Orchestrating Client Service Workflows in Wealth Management

  • Move high-value transactions forward, faster – Eliminate bottlenecks in client service workflows to accelerate onboarding, approvals, and key transactions.
  • Scale personalized service without adding overhead – Optimize how relationship managers operate, allowing them to service more clients efficiently while maintaining a high-touch experience.
  • Ensure privacy, security, and full regulatory compliance – Maintain confidentiality and meet compliance standards like FINRA, GDPR, and SOC 2 while streamlining service delivery.
  • How top banks are maximizing efficiency – Learn how leading organizations like Standard Chartered, Citibank, and Raiffeisen Bank have streamlined workflows to increase transaction velocity and improve client experiences.
  • Increase retention and drive business growth – Deliver exceptional service at scale, ensuring clients feel prioritized while maximizing team productivity.

 

Is Your WealthTech Ready for the Future? APIs: The Silent Game-Changer in Automation & AI

  • Learn from tech’s pivot playbook: how past disruptions foreshadow today’s API-driven revolution
  • Crush silos, build ecosystems: turn integration headaches into competitive moats with strategic API architecture
  • Cash the automation dividend: real-world tactics to boost margins through API synergy
  • You can’t open the AI lock without an API key: the role of APIs as the ultimate tool for releasing AI’s benefits
  • Avoid the “legacy trap”: why clinging to outdated systems could see you miss out on as much as 40% efficiency savings

Panel Discussion: GenAI – Accelerating The Pace of Change For Wealth

  • To what degree can you use client data for products and services?
  • What’s too much?
  • Building fruitful relationships with clients to strengthen trust
  • Critical data purity to allow clear communication and engagement
  • The impact of AI on consumer expectations: demanding quick and clear communication in a limited timeframe.
  • Using customer data in training and testing models vs synthetic data