The AI Tools Actually Worth Using in Financial Services and FinTech: A Practical Guide

There is no shortage of AI tool roundups on the internet. Most of them are written for a general audience, list the same ten platforms, and say nothing about what actually matters in a regulated, client-facing, compliance-aware environment.

This is not that article.

81 percent of financial services firms are now adopting AI at some level, yet only 14 percent currently see it as transformational to their organisational strategy, according to the 2026 Global AI in Financial Services Report from Cambridge Judge Business School. The gap between adoption and transformation is not a technology problem. It is a practical one. Most teams are using AI tools without a clear framework for which tool does what, where the governance boundaries sit, and how to build a workflow that is both productive and compliant.

This guide addresses that gap. What follows is a practical, honest assessment of the AI tools we think are genuinely worth your attention in a financial services or FinTech context, what each one is best for, and where you need to be careful before deploying them in a regulated environment.

ChatGPT

Best for: Fast ideation, quick drafts, research starting points, and brainstorming at speed.

ChatGPT remains the most versatile general-purpose AI tool available. According to a Bank of England and FCA survey of 118 UK financial services firms, 75 percent are already using AI, with a further 10 percent planning to within three years. ChatGPT is likely behind a significant proportion of that adoption given its accessibility and breadth of capability.

For financial services teams it is most useful at the top of a workflow. Generating first draft copy, exploring content angles, summarising long documents, and producing quick research passes before a deeper dive. It is fast, capable, and widely understood across teams.

The compliance consideration worth knowing: the default consumer version of ChatGPT trains on user inputs. Do not put client data, deal information, proprietary pipeline details, or anything commercially sensitive into the standard interface. If your team is using ChatGPT regularly, an enterprise plan with appropriate data processing agreements is not optional in a regulated environment. It is a governance requirement.

Claude

Best for: Long-form projects, nuanced reasoning, document analysis, and anything requiring sustained context and precision over an extended session.

Claude handles complexity differently to ChatGPT. Where ChatGPT excels at speed and breadth, Claude excels at depth and consistency. For financial services teams working on strategy documents, detailed briefs, complex client proposals, or content programmes that require a consistent voice and argument across thousands of words, Claude is the stronger choice.

The Projects feature is particularly useful for ongoing work. It allows you to maintain a persistent context across multiple sessions, which means the model retains background knowledge about your firm, your clients, and your specific requirements without you having to re-explain everything each time.

The connectors available in Claude's Projects are also worth highlighting for financial services teams already working across multiple platforms. Claude can connect directly to Google Drive, Gmail, Google Calendar, and tools like ClickUp, pulling context from live documents, emails, and tasks into the conversation without you having to copy and paste information across. For a team managing client work across several platforms simultaneously, having a single AI layer that can reason across all of them in one session removes significant friction from day to day operations.

Evident Insights named Anthropic the most referenced AI vendor in banking use cases in Q1 2026, overtaking OpenAI in share of public deployments. Anthropic's enterprise tier includes stronger data handling commitments than the consumer product. For regulated businesses working with sensitive commercial information, that distinction matters and is worth reviewing before deploying Claude across your team.

NotebookLM

Best for: Research synthesis, making sense of large document sets, and building a knowledge base around a specific project.

NotebookLM is Google's research AI and it does something none of the other tools on this list does as well. It lets you upload a set of source documents and then interrogate them directly, with every answer cited back to the specific source it came from.

For financial services teams this is genuinely valuable. Regulatory documents, market research reports, competitor filings, earnings calls, and industry white papers can all be uploaded and queried in natural language. The citation model means you can trust the outputs in a way that is difficult with general-purpose models that may hallucinate sources.

It is not a content creation tool. It is a research and synthesis tool. Used correctly it dramatically reduces the time spent manually reading and cross-referencing large volumes of material.

Gemini

Best for: Google Workspace integration, real-time search grounding, and tasks where current information matters.

Gemini's strongest differentiator is its deep integration with Google Workspace. For teams already working in Gmail, Google Docs, Google Meet, and Google Drive, Gemini surfaces across all of those environments and can summarise emails, draft responses, generate meeting notes, and pull context from documents without switching tools.

The real-time search grounding is also useful for financial services teams monitoring market developments, regulatory announcements, and sector news. Unlike models that rely on training data with a fixed cutoff, Gemini can pull current information into its responses which matters when the landscape is moving fast.

Perplexity

Best for: Research with cited sources, competitive intelligence, and answering specific questions with verifiable references.

Perplexity is increasingly how senior buyers in financial services research providers, compare solutions, and shortlist firms before making contact. That alone makes it worth understanding.

As a research tool it combines the conversational interface of a large language model with real-time web search and full source citation. Every answer links back to the source it was drawn from. For financial services teams where accuracy and attribution matter, that is a meaningful advantage over general-purpose models.

There is also a strategic reason for FinTech and financial services firms to pay attention to Perplexity specifically. If your target buyers are using it to find and evaluate providers, your firm's visibility in Perplexity responses is a commercial question, not just a technical one. This is part of what AEO, answer engine optimisation, addresses and it is an area where most financial services firms are currently invisible.

Granola

Best for: Meeting notes and summaries without the friction of traditional transcription tools.

Granola works differently to most meeting transcription tools. Rather than requiring you to start a recording or integrate with your video platform, it runs passively in the background on your device and produces a structured summary of your conversation automatically.

For financial services teams running multiple client calls, discovery sessions, and internal strategy meetings daily, the reduction in administrative overhead is meaningful. The summaries are clean, structured, and accurate enough to replace manual note-taking in most contexts.

Worth noting: several platforms your team likely already uses, HubSpot, Google Meet, Zoom, and ClickUp among them, now have native transcription and summarisation built in. Granola earns its place if you find those native tools fall short on quality or flexibility. Try both before committing to a standalone tool.

ElevenLabs

Best for: AI voice generation for content, outreach, and training material.

ElevenLabs produces the most convincing AI-generated voice available and the use cases for financial services and FinTech teams are more specific than most people realise.

For sales teams, AI-generated voiceovers enable personalised video prospecting at scale. A short video message with a human-sounding voiceover, personalised to the recipient's firm and role, adds warmth and credibility to outreach that text-based sequences cannot replicate. For content and marketing teams, ElevenLabs removes the dependency on studio recording for video content, explainers, and training material.

The compliance consideration: AI-generated audio representing your brand in a client-facing context in a regulated industry needs a governance framework before deployment. Make sure any AI-generated voice content is clearly produced within your firm's approved communication guidelines.

Runway

Best for: AI video generation for content marketing and sales material.

Runway is one of the most capable AI video generation platforms currently available for commercial use. For FinTech marketing teams without a video editor or production budget, it enables the creation of short explainer videos, social content, and product demonstrations that would previously have required significant resource.

The most relevant use case for financial services teams is short-form content. Social media video, internal communications, and prospecting material can all be produced faster and at lower cost than traditional video production allows.

The same governance principle applies here as with ElevenLabs. AI-generated video content in a regulated context needs to sit within your firm's approved communications framework. Synthetic faces and voices representing your brand in client-facing material carry reputational and regulatory considerations that generic AI video tools do not account for by default.

Grok

Best for: Real-time market intelligence, social signal monitoring, and sector news as it breaks.

Grok is the AI built into X, formerly Twitter, and its primary differentiator is real-time access to the full X feed. No other major model has this.

For financial services and FinTech teams monitoring regulatory announcements, competitor moves, funding rounds, and sector sentiment in real time, that is a specific and defensible use case. When a central bank makes an announcement, when a major FinTech raises a funding round, or when a regulatory body posts new guidance, Grok surfaces it faster than any other AI tool.

We include it here with eyes open. Grok sits within an ecosystem that comes with its own considerations and not every firm will be comfortable deploying it across their team. The real-time intelligence capability is genuinely useful. The context in which it sits is worth your own assessment before adopting it.

Harvey

Best for: Legal and regulatory analysis, compliance review, and contract intelligence in financial services.

Harvey is an AI platform built specifically for legal and financial services professionals. It is trained on legal and regulatory data and is used by major law firms and financial institutions for due diligence, regulatory interpretation, and contract review.

Most marketing and growth teams will not use Harvey directly. It is built for legal, compliance, and risk functions. However it is worth knowing about for two reasons. First, your compliance and legal colleagues may already be evaluating it and understanding its capabilities informs how you think about AI governance across the firm. Second, it signals the direction of travel for sector-specific AI. The general-purpose models will always have limitations in highly regulated domains. Purpose-built tools like Harvey represent what AI in financial services looks like when it is built for the context rather than adapted to it.

Canva AI

Best for: Visual content creation within an existing design workflow.

Canva's built-in AI tools, Magic Design, text to image generation, background removal, and brand kit application, are not the most technically advanced image generation available. Dedicated tools produce better raw image quality.

What Canva AI wins on is workflow. If your team is already producing social graphics, presentation decks, and marketing assets in Canva, the AI features are fast, good enough, and require no context switching. For a FinTech marketing team without a dedicated design resource, that practical advantage outweighs the quality gap in most everyday use cases.

For higher-stakes visual content where image quality and brand precision matter, it is worth exploring what Claude, ChatGPT with image generation, or dedicated design AI tools can produce. Use Canva AI for speed and volume. Use dedicated tools for quality and precision.

A note on governance

57 percent of FinTechs have already adopted agentic AI compared to 45 percent of traditional financial institutions, and 81 percent of industry respondents believe agentic AI will be meaningfully achieved across the sector by 2030. The pace of adoption is accelerating. The governance frameworks are not keeping up.

Every tool on this list has a consumer version and an enterprise version with meaningfully different data handling commitments. In a regulated financial services environment, the distinction between the two is not a minor detail. It determines whether your use of AI sits within your firm's compliance framework or outside it.

Before deploying any AI tool across your team, three questions are worth answering:

• What data will this tool touch and where does that data go?
• Does the vendor have data processing agreements appropriate for a regulated financial services environment?
• Who in the firm has oversight of how the tool is being used?

Building an AI toolkit without answering those questions is not a productivity strategy. It is a governance gap waiting to become a compliance problem.

The right AI toolkit for a financial services or FinTech team is not the one with the most tools. It is the one where every tool has a clear purpose, a defined boundary, and an owner who understands both.

People building systems. Systems building growth.