Insurance Claim Vulnerability Detection for Leakage Prevention

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Solution Overview

Problem

Insurance providers face inefficiencies in claim handling due to claim leakage caused by failures to follow policies and procedures, with manual reviews being inadequate and time-consuming, leading to significant losses and delays in identifying root causes.

Innovation Solution

A computing system that uses machine learning to analyze both structured and unstructured data from insurance claims, identifying vulnerabilities through a customized data model, providing real-time monitoring and feedback to supervisors to prevent claim leakage.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual reviews are used to detect claim handling deficiencies, then claim leakage can be identified, but the process is time-consuming and inadequate

Engineering Contradiction:

Improveclaim leakage detection accuracyVSAvoidreview time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical review processes with an automated computer-based system that uses machine learning models and natural language processing to analyze claim documentation, extract vulnerability features, and detect claim handling deficiencies automatically, thereby eliminating time-consuming manual reviews while maintaining or improving detection accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces an intermediary automated analysis system that acts as a bridge between raw claim data and final vulnerability detection, using trained machine learning models to process and interpret claim documentation, thereby speeding up the detection process while preserving reliable identification of claim leakage risks

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If automated vulnerability detection is implemented, then claim handling efficiency is improved, but system complexity increases

Engineering Contradiction:

Improveclaim handling efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent segments the complex vulnerability detection task into distinct modular components: documentation extraction modules, feature extraction modules, machine learning model evaluation modules, and vulnerability scoring modules. Each module performs a specific function and can be independently trained and maintained, reducing overall system complexity while improving claim handling efficiency

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent creates a universal automated vulnerability detection system that can handle multiple types of claim documentation, various vulnerability types, and different claim scenarios through a single multi-functional platform using trained machine learning models, thereby improving efficiency without proportionally increasing complexity

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Reliability

If frequent vulnerability detection processing is performed on high-risk claims, then claim leakage prevention is improved, but processing resources are consumed

Engineering Contradiction:

Improveclaim leakage preventionVSAvoidprocessing resources

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The patent applies local quality by concentrating processing resources on high-risk claims that require frequent vulnerability detection, while using less intensive processing for lower-risk claims. The system dynamically adjusts processing frequency and depth based on individual claim risk profiles, thereby improving claim leakage prevention for critical cases while optimizing overall resource consumption

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS20260004358A1Systems, methods, and platforms for automated quality management and identification of errors, omissions and/or deviations in coordinating services and/or payments responsive to requests for coverage under a policy

Publication Date: 2026.01.01 FUTURITY GROUP INC

AI summary

In an illustrative embodiment, systems and methods for monitoring insurance claims include identifying, based on predetermined monitoring frequency, insurance claims identified for vulnerability detection processing. Vulnerability detection features may be extracted from data files of the claims, which provide an indication of claim handling deficiencies that can cause claim leakage. A trained vulnerability detection data model can be used to detect claim handling vulnerabilities within the extracted vulnerability detection features where each of the vulnerabilities may include a likelihood of the vulnerability resulting in claim leakage. The vulnerability detection data model may be trained with a data set customized to a respective insurance provider for each claim. Vulnerability scores indicating an overall likelihood of claim leakage can be calculated for the claims based on the detected claim handling vulnerabilities. Vulnerability scores for claims assigned to a user may be presented within a user interface screen at a remote computing device.