Fraud Risk Scoring With Cross-Platform Auditability
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Solution Overview
Problem
Current systems for tracking fraudulent activity lack connectivity across platforms, leaving entities vulnerable to fraud and failing to provide secure access to confidential information.
Innovation Solution
An apparatus and method for tracking fraudulent activity using a user database, processor, and memory to receive identification data, generate a fraud score, and initiate security parameters based on user profiles and local fraud risk factors, with alert generation and secure identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current systems are used for tracking fraudulent activity, then system simplicity is maintained, but connectivity across platforms is lacking and entities remain vulnerable to fraud
Solution Approach 1:
The patent combines multiple fraud detection systems and data sources into a unified platform that shares fraud indicators across entities. The system merges user profiles, fraud scores, and risk factors from multiple platforms into a centralized database, enabling connected fraud tracking while maintaining manageable complexity through standardized data structures and protocols.
Solution Approach 2:
The fraud tracking system is designed with universal applicability across multiple platforms and entities. The system uses model-agnostic machine learning approaches that can process various types of data from different sources, and the fraud indicators generated are applicable across diverse financial and e-commerce platforms, providing broad protection without requiring platform-specific implementations.
2Adaptability or versatility
If model-agnostic machine learning is implemented, then adaptability to different data sources is improved, but computational complexity increases
Solution Approach 1:
The system changes the parameters of fraud detection by using model-agnostic approaches that focus on input-output relationships rather than internal model structures. This allows the system to adapt to different machine learning models and data sources by adjusting data preprocessing and feature engineering parameters, while keeping the core detection logic relatively simple and computationally efficient.
Solution Approach 2:
The patent introduces intermediary components such as feature engineering layers and data normalization protocols that mediate between diverse data sources and the fraud detection algorithms. These intermediaries standardize data formats and extract universal features, enabling the system to handle multiple data types without requiring complex model-specific adaptations, thus reducing overall computational complexity.
Data Source
AI summary
An apparatus and method for fraud-risk assessment. Event data including identification, transactional, geospatial, biometric, and behavioral signals, are received and processed by a scoring module employing one or more analytical models to generate a score. Based at least in part on the score, a security action is selected and a risk-tiered alert is generated. An audit record including the score, rationale, action, timestamps, and associated data characteristics is stored in one or more datastores configured for secure, verifiable, or immutable recordkeeping. The operations may be performed in any order or in parallel. Embodiments include velocity controls for real-time flows and post-event workflows to escalate to external recipients, create or update an investigation case, or supply features for model retraining, while preserving auditability.