Automated Plugin Generation for Predictive Model Deployment
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Solution Overview
Problem
Current fraud detection systems face challenges in porting predictive models from development environments to execution environments, requiring automated translation and prioritization of complex variables for real-time transaction analysis, while lacking natural language explanations for flagged transactions.
Innovation Solution
A method and system for automatically generating a plugin based on a predictive model, using a CSV format to define variables and predictive factors, which can be deployed on a target platform to provide real-time transaction analysis with natural language explanations for flagged transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predictive models are manually ported from development to execution environments, then model accuracy is preserved, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically generating the deployment configuration file and code snippets during the model development phase itself. This preliminary generation of deployment artifacts eliminates the need for manual porting operations later, thereby preserving model accuracy while significantly reducing the time required for model deployment from development to execution environments.
Solution Approach 2:
The system enables self-service by allowing the predictive model to automatically generate its own deployment configuration file and associated code snippets without requiring manual intervention. The model itself serves as the source for generating deployment artifacts, eliminating dependency on external manual porting processes and reducing both time consumption and operational complexity.
2Measurement precision
If complex predictive models with multiple variables are deployed, then detection accuracy improves, but system complexity and difficulty of operation increase
Solution Approach 1:
The system extracts the complex model configuration details from the predictive model and separates them into a structured deployment configuration file. This extraction process isolates the complexity into a manageable format that can be automatically processed, thereby maintaining high fraud detection accuracy through comprehensive variable analysis while significantly improving the ease of deployment operations.
Solution Approach 2:
The deployment configuration file serves as an intermediary between the complex predictive model and the execution environment. This intermediary structure translates complex model specifications into a standardized format that can be automatically processed by the system, thereby maintaining measurement precision while reducing operational complexity and improving ease of deployment.
3Reliability
If detailed predictive factors are analyzed for each transaction, then fraud detection quality improves, but processing speed and productivity decrease
Solution Approach 1:
The system performs preliminary analysis of predictive factors and pre-calculates risk scores during the model configuration phase. By preparing predictive factor weights and thresholds in advance, the system enables rapid real-time evaluation of transactions without sacrificing detection quality, thereby maintaining high fraud detection reliability while improving transaction processing speed and productivity.
Data Source
AI summary
Method and system for configuring a model and deploying it on a target. The method may include using a processing unit, automatically generating a plugin based on a statistical model, the plugin including variables and issue definitions extracted from the model; and deploying the plugin on a target platform.