AWS re:Invent 2025 - Building the Future Trading Platform leveraging AI and AWS (AIM3307)

Building the Future Trading Platform at LPL Financial

Overview of the Wealth Management Industry

• The wealth management industry is a massive $144 trillion market with 300,000 financial advisors serving 68 million clients.
• A generational wealth transfer is underway, with baby boomers transitioning $100 trillion in assets to the next generation of more tech-savvy, digital-native clients.
• This shift is driving demand for personalized, intelligent, and instant financial services - a significant challenge for legacy systems.

LPL Financial: The Leading Independent Broker-Dealer

• LPL Financial is the #1 independent broker-dealer in the U.S., with 32,000 financial advisors managing $2.3 trillion in assets.
• LPL's value proposition is the flexibility and scale it provides to advisors, allowing them to customize their technology and tools.
• This customization creates a technology challenge, as the platform must adapt to diverse needs from small businesses to large institutions.

Trading and Investment Management at LPL

• Trading and investment management act as the "connective tissue" between advisors and the financial markets.
• Behind the scenes, hundreds of high-performance systems work in unison to execute, settle, and monitor trades with speed, accuracy, and transparency.
• LPL's trading ecosystem comprises 78 interconnected systems, along with external partners, creating a complex but seamless experience for users.

Business and Technology Goals

• Key business goals include maximizing customer/advisor satisfaction, retention, and cost efficiency to drive margin expansion.
• Technology goals to support these business objectives include:
  • Zero security incidents and 100% compliance
  • Ultra-resilient, always-on systems with fast recovery
  • 10x scalability and sub-millisecond performance
  • Cost optimization

LPL's Cloud Transformation Journey

• LPL is taking an iterative, incremental approach to cloud transformation, rather than a "big bang" migration.
• The journey started with migrating on-premises systems to the cloud, leveraging AWS services like EKS, Postgres, DynamoDB, and SageMaker.
• In 2024, the focus shifted to enhancing resilience by reducing on-premises dependencies and moving towards an active-active, multi-region architecture.
• Key challenges include ensuring data consistency across active-active regions and orchestrating requests between multiple regions.

Client Rebalancer: A Flagship Trading Platform

• Client Rebalancer is LPL's homegrown, models-based trading platform that enables advisors to manage client portfolios at scale.
• The platform automatically rebalances thousands of client accounts based on predefined investment models, recalibrating portfolios as market conditions change.
• Client Rebalancer processes millions of trades and drift checks daily, leveraging AWS services like Kafka, EKS, and in-memory caching to achieve high performance and scalability.

AI-Powered Use Cases

1. Market Center Outage Detection:

   • Uses a Random Cut Forest algorithm on SageMaker to detect anomalies in real-time trade data, alerting the operations team before market center outages impact advisors.
   • This "left-shift" approach helps prevent operational issues like duplicate orders.

2. ETF Classification:

   • Leverages an unsupervised Artificial Neural Network model on SageMaker to automatically classify new ETFs based on their underlying holdings.
   • This automates a previously manual process, ensuring proper compliance and regulatory checks for new ETF products.

3. Back-Office Automation:

   • Exploring a multi-agent AI framework to automate the complex process of tax-aware portfolio rebalancing.
   • Agents for rebalancing, validation, and trading work together to optimize outcomes, with the potential for 10x scalability.

4. Model Suggestions for Advisor Portfolios:

   • AI models analyze advisor books to suggest appropriate investment models, streamlining the transition to a models-based trading approach.
   • This helps advisors spend more time with clients rather than on operational tasks.

Key Takeaways

• LPL is transforming its trading platform to be cloud-native, intelligent, and highly scalable to meet the evolving needs of wealth management clients.
• The cloud journey is an iterative process, with a focus on reducing on-premises dependencies, enhancing resilience, and leveraging AWS services like EKS, Aurora, and SageMaker.
• AI and machine learning are being applied to critical trading workflows, automating complex tasks, detecting anomalies, and optimizing outcomes.
• The goal is to empower advisors with intelligent, adaptive technology that enhances their ability to serve clients and grow their businesses.