AI-Driven User Profiling & Economic Intelligence
This research project is part of our "Piloting Progress" phase, where we focus on early adoption, user testing, and refining our MVP.
What is AI-Driven User Profiling & Economic Intelligence?
AI-driven user profiling is the process of analyzing, mapping, and optimizing financial behaviors using machine learning and predictive modeling. Within the CEIS ecosystem, economic intelligence refers to the ability of AI agents to interpret real-time financial data, identify patterns, and make personalized recommendations that help users improve financial outcomes.
Rather than static financial tools, AI-powered profiles continuously evolve based on user activity, preferences, risk tolerance, and economic conditions—enabling a dynamic, adaptive financial system tailored to each individual.
Why It’s Important to the CEIS Ecosystem
Personalizes financial decision-making – AI agents provide real-time insights, allowing users to optimize spending, saving, and investing based on their individual economic profile.
Bridges the gap between data and action – By analyzing transaction behaviors and financial patterns, AI agents transform raw data into actionable intelligence.
Enables predictive financial planning – AI-generated Personal Economic Graphs forecast financial health, purchasing power, and economic growth trajectories, allowing users to proactively adjust their financial strategies.
Description of the Protocol
The AI-Driven Economic Intelligence Protocol (AEIP) enables AI agents to:
Analyze real-time financial behaviors – AI models process spending habits, transaction history, and financial goals to develop individualized strategies.
Generate Personalized AI Agents – Each user is assigned an AI agent that continuously learns, adapts, and optimizes financial pathways.
Construct a Personal Economic Graph – A dynamic representation of a user's financial habits, risk profile, and potential growth trajectory, allowing AI agents to recommend tailored financial strategies.
Enable Autonomous Economic Optimization – AI agents execute automated optimizations, suggesting or even making transactions based on pre-set financial objectives.
Objectives: Testing & Optimization
Phase 1 is focused on refining AI models and validating their real-world effectiveness in optimizing financial decision-making. Key research questions include:
What behavioral patterns are most predictive of long-term financial health within an AI-driven economy?
How do users interact with AI-driven financial recommendations, and what levels of automation do they trust?
How should AI agents balance personalized optimization with broader network economic trends?
What factors determine the most effective AI-driven strategies for increasing user purchasing power and long-term wealth accumulation?
By addressing these questions, Phase 1 will refine AI-powered financial intelligence into a scalable, adaptive system that enables users to navigate and thrive in an AI-driven economic environment.
Supervisors
Leon Tsvasman Ph.D.
Yingbo Li, Ph.D.
Laurence Levin, Ph.D.
Mohyeddin Kassar, Ph.D.
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