Autoencoder-Decoder Platform for Anomaly Detection
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Solution Overview
Problem
Current technologies for detecting abnormal entities and activities in the merchant processing industry are inefficient, inaccurate, and costly, relying on manual analysis or rule-based systems that require constant updates and are prone to high false-positive rates and biases.
Innovation Solution
A machine-learning-based anomaly detection platform using a trained autoencoder-decoder, specifically a recurrent neural network, that processes feature values to identify reconstruction errors and provides actionable insights and recommendations for detected abnormalities, reducing false positives and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If rule-based systems are used to detect abnormal entities and activities, then detection can be automated, but the systems become cumbersome and overly complex as fraudulent activities evolve
Solution Approach 1:
The patent replaces the mechanical rule-based system with a machine learning model that automatically learns detection patterns from data. Instead of manually creating and maintaining complex rule-sets, the system uses algorithms to identify abnormal entities and activities, reducing operational complexity while maintaining automation.
Solution Approach 2:
The system transitions from fixed rule-based parameters to dynamic parameters learned from data. The machine learning model adapts its detection criteria based on patterns in the data, allowing the system to respond to evolving fraudulent activities without manual rule updates.
2Measurement precision
If manual analysis is used to detect abnormal entities and activities, then accuracy can be maintained through expert judgment, but the process becomes prohibitively expensive and time-consuming
Solution Approach 1:
The system enables self-service detection by automatically analyzing data and identifying abnormal entities without requiring manual expert intervention. The machine learning model performs detection independently, eliminating the need for expensive and time-consuming manual analysis while maintaining high accuracy.
Solution Approach 2:
The patent replaces manual expert analysis with an automated machine learning system. This substitution maintains detection accuracy by using learned patterns while eliminating the time loss and cost associated with human analysts reviewing each case.
3Ease of manufacture
If rule-based systems are used for detection, then implementation can be straightforward initially, but the systems require constant updates and maintenance as fraud evolves
Solution Approach 1:
The system implements dynamic adaptability through machine learning models that continuously learn from new data. Unlike static rule-based systems, the model automatically adapts to new fraud types by identifying patterns in the data, maintaining versatility without requiring manual updates.
Solution Approach 2:
The system performs preliminary learning during training phases, preparing the model to detect various fraud types before deployment. This preliminary action enables the system to be adaptable from the start and continue evolving without constant maintenance updates.
4Device complexity
If traditional detection methods are used, then existing infrastructure can be leveraged, but false-positive rates remain high due to biases in rule-sets
Solution Approach 1:
The patent replaces biased rule-based systems with machine learning models that learn objective patterns from data. This substitution reduces false-positive rates by eliminating human biases embedded in manual rule-sets while maintaining system simplicity through automated decision-making.
Solution Approach 2:
The system implements feedback mechanisms where detection results are continuously evaluated and used to improve the model. This feedback loop reduces false-positives by learning from errors and adjusting detection criteria, improving reliability while keeping the system straightforward to operate.
Data Source
AI summary
The present disclosure generally relates to providing accurate and real-time insights into abnormal entities and activities using machine learning algorithms. An exemplary computer-enabled method comprises receiving a set of input data, wherein the set of input data is associated with an entity; automatically obtaining, based on the received set of input data, a set of derived data, wherein the set of derived data is associated with the entity; obtaining, based on the set of derived data, a plurality of feature values corresponding to a plurality of features; providing the plurality of feature values to an autoencoder-decoder to obtain a plurality of feature-specific reconstruction errors; selecting, based on the plurality of feature-specific reconstruction errors, one or more features from the plurality of features; outputting the selected one or more features and one or more textual descriptions associated with the selected one or more features.