Automated Resource Request Routing System
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Solution Overview
Problem
Current systems for evaluating resource requests in enterprises, such as insurance claims, are time-consuming and inefficient, particularly when determining potential third-party involvement, leading to delays in subrogation processes.
Innovation Solution
A resource request evaluation system utilizing a back-end application server that retrieves request characteristics, applies business rules, and employs a machine learning algorithm to dynamically route requests to either a request handler or a specialist, with the option for the parties involved to answer a series of questions, to efficiently determine the appropriate responder.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluation of resource requests is performed by claim handlers, then accuracy in determining third-party involvement can be maintained, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent introduces an automated evaluation system that acts as an intermediary between resource requests and human specialists. This system uses machine learning models and algorithms to pre-evaluate requests, filter candidates, and identify potential third-party involvement before human review, thereby maintaining accuracy while significantly improving processing speed and efficiency
Solution Approach 2:
The system performs preliminary evaluation of resource requests using automated algorithms and machine learning models before human specialists are involved. This pre-screening process identifies promising candidates for third-party involvement, allowing human experts to focus only on complex cases that require their judgment, thus resolving the contradiction between thorough evaluation and processing efficiency
2Productivity
If automated systems are used to evaluate resource requests, then processing speed increases, but the complexity of the system increases
Solution Approach 1:
The evaluation system is segmented into multiple independent modules: data collection module, machine learning model module, algorithmic evaluation module, and human specialist review module. Each module performs a specific function and can be independently developed, tested, and maintained, reducing overall system complexity while enabling high-speed automated processing
Solution Approach 2:
The patent creates a multi-functional evaluation platform that can handle various types of resource requests (insurance claims, subrogation cases, third-party involvement assessments) using a unified automated system. This universal system uses the same core infrastructure and machine learning framework across different evaluation scenarios, reducing complexity compared to having separate systems for each function
3Measurement precision
If all resource requests are assigned to specialists for thorough investigation, then accuracy improves, but resource allocation efficiency decreases
Solution Approach 1:
The system applies different levels of evaluation quality to different requests based on their characteristics. Routine requests with clear third-party involvement are processed through automated algorithms with high accuracy, while complex ambiguous cases are routed to human specialists. This localized quality approach ensures thorough investigation only where necessary, optimizing both accuracy and resource allocation efficiency
Data Source
AI summary
A resource request data store contains electronic records connected to risk relationships with an enterprise (each record may represent a resource request and include a resource request identifier and request characteristics). A back-end application computer server retrieves request characteristics associated with a resource request for a first party. Based on the request characteristics and business rules, the server determines if the resource request for the first party should be immediately assigned to a request handler. If the request is not to be immediately assigned, the server arranges for the first party to answer a dynamic series of questions. Based on answers provided by the first party and a set of investigation rules, automatically determine if the resource request for the first party should be assigned to the request handler or to a specialist to investigate whether another enterprise may be responsible for responding to the resource request, the system utilizes a machine learning algorithm to evaluate resource requests currently assigned to the request handler to determine if any of those resource requests should instead be routed to the specialist. The resource request is then routed to a request handler or the specialist.