How GenAI Supercharges Client Advisor Onboarding in Financial Services

In banking and financial services, the pressure is always on to maximize revenue and optimize performance. Take new client advisor onboarding, which gives new joiners a financial advisor training phase before they truly start contributing to the bottom line. What if there was a way to dramatically speed up that process, equipping new advisors with the knowledge and skills they need to excel, not in months, but in weeks. With Enterprise GenAI, that is now on offer. 

Enterprise GenAI transforms how your organization manages knowledge, putting all of your data at your workforce’s fingertips, regardless where it is stored. By streamlining information retrieval, personalizing learning, and accelerating knowledge absorption, GenAI doesn’t just speed up the new advisor onboarding process, it directly fuels faster revenue generation and a quicker return on your talent investment. 

In this blog, we’ll explore precisely how enterprise GenAI can transform your advisor onboarding and deliver a tangible competitive advantage.

An Imminent Changing of the Guard

According to estimates by McKinsey, the consulting firm, the financial services industry is facing a growing scarcity of client advisors. With the new retirees exceeding new recruits, shortage levels could reach around 100,000 advisors in the US alone. “All told,” they write, “if the productivity gains are realized, the industry will need between 320,000 and 370,000 total advisors to meet demand by 2034.” Getting there will involve hiring and training somewhere from 30,000 to 80,000 new advisors over the next decade. 

To keep up with demand, the authors of the study point out that hiring efforts will need to be paired with efficiency improvements from emerging technologies – in particular GenAI: “According to our estimate, even a 30 to 40 percent average advisor adoption of more wealth-management-specific gen-AI-enabled tools and processes across the value chain and across the full advisor population by 2034 can deliver 6 to 12 percent of time savings—and, in turn, increase advisor capacity.” 

Specific areas of improvement the highlight include: 

• Client meeting preparation
• Financial proposal creation
• Day-to-day operations, administration, and compliance management
• Investment research

Streamlining New Financial Advisor Training

Increasing the efficiency of day-to-day operations is one thing. Onboarding new hires is another. But, here too, enterprise GenAI holds huge promise, redefining all aspects of financial advisor training. By teaming up junior client advisors with powerful GenAI employee agents that combine their institution’s corpus of proprietary knowledge with the reasoning capabilities offered by advanced large language models, client advisors will be able to hit the ground running with the support of virtual mentors. 

Hit play below for a sampling of how enterprise GenAI is already making a difference on the market today – empowering new client advisors with knowledge that they can tap into in the same way that they would exchange with a senior mentor in their firm. 

In this demo, a junior client advisor, Mark, uses the Squirro enterprise GenAI platform during a client meeting, gathering the information he needs to optimally advise a client who would like to roll over their 401(k) to an individual retirement account – a complex, multi-step process. 

• Search for information: Mark can use the search bar to access all of the bank's internal documents.
• Retrieve relevant documents: The semantic search accurately retrieves the most relevant documents quickly.
• Go directly to relevant paragraphs: Clicking on a document directs Mark to the most relevant section, saving time during the client meeting.
• Generate summaries: Mark can quickly extract the key points from automatically generated summaries of documents rather than having to read them from cover to cover.
• Ask follow-up questions: Mark can ask follow-up questions to the documents, in the same way that he would interact with a senior mentor.
• Ensure compliance: AI guardrails ensure that outputs are compliant with company and regulatory requirements.
• Create an email summary: Finally, Mark can create an email summarizing the main takeaways of the client meeting to share with the client and his superiors.

These are just a few features that Mark can lean on to make sure that he delivers maximum value during his early days as a client advisor. 

Beyond Financial Advisor Training: Prerequisites for Safe GenAI Adoption in BFS

Financial institutions are under close scrutiny both by industry regulators and their own customers. It makes sense, therefore, that safe and sustainable adoption of GenAI within these organizations requires rigorous adherence to accuracy, data privacy, and information security. While many enterprise GenAI providers claim to have met these demands, the scarcity of production-scale enterprise GenAI deployments suggests otherwise. 

At Squirro, we have over a decade serving financial service providers, central banks, and other organizations in highly regulated sectors – initially with enterprise search solutions and now leading the charge in enterprise GenAI.  

Privacy: For GenAI in BFSI, privacy isn't optional, it's foundational. This means rigorous protection of Personally Identifiable Information (PII) and strict enforcement of Access Control Lists (ACLs). A robust privacy layer is essential to prevent PII exposure during AI processing. ACLs are equally crucial, ensuring only authorized personnel can access specific data, maintaining confidentiality and meeting stringent regulatory demands.

Security: Security is another pillar. BFSI GenAI must adhere to the highest standards, including ISO 27001, and employ robust encryption to safeguard sensitive data. Secure deployment options are key, such as on-premises setups or single-tenant Virtual Private Clouds (VPCs), to ensure data residency and control. Crucially, the flexibility to choose and change Large Language Models (LLMs) allows organizations to adapt to evolving security threats and data residency regulations.

Accuracy: Accuracy is non-negotiable when dealing with financial decisions. Semantic knowledge graphs are the answer. By integrating deep domain knowledge and complex process flows, they enrich data completeness, drastically reduce AI “hallucinations,” and enable deterministic data retrieval, eliminating ambiguity. Knowledge graphs also offer full data lineage, providing transparency into how insights are generated and bolstering the trustworthiness of AI applications.

Flexibility: GenAI isn't deployed in a vacuum. BFSI needs solutions that integrate seamlessly with existing infrastructure. LLM-agnostic enterprise AI platforms are vital. They connect effortlessly to existing data sources and workflows, allowing for LLM flexibility and model mixing tailored to specific needs. This approach maintains control over security, cost, and performance and builds future-proof solutions that can evolve.

Scalability: Scaling GenAI in BFSI must be seamless without sacrificing privacy, security, accuracy, or cost-efficiency. Many organizations struggle with scalability when building AI in-house. While many enterprise-ready SaaS AI platforms promise fast deployment and security, few vendors have proven their ability to deliver permission-enabled and production-scale GenAI in this demanding sector. We have. 

Unlock the Future of Banking and Financial Services 

If you are seeking to embrace the transformative power of GenAI in the financial sector and gain a competitive edge, we’ve prepared a comprehensive technical guide that equips you with the knowledge and strategies you need to leverage GenAI to break down data silos and optimize operational efficiency. Learn how to navigate the complexities of data security and compliance, ensuring your GenAI solutions are deployed safely and adhere to industry regulations.

Download our technical essentials guide: Transforming Banking and Financial Services With Enterprise GenAI to discover practical use cases that showcase the potential of GenAI in automating processes, enhancing decision-making, and delivering superior customer experiences.

Understand the essential requirements for building reliable and trustworthy AI solutions within the BFSI sector, and gain insights into the fundamental components of a robust Enterprise GenAI platform. And empower yourself to navigate the evolving financial landscape with confidence and agility.