Deep Learning Cash Structuring Detection with Ensemble Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current financial crimes compliance detection programs fail to effectively and efficiently detect potentially suspicious cash structuring activity related to money laundering and other financial crimes, leading to high rates of false positives and missed alerts.
Innovation Solution
A system utilizing a deep learning model with convolutional neural networks and ensemble teacher models to classify cash structuring activity, distinguishing between global and daily inputs, and generating scores for accounts that fall outside predetermined thresholds, thereby alerting users to potentially suspicious activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional financial crimes compliance detection programs are used, then the system is simple to operate, but the detection accuracy is low and false positives are high
Solution Approach 1:
The patent replaces traditional rule-based mechanical detection systems with deep learning neural networks that automatically learn patterns from data. The convolutional neural network and ensemble teacher model substitute for manual compliance rules, enabling the system to detect cash structuring through learned behavioral patterns rather than predetermined thresholds, thereby improving detection accuracy while managing complexity through automated feature extraction.
Solution Approach 2:
The patent transforms the detection approach by changing from fixed threshold parameters to dynamic learned parameters. The system uses daily inputs and global inputs with varying weights determined by the neural network, allowing detection thresholds to adapt based on learned patterns rather than static compliance rules, improving precision while the model handles parameter complexity internally.
2Measurement precision
If deep learning models with multiple layers are used, then detection accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The patent performs preliminary data processing by organizing transaction data into daily inputs and global inputs before feeding them to the deep learning model. The convolutional neural network pre-processes this structured data to extract relevant features, reducing the computational burden during final detection and enabling faster processing of complex multi-layer models without sacrificing detection accuracy.
Solution Approach 2:
The patent segments the detection process into distinct components: daily input processing, global input processing, and pattern recognition. The convolutional neural network processes daily transactions separately from global account information, allowing parallel computation and reducing overall processing time while maintaining high detection accuracy through specialized processing for each data type.
3Reliability
If more sophisticated detection algorithms are implemented, then false positives are reduced, but the difficulty of detecting and measuring suspicious activity changes
Solution Approach 1:
The patent implements feedback mechanisms where the ensemble teacher model and convolutional neural network continuously learn from detection outcomes. The system uses aggregated deep learning model outputs to refine its detection patterns, reducing false positives through iterative improvement while the automated feedback loop manages the complexity of sophisticated algorithms by self-adjusting detection parameters based on performance.
Solution Approach 2:
The deep learning model performs self-service by automatically learning detection patterns from data without requiring manual rule configuration. The convolutional neural network self-adjusts its weights and thresholds based on training data, reducing false positives through learned patterns while the system handles its own complexity management through automated feature extraction and pattern recognition.
Data Source
AI summary
A system includes receiving data associated with an account, the data having a plurality of members; generating based on an ensemble teacher model, a deep learning model having a number of layers; inputting a plurality of members determined to be daily inputs into the deep learning model; extracting a daily pattern from the daily inputs and aggregating a deep learning model output; inputting the global inputs and an aggregated deep learning model output into a classifier; outputting from the classifier, a number of scores combined into a single score for the account. Further, the device may include alerting a user if the single score falls outside of a predetermined threshold.