Modular Chainable Algorithms for Risk Detection
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Solution Overview
Problem
Existing computer-based data analytics tools for risk detection and visualization lack algorithmic flexibility and the ability to chain multiple algorithms together modularly, limiting their effectiveness in sophisticated modeling and analytics tasks.
Innovation Solution
The system employs modular chainable algorithms that include executable program code for data modeling, visualization code for output rendering, and workflow code for automated actions, allowing for advanced data analysis and visualization while improving processing speed and reducing computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional data analytics tools are used for risk detection, then basic detection capability is provided, but algorithmic flexibility and the ability to chain multiple algorithms are limited
Solution Approach 1:
The system segments the data analytics functionality into independent, modular algorithms that can be individually selected and chained together. Each algorithm is a self-contained unit that performs a specific risk detection function, allowing users to compose complex analytics pipelines from simple building blocks without increasing overall system complexity
Solution Approach 2:
The platform provides a universal algorithm execution environment that can run multiple different types of algorithms through a common interface. The system is designed to execute diverse analytics algorithms (fraud detection, anomaly detection, pattern recognition, etc.) using the same infrastructure, making it adaptable to various risk detection needs without requiring separate specialized systems
2Productivity
If sophisticated modeling and analytics tasks are performed, then analysis capability is improved, but processing speed decreases and computational resources increase
Solution Approach 1:
The system dynamically allocates computational resources based on the specific algorithm being executed and the characteristics of the input data. The platform can adjust processing parameters, parallelization levels, and resource allocation in real-time to optimize the balance between analysis depth and processing speed for each analytics task
Solution Approach 2:
The system allows dynamic adjustment of algorithmic parameters and processing configurations to optimize performance. Users can modify parameters such as sampling rates, threshold values, and computation intensity to achieve the desired balance between sophisticated analysis capability and processing speed for different risk detection scenarios
Data Source
AI summary
Computer-based systems and methods for risk/reward detection, visualization, and resolution using modular chainable algorithms are provided. The system allows for computer-based modeling of large data sets with improving processing speed and utilizing fewer computational resources. The modular chainable algorithms included embedded program code executable by a processor for performing a data modeling or analytic function on source data, visualization code for visualizing output of the program code, and workflow code for automatically performing one or more actions relating to the data modeling or analytic function.