GenAI-Augmented Financial Advisor
Integrating GenAI seamlessly into your workflow
Overview
Before generative AI, financial advising sessions required hours of preparation and research for advisors to provide their clients with relevant guidance. This time-consuming effort limited the number of clients advisors could take on.
Now, with GenAI, financial advisors can reach more clients with more targeted services using GenAI-Augmented Financial Advisor, a tool that analyzes customer calls and conducts relevant research, while providing real-time conversational recommendations.
With this Slalom accelerator, you can see efficiency improvements of~30%-60%, empowering advisors to expand their customer base, and provide a more comprehensive service.
Key use cases
- Sales training for financial advisors
- In-call recommendations for cross-sell and upsell
- Relevant research gathering for pre-call and post-call follow-up preparation
Example customer outcomes
Cross-sell and
upsell
Near real-time conversational recommendations allow advisors to identify the right opportunities to suggest additional services.
Customer base expansion
Increasing efficiency by 30%-60% helps advisors take on additional customers who may have been previously overlooked.
Efficiency improvements
The tool gathers relevant research both before and after advising sessions, potentially saving advisors significant time and effort.
Experience the future of financial planning
See firsthand how the GenAI-Augmented Financial Advisor analyzes customers calls, conducts research, and provides real-time recommendations.
Interact with demoExample Google services used
Bigtable
A scalable, fully managed NoSQL database service designed for handling large analytical and operational workloads with low latency and high throughput.
Vertex AI + Gemini
A comprehensive AI platform that enables businesses to build, deploy, and manage machine learning models with advanced capabilities and support for generative AI models.
Cloud Run
A fully managed service that allows developers to deploy and run containerized applications and microservices with automatic scaling from zero to N based on demand.
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