Personalized Financial Recap System Using Generative AI
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Solution Overview
Problem
Users face challenges in staying updated with the latest financial news and events, as well as understanding complex financial concepts, due to the vast amount of information available online, which can lead to information overload and inefficiency in making financial decisions.
Innovation Solution
A machine-learning model generates a personalized financial recap, tailored to a user's specific interests and needs, by analyzing user account data, browser history, and literacy level, and creating a customized output such as a video, text-based output, or audio output that includes relevant financial news and events impacting the user's stock portfolio.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional methods display extensive financial content including plain language articles and charts, then users can access comprehensive financial information, but users experience information overload and find it tedious to review all content
Solution Approach 1:
The patent extracts only the most relevant financial information from the vast amount of available content using machine learning models. The system identifies and pulls out key insights, news items, and market updates that are most pertinent to each user's portfolio and preferences, filtering out irrelevant information to create a streamlined, personalized summary.
Solution Approach 2:
The patent applies local quality by customizing the type, depth, and presentation of financial information according to each user's specific needs, risk tolerance, and investment goals. Different users receive tailored content at different levels of detail, ensuring that information is both comprehensive enough and focused on what matters most to each individual.
2Loss of information
If the system displays long text and figures to provide comprehensive financial analysis, then users can access detailed information, but users struggle to review all content efficiently
Solution Approach 1:
The patent performs preliminary action by pre-processing and analyzing financial content before presenting it to users. The machine learning models continuously scan, read, and analyze financial articles, news, and market data in advance, organizing and pre-filtering the information so that when a user requests a summary, only the most relevant pre-processed information is delivered.
Solution Approach 2:
The patent incorporates feedback mechanisms where users can indicate what information is most useful to them, and the system uses this feedback to refine and improve future summaries. This continuous learning process ensures that the system progressively becomes more efficient at delivering high-value information with minimal review time.
3Measurement precision
If the system provides personalized financial recaps based on user data and browser history, then information relevance increases, but device complexity increases
Solution Approach 1:
The patent uses machine learning models as intermediaries between the raw user data and the final personalized content. These models act as intelligent mediators that process complex user profiles, browser history, and account data to generate accurate, relevant financial summaries without requiring the end user to manually configure or analyze complex system parameters.
Data Source
AI summary
Various embodiments of this disclosure relate generally to generating a customized output based on account data of a user. The method comprises receiving, by one or more processors, user data from one or more databases, wherein the user data includes user browser history and user account data, creating a user record based on the user account data, wherein the user record includes one or more stocks, determining a stock subset of the one or more stocks, retrieving one or more relevant segments from a vector database, wherein the one or more relevant segments correspond to one or more relevant events that are relevant to the stock subset, generating a personalized output script based on the one or more relevant segments, creating an output based on the personalized output script, and displaying the output on one or more user interfaces of a user device.