Interactive Financial Summary Interface With LLM-Based Insights
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Solution Overview
Problem
Traditional banking statements and online accounts provide limited interactive and dynamic financial summary capabilities, failing to engage users effectively and offer actionable insights.
Innovation Solution
A system utilizing a large language model (LLM) to generate an interactive financial summary interface that includes data ingestion, graphical representation, contextual chat, and financial insights, leveraging techniques like Retrieval Augmented Generation and Chain of Thought prompting to provide personalized and dynamic financial analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional banking statements are provided as static documents, then information accuracy is maintained, but user engagement and interactivity are limited
Solution Approach 1:
The patent introduces an AI assistant as an intermediary between the user and the financial data. This assistant processes natural language queries, retrieves relevant information from the financial data, and provides contextualized responses. This mediator enables interactive engagement without requiring the entire system to become complex, as the AI assistant handles the complexity of data processing and interpretation.
Solution Approach 2:
The system enables users to independently query their financial data using natural language without requiring manual navigation through complex interfaces or contacting customer service. The AI assistant autonomously processes queries, retrieves relevant information, and provides insights, allowing users to self-serve their information needs efficiently.
2Loss of information
If online accounts provide detailed transaction information, then information completeness is improved, but actionable insights and analysis remain limited
Solution Approach 1:
The AI assistant provides feedback by analyzing transaction data and delivering actionable insights back to users. Instead of merely displaying raw data, the system processes information through the AI assistant, which generates contextualized insights, spending patterns, and financial recommendations. This feedback loop transforms complete but raw transaction data into meaningful, actionable information.
Solution Approach 2:
The patent replaces traditional mechanical information display systems with an AI-based cognitive system. Instead of simply presenting transaction data in tabular formats, the AI assistant cognitively processes the data, understands user intent through natural language, and generates insightful responses. This substitution enables the system to adapt to different user needs and provide versatile financial analysis.
3Ease of operation
If financial summaries are made interactive with chat functionality, then user engagement improves, but response accuracy and contextual understanding may deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-processing and structuring financial data before user interactions occur. The AI assistant is trained on and familiar with the user's specific financial data, transaction patterns, and account structure in advance. This preliminary preparation enables the AI to accurately understand and respond to user queries with high precision, maintaining measurement accuracy while providing interactivity.
Data Source
AI summary
Periodic bank statements may be created that are dynamic and interactive. Financial transactions of users can be ingested and similar users clustered together. The financial transactions of users can be displayed and additional insights generated using a large language model.