Customized FNOL Interface for Early Fraud Detection

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Solution Overview

Problem

Catastrophic event preparedness is often inadequate, leading to high damage costs and loss of life, while the insurance industry is reactive and prone to fraud, with inefficient manual investigative processes.

Innovation Solution

A computing system leveraging machine learning and AI to provide individualized loss prevention and mitigation services, integrating with various data sources to predict event impacts, automate FNOL information gathering, and detect fraud.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual investigative processes are used after claim events, then investigators can identify fraudulent behavior, but the process is inefficient and allows perpetrators to cover their tracks

Engineering Contradiction:

Improvefraud detection accuracyVSAvoidinvestigation time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by proactively monitoring and detecting potential claim events before formal claims are filed. investigators are alerted in advance, allowing them to initiate investigations before perpetrators can cover their tracks, thus resolving the time loss issue while maintaining fraud detection accuracy

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces manual mechanical investigative processes with automated electronic monitoring systems, data analytics, and digital forensics tools. This substitution enables real-time or near-real-time detection and investigation, dramatically reducing the time loss while maintaining or improving fraud detection reliability

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If the insurance industry waits for claim events and filings before investigating, then claim processing can begin, but fraud prevention is ineffective and premium costs increase

Engineering Contradiction:

Improveclaim processing efficiencyVSAvoidfraudulent claims

Core Design Contradiction:

ProductivityVSObject-generated harmful factors

Solution Approach 1:

The system implements preliminary action by proactively detecting potential claim events and alerting investigators before formal claims are filed. This allows the industry to address fraudulent behavior before it results in paid claims, reducing the harmful effects of fraud while maintaining efficient claim processing for legitimate cases

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent establishes a feedback loop where data from multiple sources (telematics, weather, news, social media) continuously monitors for potential claims. This feedback mechanism enables real-time detection and response to fraudulent activities, allowing the system to distinguish between legitimate and fraudulent claims efficiently, thereby reducing fraud-related costs while maintaining processing productivity

Inventive Principle:

Principle #23Feedback

3Ease of operation

If general preparedness guidance is provided to individuals, then people can take some preventive actions, but damage costs remain high due to lack of individualized guidance

Engineering Contradiction:

Improvepreparedness guidance accessibilityVSAvoiddamage costs

Core Design Contradiction:

Ease of operationVSLoss of energy

Solution Approach 1:

The system applies local quality by providing customized preparedness guidance tailored to each user's specific location, property characteristics, and risk profile. Instead of generic advice, users receive localized recommendations based on their specific vulnerabilities and threat assessments, making the guidance more effective at reducing damage costs while remaining easy to access

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The patent enables preliminary action by providing users with proactive preparedness recommendations before catastrophic events occur. The system analyzes user data, assesses risks, and delivers personalized action items that users can complete in advance, thereby reducing potential damage costs while maintaining ease of operation through automated, targeted guidance

Inventive Principle:

Principle #10Preliminary action

4Adaptability or versatility

If predictive models provide localized severity predictions, then individualized preparedness guidance can be offered, but prediction precision must be improved for certain catastrophic events

Engineering Contradiction:

Improveindividualized preparedness capabilityVSAvoidlocalized severity prediction accuracy

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The system merges multiple data sources including telematics, weather data, news feeds, social media, and historical claim data to create a comprehensive predictive model. This combination of diverse data streams enhances measurement precision for localized severity predictions while maintaining adaptability to provide individualized preparedness guidance for different user scenarios and catastrophic event types

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS12530725B2Customized user interface experience for first notice of loss

Publication Date: 2026.01.20 ASSURED INSURANCE TECH INC

AI summary

A computing system can determine a subset of users that have been affected by an event occurring in a given area. Subsequent to the event, and for each respective user of the subset of users, the system generates interactive follow-up content specifically tailored for the respective user. The system can transmit updated content data to the computing device of the respective user, causing the computing device of the respective user to present the interactive follow-up content. Based on user interaction by the respective user with the interactive follow-up content, the system can receive contextual information provided by the respective user, the contextual information corresponding to damage or loss resulting from the event as indicated by the respective user.