Distributed Risk Identification Architecture for Financial Service Platforms
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Solution Overview
Problem
Existing risk control systems in Internet financial service platforms face inefficiencies due to high computational and network resource demands when processing large amounts of data, leading to prolonged processing times and reduced risk control efficiency.
Innovation Solution
A distributed risk identification architecture where end-user devices perform initial risk identification using stored rules or models, and when uncertain, trigger cloud risk identification devices to process service data, thereby offloading computational burdens and reducing system overheads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the risk control server processes all collected data centrally, then risk identification accuracy is maintained, but processing time increases and risk control efficiency decreases
Solution Approach 1:
The patent divides the centralized risk control system into distributed risk identification modules deployed across multiple edge devices (servers, cloud platforms, terminal devices). Each module independently performs risk identification on local data, segmenting the processing workload and reducing centralized bottlenecks while maintaining comprehensive risk detection capabilities.
Solution Approach 2:
The patent introduces a multi-dimensional risk identification architecture that operates at different levels (edge device level, server level, cloud level) simultaneously. This dimensional expansion allows parallel processing of risk data across multiple nodes, reducing overall processing time while maintaining identification accuracy through collaborative verification.
2Reliability
If the risk control server processes all collected data centrally, then comprehensive risk analysis is achieved, but system resource overhead increases
Solution Approach 1:
The patent segments the data processing workload across multiple distributed devices, with each device processing only its local data subset. This segmentation reduces the computational burden on any single server, lowering overall system resource overhead while maintaining comprehensive risk coverage through distributed analysis.
Solution Approach 2:
Edge devices perform self-service risk identification by executing local risk identification models on their collected data. This self-service capability reduces the need for centralized processing resources, as each device independently handles its own risk analysis while contributing results to the overall risk control system.
3Measurement precision
If all data is transmitted to the risk control server, then complete risk assessment is possible, but network transmission requirements become extremely high
Solution Approach 1:
The patent extracts and processes critical risk-related features locally at edge devices before transmission. Only essential risk indicators and processed results are transmitted to the server, rather than raw complete datasets. This extraction approach maintains risk assessment completeness while dramatically reducing network transmission volume.
Solution Approach 2:
Risk identification processing is performed preliminarily at edge devices before data reaches the central server. This preliminary action pre-processes data locally, extracting key risk information in advance, which reduces the amount of data that needs subsequent transmission and centralized processing while maintaining comprehensive assessment capability.
Data Source
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AI summary
The present application discloses a risk identification method, a risk identification apparatus, and a cloud risk identification apparatus and system. The method includes the following: after collecting service data, performing, by an end-user device, risk identification on a service processing request that generates the service data based on a stored risk identification rule, and when a risk identification result cannot be determined, triggering a cloud risk identification device to perform risk identification on the service processing request that generates the service data. A distributed risk identification architecture is provided in the implementations of the present application. This effectively alleviates an existing technology's problem that a risk control server takes a relatively long time to process received data, which causes relatively low risk control efficiency; reduces the operation burden of the risk control server through this type of multi-layered risk identification; reduces overheads of system resources; and also completes risk identification on the end-user device, to shorten the time of risk identification, and improve user experience.