AI in Fintech: Personalized, Real-time Retirement Portfolio Management

Customer Overview

Our client, a US-based technology development company, builds products across domains like finance and logistics to solve real-world problems. They’ve developed top-performing SaaS platforms that help users automate tasks and enable businesses to secure continuous revenue streams. Backed by visionary leadership and driven by innovation, our client’s team is always in pursuit of building the next transformative solution.

Project Overview

Fueled by a vision to help people achieve long-term economic security in their retirement years, our client set out to develop a fintech solution in the form of a SaaS-based wealth management software. Recognizing that retirement accounts like 401(k)s and IRAs often underperform due to poor investment strategies, the client aimed to offer accurate, data-backed, and personalized recommendations for asset allocation. They envisioned a platform that would hyper-personalize each user’s portfolio, actively monitor and manage it, and dynamically respond to changing market and economic conditions.

Challenges

Developing a seamless, hyper-personalized wealth management software for retirement portfolio optimization, including real-time data accuracy, intuitive visualizations, and tailored investment recommendations

• The platform users must be able to view accurate periodic (daily, weekly, monthly, yearly, or custom period) profits/losses on all their accounts (401K, IRA, etc.), both individually and combined.
• The system must provide personalized asset allocation recommendations after carefully considering the user’s financial goal, age, income, current portfolio, risk levels of investments, and changing market trends.
• All recommendations must be backed by up-to-date, accurate financial data coming from reliable sources.
• Complete financial information must be displayed in a simple, understandable visual format. Be it account-level profits/losses, transactions, etc., asset-level historic, current, and trend data, or asset allocation recommendations.
• The platform must provide a seamless experience to its users, from easy onboarding to effective portfolio optimization and smooth management, all while offering hyper-personalization.

Solution

Using AI for wealth management, we built a Retirement Portfolio Management Platform that unifies retirement accounts, visualizes performance, and personalizes investment recommendations to enable informed, goal-aligned portfolio decisions.

• Using Java 21, Spring Boot, Python, and Angular 17, we developed a responsive SaaS web application to help retirement account holders manage and optimize their investment portfolio across devices, including mobile.
• Developed integrations to onboard retirement accounts into the web app. Created data ingestion pipelines to aggregate transaction data of every account into a unified time-series format and built accurate profit/loss computation capabilities.
• Integrated multiple trusted data sources like Alpha Vantage and Twelve Data using AWS Glue to ensure accurate, real-time financial information. As data from different sources comes in varied formats, we cleaned, standardized, and stored the data in AWS S3 for reliable access.
• To present all financial data in a simple, understandable visual format, we used charts and tooltips, grouped data into periods (daily, weekly, etc.), and used color coding, like green for profits and red for losses. Provided legends and explanations in the UI and used frontend charting libraries, while ensuring well-structured, accurate dataset inputs in the backend.
• We build a portfolio optimization process, curating each step in detail from gathering users’ goals, analyzing the current portfolio, to providing portfolio improvement suggestions.
• Categorized users (aggressive, moderate, defensive investors), added weights to stocks (high-risk, balanced mix, safer stocks), and built a function to match the right stock buckets to the user’s goals.
• Built an ML predictive model using Scikit-learn to analyze risks and returns, consider every user’s unique goals, and provide reliable, personalized recommendations.
• To provide a seamless experience of this wealth management software, we enabled caching to allow the system to remember where the user left off in the journey and allow them to continue.
• Using AWS Cognito, we ensured secure sign-up, sign-in, and access control for all accounts on the platform.

Benefits

The AI-powered Retirement Portfolio Management Platform we developed offered the following advantages:

• End-users experienced up to 18% better portfolio outcomes through personalized asset allocation recommendations.
• User engagement increased 2.3x over 12 months, driven by clear, visualized account performance.
• The goal-aligned investment experience reduced the platform’s churn rate by 30% post-launch.
• The SaaS product built trust and credibility by integrating real-time data from verified financial sources.

Technology

• Java 21
• Spring Boot
• Python
• Angular 17
• AWS Glue
• AWS Cognito
• AWS Lambda
• AWS S3
• Scikit-learn

Industry

• Fintech