Signal Processing for ML Fraud Detection
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Solution Overview
Problem
Current methods for detecting anomalies in documents, such as those used in digital identity verification, face challenges in rapidly adapting to new fraud mechanisms with limited data and requiring extensive resources, leading to inefficiencies in delivering scalable and customized solutions.
Innovation Solution
A method involving the selection of signal processing algorithms based on discriminative power to isolate relevant signals for training machine learning models, allowing for efficient anomaly detection with minimal data, using techniques like spatial, temporal, or spatio-temporal signal processing to generate input data for machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are trained with vast amounts of data and customized human expert driven data augmentation to achieve high performance in anomaly detection, then detection accuracy is improved, but data requirements and time consumption increase significantly
Solution Approach 1:
The patent extracts and isolates specific signal processing algorithms that are most effective for detecting anomalies in document images. By selecting and extracting only the most discriminative algorithms from a larger set, the system achieves high detection accuracy without requiring vast amounts of training data, thus resolving the contradiction between detection precision and data quantity requirements
Solution Approach 2:
The patent introduces signal processing algorithms as intermediary components between the raw document images and the machine learning model. These intermediary algorithms pre-process the images to enhance anomaly signals, allowing the machine learning model to achieve high accuracy with fewer training samples by receiving pre-enhanced input rather than raw data
2Reliability
If customized solutions are developed for each fraud scheme using skilled scientists and extensive resources, then detection reliability is improved, but device complexity and resource requirements increase
Solution Approach 1:
The patent creates a universal anomaly detection system that can handle multiple types of fraud schemes and document types through a single automated framework. The system uses a standardized process of selecting signal processing algorithms based on discriminative power, eliminating the need for separate customized solutions for each fraud type while maintaining high detection reliability across diverse scenarios
Solution Approach 2:
The system automatically selects the most appropriate signal processing algorithms based on their discriminative power for detecting anomalies in specific document types, without requiring manual configuration by skilled scientists. This self-service capability reduces system complexity and resource requirements while maintaining reliable detection across different fraud schemes
3Measurement precision
If extensive data augmentation and customization are performed for each document type and fraud mechanism, then detection accuracy is improved, but productivity and speed of delivering solutions decrease
Solution Approach 1:
The patent performs preliminary selection of signal processing algorithms based on their discriminative power before training the machine learning model. By pre-selecting the most effective algorithms for each document type and fraud mechanism, the system reduces the time and resources needed for data augmentation and model training, thereby increasing productivity while maintaining detection accuracy
Data Source
AI summary
Described are methods and systems for training a machine learning (ML) model to detect anomalies in images of documents. A first image of a first set of images of documents is obtained. Each first image relates to a region of the document and the first set of images comprises an image of a document containing an anomaly and an image of a document not containing an anomaly. Signal processing algorithms are applied to the first images to generate a signal for each first image and each algorithm, and a discriminative power of each algorithm is evaluated. Based on the discriminative power, a signal processing algorithm is selected and ML model input data is generated using signals generated by applying the algorithm to second digital images. The ML model is trained using the input data to produce output indicating whether an image of a document contains an anomaly.