Case Study: AI-Powered Wealth Management Assistant

Wealth Management

AI-Powered Wealth Management Assistant

The Challenge: Service Model Scalability

A prestigious private bank was struggling to maintain its high-touch service model in an increasingly competitive landscape. Relationship Managers (RMs) were overwhelmed, managing 50 high-net-worth households each while spending 80% of their time on administrative tasks rather than client engagement. This inefficiency led to a concerning 12% annual client attrition rate and communication compliance issues, with 30% of client interactions occurring outside approved channels. The average response time to client inquiries had stretched to 2.5 days, creating frustration and missed opportunities. Additionally, the bank's traditional service model showed limited scalability for the growing mass affluent segment, while facing increasing pressure from digital-first wealth management platforms.

Our Solution: AI-Enhanced Client Service Platform

Lydatum designed and implemented a comprehensive AI-powered wealth management assistant that transformed the bank's service delivery model. The solution consisted of three integrated components:

• Intelligent Client Service Hub: Built on Amazon's cloud infrastructure, we created a sophisticated platform using Amazon RDS Postgres and Redshift for data management, while leveraging Amazon SageMaker and Bedrock for advanced ML and GenAI capabilities. This was integrated with Salesforce Financial Services Cloud and BlackRock Aladdin to provide a complete view of client relationships and portfolio analytics.
• Automated Workflow Engine: Using Python FastAPI services deployed on Amazon EKS, we developed a containerized microservices architecture that automated routine tasks and communications while ensuring compliance through AWS Glue's data governance framework. This enabled RMs to focus on high-value client interactions while maintaining consistent service quality.
• Change Management Program: The implementation was supported by a comprehensive adoption strategy that included in-depth interviews with top clients and the formation of an advisory council of senior RMs. After evaluating eight potential platforms, we conducted a carefully phased rollout to 25 RMs managing 250 clients, supported by a three-day certification program and ongoing technical team shadowing.

The solution's development was guided by bi-weekly feedback sessions, ensuring continuous refinement and alignment with both RM and client needs. This iterative approach was crucial in creating a platform that genuinely enhanced the wealth management relationship rather than simply digitizing existing processes.

The Impact: Transformative Service Evolution

The implementation of the AI-powered wealth management assistant delivered exceptional results across all key metrics:

The platform's impact extended beyond pure financial metrics. Response times to client inquiries were reduced from 2.5 days to just 3 hours, while RM capacity increased by 50% to 75 households per manager. Client satisfaction saw a 12-point improvement as they experienced more proactive and personalized service. Perhaps most significantly, the solution enabled the bank to create a new service model for the mass affluent segment, opening up a crucial growth market while maintaining the high-touch feel of traditional private banking.

Technologies Used: Amazon RDS (Postgres & Redshift), Amazon SageMaker, Amazon Bedrock, AWS Glue, Salesforce Financial Services Cloud, BlackRock Aladdin, Python FastAPI, Amazon EKS