Wealth Management Customer Care Automation

Every customer interaction holds invaluable insights that can shape your business strategy, identify upsell opportunities, and enhance customer management. Yet, extracting actionable data from these conversations can be a time-consuming and challenging process.

Our Wealth Management Customer Care Automation Solution leverages the power of Generative AI to automatically transcribe, summarize, and extract key insights from client conversations, helping you unlock business value from every call.

• Automated Transcription & Summarization
  Frontier GenAI models are fined-tuned on your data to better understand industry terminology and company-specific jargon, then these models automatically transcribe customer calls and generate concise, highly accurate summaries, key discussion points, action items, and potential opportunities.

• Customer Sentiment Analysis
  Analyze customer sentiment to identify at-risk customers, and next best actions to prevent churn.

• Identify Upsell & Cross-Sell Opportunities
  Capture key customer preferences, concerns, and future financial goals from conversations, giving relationship managers a comprehensive understanding of each client. Identify patterns and cues that suggest potential upsell or cross-sell opportunities.

• Strategic Business Insights
  Analyze trends across multiple customer conversations to uncover valuable insights that help shape your broader business strategy. From emerging customer needs to evolving market trends, you’ll have the data to make informed, strategic decisions.

• Save Time & Increase Efficiency
  What once required hours of manual effort can now be done in minutes. Automated transcription and summarization streamline workflows, giving your team more time to focus on strategic initiatives and high-value customer interactions.

This powerful Generative AI solution was built with:

• Advanced agentic workflows

• Multi-document reasoning

• Retrieval augmented Generation (RAG)

• Advanced document extraction technology and fine-tuned extraction models

• Bespoke prompt engineering and turning, including in-context learning, few-shot learning, and task decomposition

• State-managed agentic workflows for reasoning against complex constraints