal Fraud Models
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Solution Overview
Problem
Existing methods for calculating store risk ratings rely heavily on historical fraud loss rates and require domain expertise, failing to account for spatial and temporal variations in fraud patterns, especially when fraudsters target multiple locations.
Innovation Solution
Implementing a spatial model that incorporates neighboring store fraud loss data using geospatial techniques and a temporal model leveraging deep learning sequence models to enhance store risk ratings, combining attention mechanisms to identify pertinent historical information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods focus on single store historical fraud loss rate, then calculation simplicity is maintained, but measurement precision of store risk rating deteriorates
Solution Approach 1:
The evaluation model is segmented into two distinct components: a spatial model that processes geographic proximity information between stores, and a temporal model that processes historical fraud loss sequences. This segmentation allows each model to specialize in specific aspects of risk evaluation, improving overall measurement precision while maintaining manageable complexity through modular design
Solution Approach 2:
The patent introduces a spatial dimension by incorporating geographic coordinates and distance calculations between stores, transforming the traditional single-dimension historical rate evaluation into a multi-dimensional assessment that includes spatial relationships. This dimensional expansion significantly improves risk rating accuracy by capturing geographic fraud patterns
2Reliability
If domain expertise and human investigators are required, then evaluation reliability is improved, but productivity deteriorates
Solution Approach 1:
The system implements self-service evaluation through automated spatial and temporal models that independently process chargeback data, calculate risk scores, and generate evaluations without human intervention. The models automatically incorporate domain knowledge through their design, enabling the system to serve itself and eliminate dependency on human experts for routine evaluations
Solution Approach 2:
The patent replaces the mechanical system of human investigator analysis with automated computational models. The spatial model uses geometric calculations and the temporal model uses sequence processing to substitute human cognitive processes, maintaining reliability through algorithmic consistency while dramatically improving productivity through automation
3Measurement precision
If spatial and temporal models are implemented, then measurement precision of fraud pattern detection is improved, but device complexity worsens
Solution Approach 1:
The complex evaluation system is segmented into two independent models: a spatial model handling geographic analysis and a temporal model handling historical sequences. This segmentation reduces implementation complexity by allowing each model to be developed, tested, and maintained separately while achieving high detection precision through their combined insights
Solution Approach 2:
The patent merges the outputs of the spatial model and temporal model to produce a comprehensive risk evaluation. By combining these two specialized models, the system achieves superior fraud pattern detection accuracy that neither model could achieve alone, while the modular merging approach keeps implementation complexity manageable
Data Source
AI summary
Systems and methods for evaluating risks for physical locations are disclosed. A request for risk evaluation of transactions associated with a physical location located within a predetermined region including a plurality of neighboring locations is received. A spatial model is implemented to generate a first risk score based on chargeback data associated with the physical location and chargeback data of the plurality of neighboring locations. A temporal model is implemented to generate a second risk score for the physical location. A final risk score for the physical location is generated based on the first risk score and the second risk score. The final risk score is transmitted to a computing device for detecting fraudulent transactions associated with the physical location in response to the request.