Cross-Border Trade Risk Scoring via IoT and Blockchain Integration
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Solution Overview
Problem
Current risk assessment systems for cross-border trade, particularly in the food and agriproducts sector, inadequately integrate real-time and non-financial data, lack sufficient security measures, and fail to adapt dynamically to changing market conditions, leading to ineffective risk scoring and decision-making.
Innovation Solution
A risk assessment system that incorporates IoT data, user-uploaded data, third-party data, and platform data, secured by a blockchain framework, using AI and machine learning models to generate real-time risk scores, and updates scores dynamically, ensuring data integrity and user accessibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional risk assessment methods are used with limited financial metrics and historical data, then the system is simple to operate, but the measurement precision of risk assessment is insufficient
Solution Approach 1:
The patent combines multiple data sources including financial data, non-financial data, IoT sensor data, and platform transaction data into a unified risk assessment system. This integration of diverse data types enables comprehensive risk evaluation while maintaining system coherence through a centralized platform architecture.
Solution Approach 2:
The risk assessment system is designed to handle multiple types of data inputs and perform various assessment functions simultaneously. The platform can evaluate both buyer risk and seller performance using the same infrastructure, making the system versatile and multi-functional for different trade participants and scenarios.
2Measurement precision
If real-time data from multiple sources is integrated, then the risk assessment becomes more accurate, but the data security risks increase
Solution Approach 1:
The patent introduces a blockchain framework as an intermediary layer that secures data transmission and storage. The blockchain technology provides cryptographic protection and decentralized verification, acting as a mediator that enables real-time data integration while maintaining security through distributed ledger technology and smart contracts.
3Adaptability or versatility
If traditional risk assessment models are used, then the system is easier to implement, but the adaptability to changing market conditions is insufficient
Solution Approach 1:
The risk assessment system employs dynamic scoring models that continuously update risk scores based on new incoming data from multiple sources. The system adapts to changing market conditions by incorporating real-time IoT data, transaction data, and external factors, allowing risk assessments to evolve dynamically rather than relying on static historical models.
Solution Approach 2:
The patent implements feedback mechanisms where risk assessment results are continuously refined based on new data inputs and outcome verification. The system learns from actual transaction outcomes and uses this feedback to improve future risk predictions, creating a self-improving adaptive system that responds to market changes.
4Reliability
If comprehensive data collection from multiple sources is performed, then the risk assessment becomes more reliable, but the processing time increases
Solution Approach 1:
The system performs preliminary data validation, cleaning, and structuring as data enters the platform, preparing it for analysis in advance. By pre-processing data from multiple sources before the actual risk assessment calculation, the system reduces the time required for comprehensive analysis while maintaining data quality and reliability.
Solution Approach 2:
The risk assessment system operates continuously, with data collection, processing, and scoring occurring in an ongoing stream rather than batch processing. This continuous operation allows the system to maintain up-to-date risk assessments without significant delays, as the processing pipeline remains actively engaged with incoming data flows.
Data Source
AI summary
The risk assessment system for evaluating and scoring small businesses engaged in cross-border trade, includes a computer system connected to one or more buyer devices, one or more seller devices, an Internet of Things (IoT) module and a blockchain framework. The computer system functions to receive various data inputs, including user-uploaded, third-party, and platform data and employs an optical character recognition (OCR) data extractor to process these inputs and utilizes artificial intelligence (AI) models to generate processed data. The computer system further calculates a transaction history score by analyzing payment discrepancies and integrates IoT data for comprehensive risk assessment. Utilizing a proprietary risk scoring model, the computer system generates a buyer risk score or a seller performance score, ranging from 1 to 1000, providing a dynamic and data-driven solution for risk evaluation in international trade. The respective scores may then be displayed on the buyer and seller devices.