5 Ways GenAI is Changing Knowledge Sharing Across Finance Teams

In banking, knowledge is only as powerful as a team’s ability to access and apply it exactly when it’s needed. 

It’s only powerful when teams can access the knowledge they need at any time. Too often, valuable insights get trapped in inboxes, buried in PDFs, or siloed within teams, making collaboration slow and decision-making inconsistent. For financial institutions navigating volatile markets, evolving regulations, and rising client expectations, these issues in knowledge sharing aren’t just inconvenient - they’re costly.

To deal with issues, AI is one solution that a lot of financial enterprises have turned to. In particular, a subset of AI called generative AI (GenAI). We see this from the fact that banks spent $5.6 billion in 2024 on GenAI alone.

Whether it’s an advisor searching for the latest investment perspective or a branch manager looking for guidance on a new loan protocol, GenAI delivers precise, up-to-date answers in real time.

In this article, we’ll explore 5 ways in which GenAI, in particular advanced retrieval augmented generation (RAG), is redefining knowledge sharing across every layer of banking and financial services, from wealth management and compliance to investment analysis and the front lines of retail banking.

1. Wealth Management: Elevating Advisory Collaboration

We’re seeing GenAI change how wealth management teams operate by breaking down the information silos that have separated advisors, research analysts, and client service professionals. Instead of knowledge being scattered across emails, CRM systems, and market reports, GenAI creates a shared, real-time knowledge hub that brings together insights from every corner of the organization.

Think of a situation where an advisor needs to answer a question such as:

“What’s our house view on energy sector ETFs this quarter?”

By drawing internal knowledge repositories, real-time operational data, and third-party information sources, RAG-powered enterprise GenAI platforms give them a curated, up-to-date response synthesized from the latest research, investment guidelines, and internal commentary. They don’t have to waste time searching through inboxes or waiting for a colleague’s reply. With GenAI acting as a tireless digital knowledge worker, finance teams can access critical insights instantly. 

While GenAI delivers curated, up-to-date answers, advisors remain in the loop, responsible for reviewing and personalizing recommendations to ensure they align with each client’s unique goals and regulatory requirements.

The benefit of this unified approach is that every client receives consistent, high-quality advice - regardless of which team member they speak to or where that team member is located. 

GenAI doesn’t stop at answering questions. It can analyze a client’s risk tolerance, investment horizon, and financial goals to generate personalized investment recommendations or portfolio rebalancing suggestions. For example, if a client’s profile changes or market conditions shift, GenAI can proactively suggest new ETF allocations or flag potential risks. This lets advisors engage clients with informed advice due to improved data accessibility.

In addition, GenAI makes it possible for distributed finance teams to collaborate seamlessly, so that client insights aren’t ever lost in inboxes or Slack threads. They end up with a more informed and client-focused wealth management operation, providing your team with easy access to the institutional knowledge they need via AI knowledge management. It also simplifies administrative tasks like new client advisor onboarding or document processing, reducing time spent reviewing files by 50%.

2. Compliance & Regulation: Instant Access to Policy and Precedent

Regulatory knowledge is a moving target in finance. Not to mention, the cost of misinterpretation or delay can certainly be immense. With GenAI, compliance and policy documentation is always accessible instead of being buried in dense PDFs or lost to employee turnover.

Teams might pose highly nuanced questions like:

“What’s our AML escalation protocol for high-risk jurisdictions?”

Traditionally, these sorts of questions would be tricky to answer. With enterprise GenAI, they can receive clear, accurate answers synthesized from:

• The latest internal policies
• Regulatory updates
• Past casework

While GenAI does provide fast and accurate policy answers regarding risk, audit, and compliance management, compliance professionals must still interpret and validate responses to ensure regulatory adherence and mitigate risk.

This capability centralizes fragmented interpretations by providing a single, authoritative source of truth that is essential in audits and regulatory reviews. GenAI makes compliance knowledge universally available and up to date, resulting in improved consistency and reduced “compliance drift” across global offices.

Enterprises can use GenAI to scan, summarize, and compare thousands of pages of new regulatory texts in a matter of minutes, alerting compliance teams to important updates and generating actionable summaries. For example, when the EU updates its anti-money laundering directive, GenAI can highlight the sections most relevant to your enterprise, suggest necessary policy adjustments, and even draft communication templates for notifying affected teams.

GenAI can also monitor transactions and compliance workflows in real time, flagging anomalies or potential breaches for further review. For instance, if a suspicious transaction pattern emerges, Not only can it alert compliance teams, but it also provides context by referencing similar past incidents and the actions taken-supporting faster, more informed decision-making.

3. Investment Analysis: Collaborative Research at Scale

When it comes to investment analysis, enterpriser GenAI is changing how investment teams collaborate and share insights, leading to more dynamic and accessible research across the organization. Instead of analysts working in isolation or duplicating efforts, GenAI acts as a connective tissue by synthesizing, summarizing, and distributing knowledge in real-time.

It lets teams quickly surface answers to more complex questions like:

“What were the major drivers of Q1 underperformance in fintech stocks?”

The way in which the platform does so is by drawing from various sources, including:

• Earning reports
• Analyst notes
• Market commentary
• Historical data

Consequently, it generates comprehensive, context-rich summaries that are instantly available to all relevant stakeholders.

This shared research assistant doesn’t just store information. It also embeds institutional memory. Junior analysts can tap into years of past reports and investment theses, accelerating their learning curve and ensuring that valuable insights are never lost to turnover or forgotten in outdated files. 

Peer-to-peer learning becomes seamless, as GenAI lets analysts build on each other’s work, identify trends, and avoid redundant research. However, human expertise remains essential for interpreting findings and making final investment decisions.

The impact of GenAI in investment management is already being recognized across the industry.  One survey by Accenture showed that 96% of financial advisors think that GenAI can completely change client servicing and investment management.

Enterprises can process large amounts of unstructured data using GenAI because of its natural language processing capabilities. Some examples of unstructured data that GenAI can process include:

• Financial news
• Earning reports
• Social media mosts
• Analyst notes

This makes it possible to derive insights that would have been missed through manual analysis.

4. Risk & Treasury: Shared Understanding of Complex Models

When it comes to risk and treasury, quantitative models are crucial. The issue is that their complexity often creates barriers between specialized teams and the broader finance organization. To bridge these gaps, GenAI can be used to translate intricate risk models and financial strategies into clear, accessible narratives that everyone can understand.

For instance, a risk manager might be asked:

“What’s the rationale behind our current hedging strategy for interest rate risk?”

Typically, they would probably end up giving an answer using dense spreadsheets or one that contains technical jargon. Instead, GenAI generates a concise explanation in plain language that connects the model’s logic to real-world business objectives. The result is that executives, front-office teams, and even non-technical stakeholders can grasp the reasoning behind key decisions and contribute meaningfully to risk discussions.

What’s more is that GenAI makes it easy to share model updates, scenario analyses, and policy changes across departments. 

Whether it’s explaining the impact of a new value-at-risk model or outlining the implications of changing credit exposures, GenAI ensures that everyone, from treasury analysts to senior leadership, is working from the same playbook. 

It’s worth noting that we’re still in early stages of GenAI adoption in treasury as only 1% of treasury functions from larger enterprises are currently employing GenAI. This means that treasury functions looking to gain a competitive advantage will want to look into using GenAI.

5. Retail & Commercial Banking: Frontline Enablement Through Shared Knowledge

In retail and commercial banking, customer-facing teams are only as effective as the knowledge they can access at the moment. Enhanced with knowledge graphs, retrieval augmented generation can deliver deterministic GenAI outputs that provide teams with standardized answers to operational and policy questions – eliminating the delays and inconsistencies that often arise from relying on internal email chains or outdated manuals.

A branch manager or relationship officer may need to know:

“How do we handle loan restructuring requests under the new policy?”

GenAI instantly synthesizes the latest policy documents, regulatory guidance, and internal procedures to deliver a clear, actionable response. This ensures that every team member operates with the same up-to-date information.

We’ve helped banks deploy GenAI-powered virtual assistants that can instantly answer policy questions, guide staff through new procedures, and provide up-to-date information on regulatory changes through natural language queries. (We’ve put together an interactive demo showcasing some of these benefits that you can access here: https://squirro.com/squirro-blog/client-advisor-onboarding-genai.)

By distributing knowledge seamlessly across branches, service centers, and digital teams, GenAI can reduce training overhead and accelerates resolution times. New employees can ramp up faster, and experienced staff can focus on delivering exceptional service rather than searching for answers. The result is a more responsive and consistent customer experience, with every client interaction grounded in the organization’s collective expertise.

Bottom Line

As information multiplies and client expectations rise, the ability to share, surface, and act on institutional intelligence is what separates industry leaders from the rest. Siloed teams, outdated manuals, and fragmented communication are no longer sustainable in a world where every decision counts.

Squirro’s GenAI Enterprise Platform is purpose-built to meet this challenge. By unifying data from compliance, risk, sales, and research, it turns scattered insights into a living, shared asset-one that empowers every team member, from advisors and analysts to branch managers and compliance officers. The result is more consistent advice and faster advice.

Ready to see how Squirro can help implement GenAI to enhance knowledge sharing across your finance teams? Book a demo today, or download out recent white paper: Transforming Banking and FinancialServices With Enterprise GenAI.