Unsupervised Financial Anomaly Detection via Geometric Data Mapping
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Solution Overview
Problem
Current financial auditing methods rely on supervised learning techniques that require historical data, labeled insights, and specific data structures, limiting their applicability and efficiency in detecting anomalies and trends across various accounting datasets.
Innovation Solution
An unsupervised analytical review method using information theory, data mining, and dimensionality reduction techniques to identify patterns and anomalies directly from accounting data, without prior knowledge or specific data formats, incorporating principles from probability theory, natural language processing, and linear algebra to characterize transactions in a geometric space and highlight material trends and anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised learning techniques are used for anomaly detection, then detection accuracy can be improved when historical labeled data is available, but the method becomes inapplicable when historical data is unavailable or data structures differ
Solution Approach 1:
The system performs self-service by automatically learning patterns and anomalies from the data itself without requiring external labeled training data. The unsupervised learning algorithms autonomously identify fraudulent transactions by detecting deviations from learned normal patterns, eliminating the need for manual data labeling and making the system adaptable to any dataset regardless of historical availability or structure
Solution Approach 2:
The patent implements universality by designing a detection system that can process various types of transaction data (financial, healthcare, insurance) with different structures and formats. The system uses generic unsupervised learning algorithms that adapt to any data type, making it universally applicable across multiple domains without requiring dataset-specific customization or labeled historical data
2Measurement precision
If labeled historical data is collected and stored for training, then supervised learning models can be trained, but this process becomes time-consuming and costly
Solution Approach 1:
The system eliminates the time-consuming data labeling process by using unsupervised learning that automatically discovers patterns and anomalies without requiring human-labeled training data. The algorithm self-learns from raw transaction data, identifying fraudulent patterns through statistical deviations and anomaly detection techniques, thereby removing the bottleneck of manual data preparation while maintaining high detection accuracy
3Reliability
If supervised learning methods are applied, then fraud detection can be performed using historical patterns, but the system cannot detect novel fraud types not present in training data
Solution Approach 1:
The patent implements dynamics by using unsupervised learning algorithms that continuously adapt to new fraud patterns as they emerge. Instead of relying on static trained models, the system dynamically learns new patterns from incoming data, allowing it to detect novel fraud types that have not been seen before while maintaining reliability through statistical anomaly detection that identifies deviations from normal behavior regardless of whether those patterns exist in historical data
Data Source
AI summary
Disclosed is a method generally applicable to any financial dataset for the purposes of: (1) determining the most important patterns in the given dataset, in order of importance; (2) determining any trends in those patterns; (3) determining relationships between patterns and trends; and (4) allowing quick visual identification of anomalies for closer audit investigation. These purposes generally fall within the scope of what in financial auditing is known as ‘analytical review’. The current method's advantages over existing methods are that is fully independent of the financial data subject to analysis, requires no background knowledge of the target business or industry, and is both scalable (to large datasets) and fully scale-invariant, requiring no a priori notion of financial materiality. These advantages mean, for example, that the same method can be by an external auditor for many different clients with virtually no client-specific customization, directing his attention to the areas where more detailed audit investigation may be required. Compared with existing methods, the current method is extremely flexible, and because it requires no a priori knowledge, saves significant time in understanding the fundamentals of a business.