Planning, Advice, and Execution Platform for Scalable Guidance
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Solution Overview
Problem
Existing financial planning systems fail to provide a coherent, end-to-end experience tailored for each user, rely excessively on subject matter experts, and lack integration of disparate data sources, leading to inefficiencies and limited usability.
Innovation Solution
A planning, advice, and execution (PAE) system that synchronizes financial objectives with user aspirations and values, utilizing machine learning and rule-based engines to provide customized, collaborative experiences, and facilitate shared goal achievement among users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing financial planning systems provide comprehensive evaluation and prediction, then the depth of financial analysis is improved, but the system complexity and reliance on expert review increases
Solution Approach 1:
The system segments the complex financial planning process into distinct functional modules: data collection module, data normalization module, machine learning model module, and user interface module. Each module handles specific tasks independently, reducing overall system complexity while maintaining comprehensive analysis capabilities.
Solution Approach 2:
The patent introduces standardized data formats and normalization layers as intermediaries between diverse data sources and the machine learning models. This intermediary layer simplifies data integration and reduces the complexity of handling heterogeneous financial data.
2Reliability
If existing systems rely on subject matter experts for advice, then the quality of financial guidance is improved, but the productivity and scalability of the system deteriorates
Solution Approach 1:
The system enables users to generate their own personalized financial plans through automated machine learning models that process user-input data and generate recommendations without requiring expert intervention. This self-service approach maintains guidance quality while dramatically improving scalability.
Solution Approach 2:
The patent replaces the mechanical system of expert human review with automated machine learning algorithms and standardized evaluation frameworks. This substitution maintains consistent quality assessment while enabling the system to handle unlimited numbers of users simultaneously.
3Loss of information
If existing systems integrate multiple data sources, then the comprehensiveness of user profile is improved, but the difficulty of data integration and processing increases
Solution Approach 1:
The system implements a universal data normalization framework that can handle multiple types of financial data sources (bank accounts, investment portfolios, expense trackers, credit reports) through a single standardized interface. This multi-functional approach comprehensively captures user financial information while simplifying integration complexity.
Solution Approach 2:
The patent transforms diverse data from different sources into standardized parameters and formats through normalization processes. By changing the parameter representation of various data types into a common framework, the system comprehensively integrates multiple data sources without proportionally increasing processing complexity.
4Adaptability or versatility
If existing systems provide personalized financial planning, then the adaptability to individual needs is improved, but the device complexity and resource requirements increase
Solution Approach 1:
The system performs preliminary data normalization and feature extraction during the data collection phase, preparing processed data for later personalized analysis. This preliminary action reduces the computational resources needed during the actual plan generation phase while maintaining high personalization capability.
Solution Approach 2:
The patent implements dynamic machine learning models that adapt to individual user characteristics and financial situations. The models dynamically adjust their analysis depth and recommendation strategies based on user-specific factors, providing high personalization without uniformly increasing resource requirements for all users.
Data Source
AI summary
Various embodiments described hereby include components of a planning, advice, and execution (PAE) system configured to deliver an advice, planning, and attainment experience that focuses on understanding clients as human beings and what they want to accomplish with their life. The PAE system, or one or more components thereof, may operate to provide technology-based solutions that continuously sync financial objectives with aspirations and values through the many moments of life. These technology-based solutions may empower humans to make financial decisions and attain life objectives, big or small, simple or complex, that make a real and lasting impact on their lives and future generations.