Personalized Wealth Insights

AI-driven investment recommendations tailored to individual user behavior and goals.

Published January 25, 2024

1 min read

Technologies Used

Python

AWS SageMaker

Next.js

GraphQL

Redis

Mobile app interface showing personalized investment portfolio and goal progress

Hyper-Personalized Finance at Scale

Traditional wealth management is high-touch and expensive, leaving millions of retail investors with generic advice. We built a Personalized Wealth Insights engine that uses AI to analyze transaction history, risk tolerance, and life goals to deliver institutional-grade investment strategies to every user's pocket.

The Challenge

Generic 'one-size-fits-all' advice leading to low user engagement.

Complex financial jargon alienating younger demographics.

Inability to scale human advisory services to mass market.

Disconnected view of a user's total financial health.

The Solution

Developed a Natural Language Processing (NLP) engine to categorize transaction data.

Built a recommendation system using Collaborative Filtering to suggest relevant products.

Created a gamified goal-setting interface to encourage saving habits.

Implemented real-time market alerts tied to user portfolios.

Key Features

Smart Budgeting

Automatically categorize spending and identify saving opportunities.

Merchant recognition
Subscription tracking
Peer benchmarking

Goal-Based Investing

Align investment strategies with specific life milestones (e.g., buying a home).

Time-horizon adjustment
Risk profiling
Auto-rebalancing

Educational Nudges

Contextual financial literacy content delivered at the right moment.

Jargon busters
Market explainers
Behavioral prompts

Technology Stack

Python

AWS SageMaker

Next.js

GraphQL

Redis

Project Timeline & Results

Week 1-3

Data Enrichment

Categorized 95% of transaction data accurately.

Ingested raw transaction logs. Trained a BERT-based model to classify merchants and spending categories. Enriched data with geolocation and merchant metadata.

Week 4-6

Recommendation Engine

Increased product cross-sell rate by 40%.

Built the core recommendation logic using AWS Personalize. Tested different algorithms (collaborative filtering vs. content-based). Integrated with the product catalog API.

Week 7-9

UX & Gamification

Doubled daily active users (DAU) post-launch.

Designed the 'Financial Wellness Score' interface. Implemented confetti animations and progress bars for goal milestones. Conducted user testing to ensure clarity and trust.