Interactive Channel for Personalized Financial Insights
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Solution Overview
Problem
Users face challenges in accessing and managing transactions effectively, as they often lack insights into their transaction patterns, leading to unnecessary automated transactions.
Innovation Solution
A system that generates an interactive channel between a first party and a second party, using machine-learning algorithms to determine the intent of requests and provide personalized financial insights based on user profiles and historical transaction data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users manually review transaction records to identify unnecessary transactions, then they can obtain relevant information about their spending, but this process consumes significant time and effort
Solution Approach 1:
The system automatically analyzes transaction records and generates spending insights without requiring user intervention. The virtual assistant proactively identifies patterns, categorizes transactions, and presents actionable insights, allowing the system to serve itself rather than requiring manual user review.
Solution Approach 2:
The patent replaces the mechanical process of manual transaction review with an automated virtual assistant system that uses machine learning and natural language processing. This substitution transforms the time-consuming manual analysis into an automated computational process that delivers insights instantly.
2Productivity
If users execute automated transactions without analyzing transaction patterns, then transaction speed is maintained, but users may continue unnecessary spending on rarely used services
Solution Approach 1:
The system implements a feedback loop where transaction data is continuously analyzed, insights are generated and presented to users, and user responses are incorporated into future analysis. This feedback mechanism enables the system to learn from user preferences and progressively improve transaction recommendations while maintaining automated processing speed.
Solution Approach 2:
The virtual assistant performs preliminary analysis of transaction patterns before users execute transactions. By pre-identifying unnecessary or rarely used services through pattern recognition, the system enables users to make informed decisions about upcoming transactions, preventing unnecessary spending before it occurs.
3Loss of information
If a comprehensive set of transaction records is provided to users, then complete information is available for analysis, but the complexity of managing and interpreting this data increases
Solution Approach 1:
The system extracts only the most relevant and actionable insights from comprehensive transaction records, presenting them to users in a simplified format. Rather than overwhelming users with complete raw data, the virtual assistant selectively extracts key patterns, anomalies, and recommendations, reducing perceived complexity while maintaining information completeness.
Solution Approach 2:
The patent segments comprehensive transaction data into organized categories and structured formats that are easier to interpret. The virtual assistant divides complex transaction records into meaningful groups (e.g., by merchant, category, time period) and presents them in a hierarchical structure, reducing the cognitive load required to manage and interpret the data.
Data Source
AI summary
A computer-implemented method includes generating an interactive channel by a first party to provide a plurality of guided options to a second party. Additionally, the method includes receiving a request, via the interactive channel, from the second party and determining an intent of the request from the second party by a machine-learning algorithm. Further, the method includes generating an insight based on the determined intent of the request and a user profile of the second party and transmitting the insight, via the interactive channel, to the second party.