A gamified goal tracking dashboard - Progressive Disclosure for AI recommendations
Synthesized all model-generated inference results into a dynamic dashboard that utilizes progressive disclosure to communicate personalized investment strategies and provides a seamless transition to human-in-the-loop (HITL) consultations. Incorporated predictive milestone markers for iterative goal tracking alongside visualizations of model-projected trajectories that facilitate calibrated user agency regarding capital requirements.
Reducing API query load for larger datasets
Built a card based feature to create incentive among users for investing in suggested products by pulling required metadata from the main dataset. Each unique user input can be classified as a long term or short term goal, and each goal type will be mapped to respective set of four investment products, within which the selected percentage return values can be converted to a face value in Rupee and displayed on the dashboard.
Product Discoverability and search
Optimized product content and design to facilitate discoverability and better SEO by using highly searched keywords such as "vision-board", "plan a dream", etc. Contextualized entire user flow to create a loop that encouraged users to try different investment strategies through prompts such as "Got more plans? We're game for it!" and "Ready, Steady, Goal!" along with the editable input menu on the dashboard.
AI recommendations that retain user agency
Architected a dynamic, co-active user experience not just by providing users with parameterized fields for model inference but also a real-time interpretability layer that synchronizes client-side interactions with backend model logic to calibrate user mental models regarding their financial choices and also provide predictive heuristics that prompt them to align with optimal confidence intervals. Given that the target audience comprises novice users with a cold-start mental model, these interpretability features facilitate calibrated trust in the system.
A Scalable Design System for future developments
Detailed out components and guidelines for the product, used material 3 component library which is easy to tailor to project specifications and can automatically update components as and when the source library is updated with minimal developer rework.
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