Auto-evolving Database Endorsement Policies for Fraud Detection
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Solution Overview
Problem
Centralized databases are prone to single points of failure, network dependency, and limited access, leading to vulnerabilities in data management and fraud detection, particularly in endorsing transactions.
Innovation Solution
A blockchain network with a shared ledger and smart contracts that compute historical patterns of fraudulent attempts, predict future fraud, and dynamically modify endorsement policies to enhance security and reliability without requiring full smart contract upgrades.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If centralized database is used for data storage and transaction management, then ease of management and control is improved, but reliability deteriorates due to single point of failure
Solution Approach 1:
The patent divides the centralized database into multiple distributed nodes across a blockchain network. Each node maintains a copy of the ledger, eliminating the single point of failure while preserving management capabilities through consensus protocols.
Solution Approach 2:
The patent introduces smart contracts as intermediary automated agreements that mediate transactions between parties. These self-executing contracts encode endorsement policies and fraud detection rules, providing reliable transaction management without centralized control.
2Device complexity
If static endorsement policies are used in smart contracts, then device complexity is reduced, but adaptability deteriorates due to inability to respond to fraudulent activities
Solution Approach 1:
The patent transforms static endorsement policies into dynamic ones that automatically adapt to changing conditions. The system monitors transaction patterns, detects fraud attempts, and modifies endorsement policies in real-time without requiring manual intervention or full smart contract upgrades.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors transaction data, analyzes fraud patterns, and uses this information to automatically adjust endorsement policies. This closed-loop system enables policies to evolve based on actual system behavior and emerging threats.
3Manufacturing precision
If manual operations are required for data retrieval from backup storage, then manufacturing precision is improved, but loss of time increases significantly
Solution Approach 1:
The patent creates multiple identical copies of the database across distributed nodes. Each node maintains a complete copy of the ledger, eliminating the need for backup storage and manual retrieval. Data can be instantly accessed from any node in the network.
Solution Approach 2:
The patent performs preliminary actions by pre-distributing data copies to multiple nodes before any potential data loss scenario occurs. This proactive replication ensures data is immediately available without requiring manual intervention during recovery scenarios.
Data Source
AI summary
An example operation may include one or more of computing historical patterns related to fraudulent attempts from a transaction log, predicting future fraud attempts from public data, correlating the historical patterns and the predicted future fraud attempts, modifying one or more endorsement policies based on the correlations, and adding the modified one or more endorsement policies to a smart contract.