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AI training workshop for financial services and insurance teams
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Financial Services and Insurance

AI training for regulated teams handling sensitive data, dense documents, and client communication under scrutiny.

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Teams we've trained include

  • A global sustainable investment manager
  • A payments infrastructure provider to banks and fintechs
  • An infrastructure investment platform
  • A consumer fintech scale-up
  • A global systematic hedge fund managing $30bn+ across absolute return, macro, and fixed income strategies
  • A Swiss private bank and wealth manager serving high-net-worth clients across Europe and Asia
  • A FTSE 250 wealth management group advising financial advisers and investors across the UK
  • A Lloyd’s of London managing agency underwriting specialty insurance and reinsurance globally
  • A leading European bank with retail, corporate, and investment banking operations across multiple markets
  • One of the world’s largest banking groups, serving millions of retail and corporate customers worldwide
  • A global payments and financial services company processing millions of transactions worldwide
  • A private bank and trust company providing wealth planning, investment, and fiduciary services
  • An emerging markets fund manager investing in equities and fixed income across Asia and Latin America
  • A financial intelligence and research firm providing analytics and risk insights to institutional clients
  • A UK wealth management business providing financial planning and investment advice to retail clients
  • A global health insurer and healthcare group serving tens of millions of customers across 190 markets

Why firms like yours hire us

  • Our AI training for financial services and insurance teams is built for regulated firms that are interested in AI but rightly cautious about privacy, compliance and auditability.
  • Sessions use your own regulatory updates, compliance checklists, vendor contracts, management packs and investment committee papers.
  • We have trained teams at hedge funds, private banks, wealth managers, asset managers, payments firms and insurers.
  • We train each team individually, in person, and one-to-one coaching is available for anyone who wants help on their own work.

AI use cases from our financial services and insurance clients

Each one was built in our sessions with clients like yours, and comes from our library of more than 1,000 AI use cases.

Regulatory horizon scanning

Tracking regulatory change is slow and manual. Weekly agents search only primary regulator sources and return a cited table of changes.

Independent claim checking

Every published figure needs checking. Three sub-agents check each claim in a draft against its source report before it goes out.

Due diligence gap analysis

Reports from different regions rarely line up. An agent compares them to show where they disagree and what is missing.

Broker notes against earnings calls

Analysts cannot read every note. Broker forecasts are checked against management's own words on the call, with every claim cited.

Expert call coverage gaps

Research teams miss angles they have not covered. Mapping a company's supply chain and competitors shows where expert calls are still needed.

Investor meeting briefings

Meeting prep means pulling from five systems. One-page briefings combine holdings reports, CRM history and public context, with sources cited.

The numbers your competitors are watching

Where financial services stands on AI, from the latest regulator and official data.

67%

of US finance and insurance workers already use generative AI

St. Louis Fed
92%

of EU banks are deploying AI

EBA
65%

of European insurers use generative AI

EIOPA
17%

of financial firms say their board fully understands AI

ESMA

How your peers are moving

Asset managers, lenders and fintechs are putting AI in front of their whole workforce. Few have the budget or time to build their own training academy. We give your team that capability in days, not years.

Jupiter Asset Management

More than 300 Jupiter staff have corporate ChatGPT licences, with engagement above 90%. The firm estimates it saves each user around 42 minutes a day, and it added further staff AI training in 2025.

Annual report 2025

Ninety One

Every Ninety One employee now has access to enterprise AI tools, and adoption has reached 90%. In the year to March 2026 the firm made AI a firm-wide priority and ran staff sessions on using it effectively and responsibly.

Annual report 2026

Funding Circle

In September 2026 Funding Circle said more than 90% of its staff were using AI often in their jobs and building their own agents and projects. One agent in its operations team cut data access requests from up to two days of manual work to minutes, plus a couple of hours of expert review.

Half-year results 2026Annual report 2025

Zopa

In August 2025 Zopa said every employee now has access to generative AI tools such as ChatGPT, backed by training. It also co-founded Jobs2030, a campaign to retrain 100,000 banking and fintech workers in AI by 2030, starting by testing the courses on its own staff.

Retail Banker International

Man Group

In February 2026 Man Group began working with Anthropic to build AI into its work, from the investment floor to distribution and HR, with employees getting wider access to AI tools. Its own researchers had already put staff adoption of generative AI at more than 70%.

SourceAdoption

We can usually identify your highest-impact use cases in a single conversation.

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What we train on

  • Rewording and summarising regulatory papers, policy documents, and legal guidance
  • Reviewing long compliance, investor-relations, and client documents faster
  • Fixing Excel formulas, supporting Power BI work, and first-pass financial analysis
  • Researching accounting standards, tax rules, and risk topics across jurisdictions
  • Drafting and refining client correspondence and internal communications
  • Producing internal and client-facing reports faster
  • Finding process gaps and best-practice workflows across teams or business units
  • Comparing policy wordings, reviewing submissions, and summarising claims files for underwriting and claims teams
  • Drafting DDQ and RFP responses, investment memos, and investment committee papers
  • Pulling themes from portfolio and market reporting, earnings calls, and broker research

Courses for financial services and insurance teams

What makes us different

  • Everything is bespoke. Each session is built around your own data, workflows and use cases, such as your compliance checklists, management packs and investment committee papers, so people practise on real work.
  • Our instructors are AI practitioners, not career trainers. They are founders, operators and advisers who use AI in their own work every day, so they answer the hard questions from direct experience.
  • We take a data-driven approach. We survey your teams before and after the programme and measure time saved, AI adoption and return on investment, so you can see exactly what changed.
  • We are a full-service AI enablement partner. Beyond the training, we offer AI strategy for leadership, one-to-one coaching for key people, and rollouts across multiple teams.

How we get your team there

A practical playbook

  1. 1

    Anchor it in the firm's priorities

    Start where faster review, clearer reporting or tighter controls create the most value: regulatory change, contract and renewal review, month-end reporting and investor communications. Firms that tie AI to those priorities, rather than treating it as a general productivity tool, build capability that compounds across teams.

  2. 2

    Build on real documents and workflows

    Teams adopt faster when training uses their actual policies, compliance checklists, DDQs, quarter-end packs and investment committee papers. Where real documents can't be shared, clearly labelled synthetic versions keep the exercise close to the job. Abstract demos generate interest. Working with live material changes behaviour.

  3. 3

    Fix the data and access rules early

    Agreeing which tools are approved, which connectors are switched on and which files stay in the firm's own systems is what turns AI into something compliance will sign off. Most firms find that access and governance, not model capability, are the real constraints.

  4. 4

    Measure adoption, not enthusiasm

    Survey teams before and after training, then track time saved and which workflows are actually in use, so AI is judged by operating impact, not licence count. The goal is a smaller set of workflows that are measurably better and that the team trusts enough to rely on.

“As an insurance executive in an industry where AI is expected to be a massive disruptor… the course was right on point.”

Active underwriter, Lloyd’s market participant

“General Purpose’s session was engaging, practical and really helpful.”

Participant at a global sustainable investment manager

“This should be a massive timesaver and may well raise questions and concerns that the committee has previously overlooked.”

Finance team member at an investment manager

Ready to build AI capability in your firm?

We can map the highest-value use cases for your team and design bespoke sessions around your actual documents and workflows.

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Sources and further reading

ESMA: AI adoption and trends in securities markets, EU evidenceSt. Louis Fed (FRED): Generative AI adoption for work, finance and insuranceManagement Science: The rapid adoption of generative AIUS Census Bureau: Large firms with at least 20 employees biggest AI usersBank of England: Artificial intelligence in UK financial services 2024FCA: AI in UK financial services, research noteHouse of Commons Treasury Committee: AI in financial servicesFCA: Mills Review into the impact of AI on retail financial servicesBank of England: Summary of AI roundtables with regulated firmsEIOPA: Generative AI survey of European insurersEBA: Rising application of AI in EU banking and paymentsEBA: Special topic, artificial intelligenceFinancial Stability Board: Monitoring adoption of AI in the financial sectorFinancial Stability Board: Sound practices for responsible adoption of AI