Smart Scheduling Engine for Multi-Advisor Financial Tasks
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Solution Overview
Problem
Financial advisors face challenges in accurately managing and coordinating complex schedules and tasks for clients with multiple advisors and constituents, leading to misaligned incentives and difficulty in tracking and organizing relevant financial events and activities.
Innovation Solution
A system and method utilizing a smart scheduling application with a task management engine, data fusion suite, machine learning engine, and user/client profile datastores to automate and optimize task scheduling, integrating data from various sources and allowing for manual adjustments, with features like health analysis and natural language processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple advisors and third-party providers are involved in managing client financial activities, then comprehensive coverage of financial events is improved, but coordination complexity and misaligned incentives worsen
Solution Approach 1:
The system segments the complex coordination problem by creating separate functional modules: a data fusion suite that collects information from multiple advisors and third-party providers, a machine learning engine that processes and prioritizes events, and a task management engine that executes scheduled actions. Each module handles specific aspects of the coordination challenge independently, reducing overall system complexity while maintaining comprehensive coverage.
Solution Approach 2:
The patent introduces an intermediary automated scheduling system that acts as a mediator between multiple advisors and third-party providers. This intermediary collects data from all parties, applies machine learning algorithms to determine event importance, and coordinates tasks without requiring direct negotiation between advisors. The intermediary resolves misaligned incentives by objectively prioritizing events based on client benefit rather than individual advisor agendas.
2Ease of operation
If traditional calendar applications are used to track financial events, then simplicity of use is improved, but ability to automatically capture and organize relevant events worsens
Solution Approach 1:
The system implements self-service by enabling automatic capture and organization of financial events without requiring manual user input. The data fusion suite autonomously collects information from multiple sources, the machine learning engine automatically prioritizes events based on client-specific criteria, and the task management engine generates scheduled tasks. This self-service capability maintains simplicity of use while dramatically increasing automation, eliminating the need for users to manually track financial events.
Solution Approach 2:
The patent replaces the mechanical manual entry system of traditional calendars with an automated information processing system. Instead of requiring users to manually input events, the system uses the data fusion suite to gather information, machine learning to analyze and prioritize, and automated task generation to create schedules. This substitution transforms the calendar from a passive recording tool into an active, intelligent system that automatically manages financial events.
3Adaptability or versatility
If manual tracking and coordination of financial tasks is performed, then flexibility in handling unique client situations is improved, but time consumption and productivity worsen
Solution Approach 1:
The system performs preliminary action by pre-scheduling financial tasks and events based on historical data, client profiles, and machine learning predictions. The machine learning engine analyzes past client behavior and event patterns to proactively create optimized schedules before manual intervention is needed. This preliminary automation handles routine coordination tasks in advance, freeing up time for advisors to focus on complex, unique client situations that require human judgment and flexibility.
Data Source
AI summary
A system and method for task scheduling and financial planning, comprising a smart scheduling application, a task management engine, a data fusion suite, a machine learning engine, in communication with profile datastores, user devices, and third party or external data sources, to provide extensive, extensible, and highly modular scheduling and employee management for firms including financial firms and family offices. Extensions of the invention may include an employee or user health analysis engine, a time management engine, and a natural language processing engine.