Table of contents
Highlights
- Generative AI for financial services turns scattered policies and systems into fast, cited answers employees can act on.
- The clearest early wins are employee-facing: compliance retrieval, onboarding, audit trails, and everyday IT and HR support.
- Basic automation follows scripts, while agentic AI can reason and act across systems to resolve requests end-to-end.
- In regulated finance, governance and auditability often matter more than the model itself for capturing value.
- Human checkpoints and reviewable logs help keep AI accountable for customer-facing or decision-influencing output.
- Moveworks is designed to act as the agentic front door for financial services teams, helping resolve compliance, onboarding, and support requests securely across systems.
Over the past few years, we’ve seen IT security teams clamp down on rogue AI subscriptions within their companies in an effort to increase safety around business and customer data. They’ve made valiant efforts to create resources, educational opportunities, and stricter policies around AI usage, all in the name of data privacy.
Stanford found that generative AI (GenAI) reached 53% population adoption within three years, faster than the PC or the internet. So even though AI can take time to prove value, many people have been open-minded when it comes to experimentation.
That enthusiasm quickly carried over into the business world, and 74% of GenAI early adopters in the financial services sector estimate a return on investment (ROI) of more than 10%, according to Deloitte.
The problem that comes with AI’s popularity, however, is proving that it’s safe and governable, especially in sensitive industries like financial services. These four practical use cases show how firms are putting the technology to work while keeping regulatory requirements, explainability, and risk controls top of mind.
What generative AI means for financial services
Generative AI for financial services uses large language models(LLMs) to create, summarize, and interpret content and answer natural-language questions. For regulated industries, it’s helping turn scattered policies, systems, and records into fast, cited answers and even completed tasks.
If an employee is looking for travel policies for the next company off-site, they might interact with an internal-facing AI conversational agent that could help them answer basic questions like what class they’re allowed to book on the flight and how much they’re allotted for dinner each night.
This type of GenAI technology can span across departments like IT, HR, customer service, and finance. When an AI virtual assistant has strong integrations with platforms like knowledge bases, HR systems, or IT service management tools, it can pull relevant information to help employees get answers they need to complete routine tasks.
Within financial services, that could look like an employee using GenAI to pull together relevant policies and account information while reviewing a case, with similar capabilities supporting workflows like underwriting, fraud detection, and compliance reporting.
How generative AI differs from basic automation and agentic AI
Basic automation follows predefined scripts to support well-understood, defined workflows. Agentic AI, often referred to as AI agents, is a more advanced form of artificial intelligence capable of reasoning, planning, and acting more autonomously.
GenAI, on the other hand, uses LLMs to create and summarize content, whether it’s helping an employee with a presentation or composing an email. It can even do more complex work, such as generating code or analyzing contracts.
Say an employee submits an expense report. Basic automation might confirm the required fields are there and send it to the right approver. An AI agent could take the process a step further by checking the expense against the relevant policy and gathering any missing information before moving it to an approval workflow.
But if the employee needed more information before taking action, GenAI could interpret the company’s expense policy and explain whether the charge falls within the rules. In many cases, that answer may be all they need to decide what to do next.
So, before you make your next AI investment, the first step is deciding which type of AI you need to achieve your specific short- or long-term goals.
Where generative AI creates value in regulated finance
The clearest early value for GenAI in an industry as highly regulated as finance comes from employee-facing work: retrieving compliance guidance, guiding onboarding tasks, automating audit trails, and resolving everyday IT and HR requests.
Between August 2024 and May 2026, the share of adults using AI rose from 45% to 62%, and the share of workers using it for their jobs rose from 33% to 45%.
Being saddled with more admin work, Zoom meetings, Slack messages, and emails than ever, professionals are looking for a way to expedite their work during the focus time they actually get. Yet, with so many promises being made about what AI can do, they still need a tactical way to figure out what’s practical and where to get started.
For the financial services industry, these four generative AI use cases often make the most sense.
Compliance documentation retrieval
The biggest problem about internal policies isn’t the writing or rewriting of the policy itself. Usually, the issue lies in the employee's ability to find the most accurate version of the information. If GenAI is integrated with an up-to-date knowledge base, employees can ask conversational AI questions in plain language and get a cited answer in return that aligns with their role and region.
When fast, more consistent policy guidance is possible, the employee can answer questions within a self-serve environment that is far more interactive than a static FAQ page. This helps avoid escalations and allows teams in both HR and IT to focus on more strategic, high-value tasks, since they aren’t burdened with constantly answering a sea of similar questions.
Employee onboarding in regulated environments
Onboarding has historically been one of the most difficult and complex HR workflows. In regulated environments, internal teams must be especially careful with routine tasks such as provisioning and access requests, since access to highly sensitive data comes along with it.
Mandatory policy attestation and role-specific training are often needed as an additional barrier to prevent the wrong person from having access to tools that don’t make sense for their role. But GenAI can guide employees through the workflow using consistent steps and document the process through to completion. These digital transformation efforts can often lead to faster time-to-productivity for regulated roles.
Audit trail automation
Audit trail automation is a core part of any governance framework and should track AI prompts, outputs, actions, and versions. If every step is reviewable, it becomes easier to see how decisions and approvals unfolded from a high level. That way, a human team can go back to determine what went wrong and how to fix it.
In fact, the Financial Industry Regulatory Authority’s (FINRA) most recent guidance recommends that businesses keep a narrow scope on the AI they use and have a solid definition of what they hope to achieve with it. Starting with a thoughtful, well-planned foundation can make other factors like permissions, audit trails, and human checkpoints easier to implement.
Everyday IT and HR support
IT and HR teams are inundated with a slew of internal support requests, from password resets and policy questions to access requests. These repetitive tasks are typically highly manual and even more time-consuming, often robbing teams of time that could be spent on more strategic work.
GenAI assistants can pull up the information support needs without making them dig through different systems to find an answer. They may also assist with the paperwork afterward, such as summarizing the call or filling in ticket notes, freeing teams up for higher-value work.
Why governance and oversight determine success
In financial services, value depends less on AI’s foundational model and more on the governance around it. Firms need to vet each new AI tool for factors like readiness, oversight, and auditability. Without those fine-grained controls, even a capable model may not be worth the investment because the strong returns will be unlikely to materialize.
Remember those early adopters, 74% of whom reported ROI above 10%? Among firms that were later to adopt GenAI, only 44% reported the same level of return, which only raises the stakes for getting governance right.
Even more seriously, regulators expect this sort of oversight for GenAI, as it increasingly processes unstructured data related to internal communications — and even those with customers — that can be very sensitive in nature given the industry.
Governance is often what separates firms moving GenAI into real production from those still stuck in pilots.
What to evaluate before you deploy generative AI
Before deploying any sort of GenAI tool, you must take the time to evaluate its accuracy, data security, system integration, and human oversight. These criteria help determine whether a pilot scales or stalls in a regulated environment.
Even as GenAI adoption becomes more widespread, evaluation can be the difference between choosing a solution that delivers the ROI you want and one that remains inconsequential and stuck in a pilot. Here are three things to pay attention to as you get started on this step.
Accuracy and data security
You can request answers from GenAI all day, but if the output isn’t reliable, it will only create more work for your team as they step in to correct it. For financial services in particular, data security is also non-negotiable. Grounding answers in permissioned sources helps make the technology both safer and more useful, so teams can see the productivity gains that justify the investment.
Instead of focusing on the model’s capabilities alone, invest time into evaluating how the platforms you like handle sensitive data and permissions. You’ll also want to get a good grasp on the hallucination risk before moving forward with your choice.
Integration with existing systems
Aside from accuracy and data security, you’ll want to make sure your platform of choice is highly integratable with the existing systems within your tech stack, including your HRIS, ITSMs, and identity systems.
Check for prebuilt connectors for the tools your business already uses. If employees have to relearn everything or completely replatform just to integrate AI into their current workflows, adoption will likely stall.
Human oversight and auditability
Last but not least, you need to build human checkpoints into the workflows you’re reworking wherever they make sense. Human oversight helps keep AI accountable, especially for customer-facing or decision-influencing outputs. Having reviewable logs for any AI solution, whether it’s GenAI or agentic AI, is crucial to move your AI project into full-on production.
As the final part of your evaluation, you’ll need to confirm that the platform you have your eye on supports basic permissions and approvals. Most importantly, it needs to record action logs in detail to make this level of human supervision possible.
Choosing an AI platform for regulated financial services
The winners in the AI race are increasingly the ones that pair high-value use cases with governance, auditability, and end-to-end action. With GenAI, it’s about making workflows both impactful and secure while complementing the systems your team already uses, like Workday or ServiceNow.
Moveworks supports this approach by acting as the agentic front door to work. The Moveworks AI Assistant is designed to reason, apply permissions, and resolve compliance, onboarding, and support requests end-to-end for regulated financial services teams, within the guardrails your team defines.
With the additional guardrails required in financial services, your team deserves a solution that works within those constraints, without forcing you to build extra workarounds. Moveworks can help you get there.
Frequently Asked Questions
Generative AI in financial services uses large language models to create, summarize, and interpret content and to answer employees' natural-language questions. In regulated firms, it helps turn scattered policies, records, and systems into fast, cited answers. It is most useful when tied to real workflows rather than standalone content generation.
It lets employees ask policy and regulatory questions in plain language and receive role-specific, cited answers drawn from approved sources. This can reduce time spent searching portals and help teams apply guidance more consistently. When paired with permissions and logging, it can also make compliance workflows more auditable.
Basic automation follows pre-defined scripts and breaks when a request crosses systems or needs judgment. Generative AI can understand context and produce or summarize content, while agentic AI can reason, plan, and take action across systems. The practical difference is how much work actually gets resolved end to end.
It can be, provided you build in governance from the start. That means permission-aware access, human checkpoints for customer-facing or decision-influencing output, and reviewable logs of what the system did. Grounding answers in approved, permissioned sources also helps manage accuracy and data-security risk.
Lower-risk, high-volume internal workflows tend to offer the clearest early wins. Compliance documentation retrieval, employee onboarding, audit trail automation, and everyday IT and HR support are practical starting points. Each pairs measurable productivity gains with the oversight regulators expect.
