AI for Finance: Practical Workflows and Review Checks

AI is used in finance for tasks including research synthesis, financial modeling support, report drafting, and compliance document preparation. Every output that informs a decision, a client recommendation, or a regulatory filing still requires a qualified professional to review and sign off. Tools like fenbs help finance teams record the assumptions, source references, and process decisions approved for AI-assisted work, so each session starts from a clear and consistent baseline.

This guide covers the main AI workflows in finance and banking, the regulatory considerations that shape how outputs must be handled, and a practical review structure. For contract review workflows see AI contract review. For automating multi-step finance processes see AI automation workflows with human review.

Where AI fits in finance work today

Finance professionals use AI across a range of tasks that previously required hours of manual work. The common thread is that AI handles the drafting and synthesis while a qualified person reviews, adjusts, and approves the output before it is used. The model accelerates the work; the professional remains responsible for its accuracy.

• Research synthesis: summarizing earnings reports, analyst notes, and industry publications for a team's review.
• Financial modeling support: drafting model documentation, assumption tables, and narrative explanations of outputs.
• Report and memo drafting: first drafts of client reports, internal memos, and board presentations.
• Compliance documentation: drafting responses to regulatory inquiries, policy updates, and procedure manuals.
• Due diligence: organizing and summarizing findings from documents for a qualified reviewer to assess.
• Regulatory research: summarizing relevant rule updates from sources such as the SEC and FINRA for legal and compliance review.

What AI cannot do in finance is make a judgment call. Whether a particular security is appropriate for a client, whether a transaction requires regulatory disclosure, or whether a risk model's assumptions are sound all require human expertise and accountability.

Research and due diligence workflows

Research is one of the highest-value uses of AI in finance because the volume of material is large and the synthesis task is well-defined. An analyst can ask an AI assistant to summarize the last four earnings calls for a company, or to extract the key risk factors from a set of 10-K filings, and review the output for accuracy and gaps.

The review step is not optional. AI summaries can miss nuance, conflate figures from different periods, or omit a disclosure that an experienced analyst would flag. Every research output intended to inform a client recommendation or an investment decision must go through a qualified professional before it leaves the team.

Record the primary sources used in each research task and link them to the project. If an assumption later turns out to be based on a misread figure, a source link makes it possible to trace the error quickly rather than reconstructing the work from memory.

Financial reporting and analysis drafts

AI assists with the writing work in financial reporting: structuring a narrative around a set of figures, drafting the management discussion section of a report, or translating a model's output into plain-language client communication. The figures and the judgment behind them stay with the human author.

For client-facing documents, the review must cover both accuracy and suitability. An AI draft may use language that is technically correct but inappropriate for the specific audience, relationship, or regulatory context. A senior reviewer should check tone and suitability, not just numbers.

Regulatory compliance and audit readiness

Finance operates under a detailed regulatory framework. In the US, the SEC and FINRA govern investment advisers, broker-dealers, and their employees. AI use in workflows that produce content covered by these rules, including investment research, client communications, and recordkeeping, requires the same review and retention standards as non-AI-produced content.

A firm's compliance team is the right source for how AI-assisted content must be handled, reviewed, and retained for their specific regulatory status. Do not assume a general-purpose AI tool meets applicable recordkeeping requirements without checking with compliance.

Document the review steps that apply to each type of AI output in your process. An audit is easier when you can show not just what was produced but who reviewed it, against what standard, and what was changed. Those records also help you improve the workflow when review catches the same type of error repeatedly.

Source tracking and approved references

Finance work depends on traceable sources. When an AI assistant summarizes a filing or cites a regulatory rule, record where that information came from. A source that cannot be re-checked cannot be trusted in a compliance conversation.

Keep a links record for each active project that notes the primary sources, their retrieval dates, and the version of the document used. When a regulation is updated or a company restates a figure, a current links record shows which project outputs may need review.

fenbs is not a replacement for your firm's document management or compliance systems. Client files, confidential data, and anything subject to regulatory retention must stay in the appropriate systems. fenbs fits the layer above those systems: the process decisions, source references, and project status that help a team work consistently without re-explaining the rules each session.

How to do this with fenbs

Set up a fenbs project for each major finance workflow that uses AI assistance. Keep the approved process rules, source references, and review status together in one record.

1. Create a free account. Start with one active workflow, such as weekly research synthesis or quarterly reporting, and add it as a project.
2. Write your process rules in Decisions and rules. Record which source types are acceptable for each task, who must review which output types, and any restrictions on what can be drafted by AI without human drafting input first.
3. Track primary sources in the project's Links and listings. When you use a specific SEC filing, earnings release, or regulatory document, add it as a link with a note on what it was used for.
4. Connect your AI assistant from Integrations and ask it to read the project rules and source list at the start of each session.
5. After each session, ask the assistant to record which tasks were completed, which sources were used, and what review steps are still open.