Automated Risk Resource Allocation Tool
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Solution Overview
Problem
Manual review of risk and resource allocation requests across multiple systems and entities is complex, time-consuming, and prone to errors, especially when dealing with numerous inter-related systems and factors.
Innovation Solution
An automated risk relationship resource allocation tool that uses a back-end application server to retrieve electronic records from a resource allocation data store, determining initial durations based on entity, service provider, and third-party guidelines, and generating recommendations for extension requests, while facilitating communication through an interactive graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual review of resource allocation requests is performed, then understanding and reaction to requests can be facilitated, but the process becomes complicated, time-consuming, and error-prone when dealing with substantial numbers of inter-related systems and entities
Solution Approach 1:
The system enables automated self-service for resource allocation decisions by using machine learning models to automatically determine initial durations and generate extension recommendations based on entity expectations, service provider expectations, and third-party guidelines, eliminating the need for manual review of each request
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated computerized system that uses machine learning algorithms and data processing to analyze resource allocation requests, determine durations, and generate recommendations, substituting human manual operations with automated computational processes
2Measurement precision
If manual review of resource allocation requests is performed, then understanding of risks and allocations can be achieved, but accuracy and consistency are compromised due to the complexity and volume of information
Solution Approach 1:
The system segments the complex analysis task into distinct components: retrieving entity expectations, service provider expectations, and third-party guidelines as separate data elements, then processing each through the machine learning model to determine initial duration and generate recommendations, making the complex process manageable and consistent
Solution Approach 2:
The patent transforms the complex qualitative analysis of resource allocation requests into quantifiable parameters by using machine learning models to process multiple input factors (entity expectations, service provider expectations, guidelines) and output specific duration values and recommendations, enabling precise and consistent measurements
3Productivity
If automated determination of initial duration is performed based on multiple expectations and guidelines, then faster and more consistent results are provided, but the system complexity increases
Solution Approach 1:
The machine learning model serves multiple functions within a single system: it retrieves and processes entity expectations, service provider expectations, and third-party guidelines, determines initial durations, generates extension recommendations, and handles various resource allocation scenarios, consolidating multiple functions into one automated system
Data Source
AI summary
Some embodiments are associated with a system that provides an automated risk relationship resource allocation tool via a back-end application computer server of an enterprise. A resource allocation data store may contain electronic records representing requested resource allocations between the enterprise and a plurality of entities (collected from the entities and service providers). The server may then receive an indication of a selected requested resource allocation and retrieve, from the resource allocation data store, the electronic record associated with the selected requested resource allocation. The server may then automatically determine an initial duration for the selected requested resource allocation based on the shortest of: (i) an entity expected duration, (ii) a service provider expected duration, and (iii) a third-party guideline expected duration. Some embodiments may generate a final recommendation associated with an extension request for a requested resource allocation from an entity depending on whether service provider records are needed.