Dynamic Interview Script for Fraudulent Claims
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Solution Overview
Problem
Existing claims submission systems are vulnerable to disingenuous reporting and human error, as they often rely on static scripts and poorly designed user interfaces, making it easy for criminals to exploit the system and for users to enter incorrect information.
Innovation Solution
A method using an artificial intelligence model to dynamically generate an interview script based on features of a submitted claim, including user data and text input, to assess the risk of fraudulent claims and correct inaccurate information, continuously updating the script to adapt to new patterns and prevent system abuse.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a static predetermined script is used for claims submission, then the system is easier to operate and implement, but it becomes vulnerable to disingenuous reporting and easy to game
Solution Approach 1:
The patent applies dynamics by transitioning from a static predetermined script to a dynamic interview script that adapts in real-time based on user responses. The system continuously updates the interview script by selecting and presenting different questions from a pool based on the user's answers, making the interaction adaptive rather than fixed. This resolves the contradiction by maintaining ease of operation through automation while improving reliability through dynamic adaptation that prevents gaming.
Solution Approach 2:
The patent implements feedback by using the user's responses to continuously update and modify the interview script. The system analyzes user answers and dynamically selects subsequent questions based on this feedback, creating a closed-loop system that adapts to the user's behavior. This feedback mechanism prevents disingenuous reporting by adjusting the questioning strategy in real-time based on detected patterns, thereby improving reliability while maintaining ease of operation.
2Device complexity
If a static predetermined script is used for claims submission, then the system implementation is simpler, but it allows criminals to learn and exploit specific protocols
Solution Approach 1:
The patent applies dynamics by implementing an interview script that dynamically adapts its questioning strategy based on user responses rather than following a fixed predetermined path. The system maintains a pool of questions and selectively presents them based on real-time analysis of user answers, making the protocol unpredictable to criminals while remaining implementable through automated systems. This resolves the contradiction between implementation simplicity and vulnerability to exploitation.
Solution Approach 2:
The patent changes parameters by dynamically adjusting which questions are presented, the order of questioning, and the depth of inquiry based on user responses and risk assessment. Rather than maintaining a fixed script, the system modifies its questioning parameters in real-time, making it difficult for criminals to learn and exploit specific protocols while keeping the system implementable through automated decision-making rules.
3Object-affected harmful factors
If a dynamic interview script is implemented using AI model, then the system can adapt to prevent fraud and reduce harmful factors, but the device complexity increases
Solution Approach 1:
The patent introduces an intermediary component - the interview script generation system that acts as a mediator between the user and the claims processing system. This intermediary dynamically generates appropriate questions based on user responses and risk assessment, reducing harmful factors through adaptive questioning while managing complexity by encapsulating the AI logic in a dedicated module rather than distributing it throughout the entire system.
Solution Approach 2:
The patent applies self-service by enabling the interview script system to automatically generate and update its own questioning strategy without requiring manual intervention. The system uses AI models to autonomously select and present appropriate questions based on user responses, reducing harmful factors through adaptive fraud prevention while managing complexity through automation rather than human oversight.
4Measurement precision
If dynamic questioning is used to assess fraud risk, then measurement precision of claim accuracy improves, but the time required for claims processing increases
Solution Approach 1:
The patent applies partial action by implementing dynamic questioning that adapts to each user's risk profile, applying more intensive questioning only when necessary rather than to all users uniformly. The system assesses fraud risk and adjusts the depth and breadth of questioning accordingly, improving measurement precision for high-risk cases while minimizing time loss for low-risk cases through selective application of dynamic questioning.
Solution Approach 2:
The patent changes parameters by dynamically adjusting the number and type of questions presented based on real-time risk assessment. The system modifies questioning parameters such as question depth, breadth, and intensity according to the user's responses and detected risk factors, improving measurement precision when needed while reducing time loss by avoiding excessive questioning for low-risk claims.
Data Source
AI summary
Embodiments are directed to a method for determining an interview script in a claims submission. The method may comprising receiving data relating to a claim being submitted, which may include claims submission data input by a user, information relating to the user, and one or more features. Data associated with the one or more features may be determined from an artificial intelligence model. A first score based on the data associated with the one or more features and data associated with the information relating to the user may be determined and used to determine an interview script. In one embodiment, questions in the interview script may continue to be provided to the interviewer computer if a continually updated score remains above a predetermined threshold. In another embodiment, the user may be routed to a live interview with a human representative if a continually updated score drops below a predetermined threshold.