Blockchain Collaborative Analytics for Fraud Detection
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Solution Overview
Problem
Current methods for detecting fraud in bank deposit operations are inadequate in terms of speed and adaptability, as they rely on outdated channels for sharing intelligence and do not effectively address the rapid and sophisticated nature of modern financial crimes, particularly in large financial institutions where fraudulent activities can spread quickly.
Innovation Solution
A blockchain-based system for collaborative analytics that allows financial institutions to store and analyze data analytics and derive metrics from these analyses, enabling the sharing of fraud detection insights without revealing proprietary models, thereby enhancing the speed and effectiveness of fraud detection across institutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If institutions share fraud analytics data through traditional channels, then collaboration and intelligence sharing improve, but the speed and adaptability of fraud detection deteriorate due to outdated communication methods
Solution Approach 1:
The patent replaces traditional mechanical communication channels (email, phone, formal reports) with a blockchain-based digital system. This substitution enables automated, real-time sharing of fraud analytics data among institutions, dramatically increasing the speed at which fraud patterns are detected and shared while maintaining data integrity through cryptographic verification.
Solution Approach 2:
The blockchain acts as a trusted intermediary between financial institutions, enabling them to share fraud analytics data without direct peer-to-peer communication. This intermediary layer provides a decentralized, tamper-proof platform that facilitates rapid information exchange while preserving institutional autonomy and data security.
2Reliability
If institutions share proprietary analytical models, then collaborative fraud detection improves, but loss of proprietary information and competitive advantage increases
Solution Approach 1:
The patent extracts only the necessary fraud pattern data and analytics results from proprietary models, leaving the core proprietary algorithms and methodologies intact within each institution. This extraction approach allows institutions to share actionable fraud intelligence through the blockchain while preserving their competitive analytical advantages.
Solution Approach 2:
The system segments fraud detection intelligence into shareable analytics results and non-shareable proprietary models. Institutions contribute segmented portions of their analytical capabilities (fraud patterns, anomaly detections) to the blockchain network while retaining ownership of their core analytical engines, enabling collaboration without full model exposure.
3Device complexity
If traditional fraud detection methods are used, then system simplicity is maintained, but adaptability to sophisticated and evolving fraud schemes deteriorates
Solution Approach 1:
The blockchain-based system provides a universal platform that can handle multiple types of fraud analytics data from different institutions and various fraud detection methodologies. This multi-functional platform adapts to sophisticated fraud schemes by incorporating diverse analytical approaches while maintaining a relatively simple underlying infrastructure.
Data Source
AI summary
An example operation may include one or more of a computer storing a first set of received data analytics in a blockchain, the first set associated with a subject matter. The operation further comprises the one or more computer storing a second set of received data analytics in the blockchain, the second set associated with the subject matter. The operation further comprises the one or more computer deriving and storing in the blockchain a first set of metrics based on analysis of the first set of analytics processed with the second set of analytics. The operation further comprises the one or more computer storing a third set of received data analytics in the blockchain, the third set associated with the subject matter, and deriving and storing a second set of metrics based on analysis of the first set of metrics processed with the third set of data analytics.