Chatbot Quiz System for Personalized Credit Education
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Solution Overview
Problem
Customers lack education on credit and personal finance, leading to increased debt and negative impacts on their credit scores due to improper credit card usage, and existing information sources are scattered and misleading, affecting brand loyalty and customer relationships.
Innovation Solution
A chatbot system that uses natural language processing and machine learning to determine customer education gaps by categorizing credit-related questions, providing personalized answers, and offering quizzes to educate customers on credit usage and scores, based on their specific credit history and profile.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If generic credit information is provided through multiple websites, then information availability increases, but information accuracy and relevance to specific customers deteriorates
Solution Approach 1:
The system performs preliminary classification of customers into segments (e.g., new customers, existing customers, high-risk customers) before providing credit information. This preliminary action allows the system to tailor information to specific customer needs, ensuring both availability and accuracy by delivering relevant information to the right customer segments first.
Solution Approach 2:
The patent applies local quality by providing different types of credit information to different customer segments. New customers receive educational content about basic credit concepts, while existing customers receive targeted information about their specific credit profiles and improvement opportunities, ensuring information accuracy for each local context.
2Adaptability or versatility
If credit information is customized to individual customer profiles, then information relevance improves, but system complexity increases
Solution Approach 1:
The system segments the customer base into distinct groups based on credit history, behavior patterns, and risk profiles. This segmentation reduces system complexity by applying standardized information templates to each segment while still maintaining high relevance, avoiding the need for fully customized information for every individual customer.
Solution Approach 2:
The patent creates universal information templates that can serve multiple customer segments simultaneously. A single information framework is designed to be adaptable across different customer types through parameter adjustments, reducing system complexity while maintaining versatility and relevance across diverse customer profiles.
3Reliability
If comprehensive credit education is provided to all customers, then customer understanding improves, but resource consumption increases
Solution Approach 1:
The system applies partial action by providing credit education selectively to customer segments who need it most, rather than uniformly to all customers. High-risk customers or those with poor credit histories receive comprehensive education, while low-risk customers with good understanding receive minimal or no education, optimizing resource consumption while maintaining reliable customer understanding where needed.
Solution Approach 2:
The patent implements self-service mechanisms where customers can voluntarily access credit education materials based on their own needs and interests. The system provides on-demand educational content that customers can access whenever they choose, reducing active resource consumption while still improving customer understanding for those who seek out the information.
Data Source
AI summary
Logic may receive one or more questions from a customer in a chat. The logic may associate the one or more questions with one or more categories of subjects. The logic may store indications of the one or more categories of the subjects with a customer profile. The logic may select questions for a quiz for the customer based on the one or more categories of the subjects in the customer profile from a set of questions. The logic may present the one or more questions to the customer. And, in some embodiments, the logic may receive answers to the one or more questions from the customer.