Full Balance Sheet Advisor for Comprehensive Financial Planning
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Solution Overview
Problem
Automated portfolio management systems lack the ability to provide comprehensive financial advice as they are not integrated with a user's complete financial picture, including debt obligations, leading to incomplete investment recommendations.
Innovation Solution
The development of a Full Balance Sheet Advisor (FBSA) system that assesses a user's entire financial situation, sets financial goals, creates plans to achieve those goals, tracks progress, and adjusts plans based on financial events, using machine learning algorithms and GUIs for user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated portfolio management systems provide investment recommendations, then investment management efficiency is improved, but the comprehensiveness of financial advice deteriorates because the systems are not integrated with the user's complete financial picture including debt obligations
Solution Approach 1:
The patent combines multiple financial systems (investment portfolio management, debt management, budgeting) into a single integrated automated financial advisor platform. This merging allows the system to access and analyze the user's complete financial picture including investments, debts, income, and expenses simultaneously, thereby providing comprehensive financial advice while maintaining automated efficiency.
Solution Approach 2:
The automated financial advisor is designed as a universal system that performs multiple financial functions: investment portfolio management, debt obligation tracking, budget creation, goal setting, and comprehensive financial analysis. This multi-functionality enables a single system to address all aspects of personal finance rather than requiring separate specialized systems.
2Loss of information
If the FBSA system integrates and analyzes complete financial data including debt obligations and investments, then the comprehensiveness of financial advice is improved, but the system complexity increases
Solution Approach 1:
The patent segments the complex financial analysis task into distinct functional modules: data collection module (gathering financial information from multiple sources), data processing module (cleaning and standardizing data), analysis module (evaluating financial health and generating recommendations), and presentation module (displaying advice to users). This segmentation manages system complexity by organizing functions into manageable, independent components that can be developed and maintained separately.
Solution Approach 2:
The system employs intermediary components such as standardized data interfaces and financial modeling layers that mediate between diverse data sources (banks, investment accounts, debt providers) and the analysis engine. These intermediaries translate various financial data formats into a unified structure, simplifying the integration process and reducing overall system complexity.
3Measurement precision
If the system provides detailed, personalized financial advice considering all financial aspects, then the quality of financial guidance is improved, but the time required for analysis and plan creation increases
Solution Approach 1:
The system performs preliminary actions by continuously monitoring and pre-processing financial data in the background, maintaining an up-to-date financial model ready for analysis. When a user requests advice, the system leverages this pre-prepared information rather than starting analysis from scratch, significantly reducing the time required to generate personalized recommendations while maintaining high quality and precision.
Solution Approach 2:
The system implements feedback mechanisms where user responses to recommendations and actual financial behaviors are continuously fed back into the financial model. This feedback loop allows the system to learn from user decisions and refine its analysis algorithms over time, improving the quality and precision of guidance while optimizing analysis efficiency based on accumulated knowledge.
Data Source
AI summary
Disclosed in some examples are systems, machine readable mediums, and methods for providing a Full Balance Sheet Advisor (FBSA). The FBSA may be a network based service accessible to users using one or more computing devices. The FBSA may be accessed using a web-browser, or a dedicated FBSA application. The FBSA looks at the user's full balance sheet to provide advice on how the user can meet their financial goals.