What is in the FSSC Future Skills Framework?

Most financial services firms are now aware that the Skills Compact places new expectations on how they demonstrate workforce capability, particularly around AI and emerging technology.

What is less well understood is the document firms are actually expected to align against: the FSSC Skills Framework.

Unlike the Skills Compact itself, which is a policy commitment, the Framework is a practical structure — a way of organising the skills, knowledge and proficiency levels that firms need across different roles.

For HR, L&D, risk and operations leaders, understanding what is actually inside the Framework is the first step toward any credible Skills Compact alignment work.

This article explains its structure, how it relates to the Skills Compact, and why AI literacy has become one of its most closely watched categories.

Why the Skills Framework exists

The FSSC (Financial Services Skills Commission) Skills Framework was developed to give the sector a common language for workforce capability.

Before its existence, firms defined and assessed skills largely on their own terms, making it difficult for regulators, trade bodies or firms themselves to compare capability across the industry.

The Skills Compact raised the stakes further. Government and regulators now expect firms to show, in a structured and comparable way, that their workforce has the skills needed to operate safely and competitively — including in areas like AI that did not feature in older competency models.

The Framework is the reference point that gives firms a shared structure to do this against, rather than each organisation inventing its own categories from scratch.

It is not itself a training programme, a certification, or a legal rulebook. It is closer to a taxonomy: a defined set of skill categories and proficiency levels that firms can map their own roles and training against.

How firms traditionally approached skills benchmarking

Before frameworks like this existed, firms typically relied on internally built tools to assess workforce capability.

Common approaches included:

• Competency matrices built by individual HR or L&D teams.
• Role profiles written for recruitment rather than skills assessment.
• Ad hoc training audits conducted before regulatory visits.
• Informal manager judgement about who "understood" a given topic.

These approaches worked reasonably well for stable, well-understood skill areas such as product knowledge or regulatory processes.

They were far less effective for fast-moving areas. Definitions varied between departments, proficiency levels were inconsistently applied, and there was no easy way to benchmark against other firms or against sector-wide expectations.

When a new capability area — like AI — needed to be assessed, firms were often starting from a blank page, with no shared reference for what "good" looked like at different levels of seniority or role exposure.

Where the Framework changes the picture, including AI literacy

The FSSC Skills Framework changes this by defining skill categories and associated proficiency levels centrally, rather than leaving each firm to define them independently.

Its structure typically includes:

• Skill categories — broad groupings of related capability, such as risk management, regulatory knowledge, technology and data, and increasingly AI literacy.
• Proficiency levels — defined stages of capability within each category, from foundational awareness through to advanced or specialist expertise.
• Role relevance — an indication of which categories and levels matter most for different functions, from front-office trading to operations and compliance.

What makes the Framework particularly relevant now is the explicit inclusion of AI and data literacy as their own categories, rather than folding them into generic "technology skills".

This matters because AI literacy needs differ enormously by role. A trader assessing AI-assisted pricing tools needs a different depth of understanding than a settlement analyst reviewing AI-flagged exceptions, or a compliance officer overseeing model governance.

A generic technology category would blur these distinctions. The Framework's more granular structure lets firms benchmark each role against a proficiency level that is actually appropriate to what that role does — and produce evidence of this that aligns with Skills Compact expectations.

Practical considerations for using the Framework

Using the Framework well requires more than simply reading it once.

Key considerations include:

• Avoid treating proficiency levels as a box-ticking exercise. A level should reflect genuine capability, not attendance at a single training session.
• Interpret categories against your own risk profile. Not every category will carry equal weight for every firm or role.
• Involve the right stakeholders. HR and L&D cannot map roles accurately without input from operational leaders, risk and compliance, who understand what each role actually does day to day.
• Expect AI-related categories to evolve. As AI capability and regulatory attention both move quickly, this part of the Framework is likely to be updated more frequently than long-established technical categories.
• Use it as a starting point, not an end point. The Framework tells you what to benchmark against; it does not design your training or assessment approach for you.

Firms that treat the Framework as a living reference — reviewed periodically and interpreted against their own structure — get considerably more value from it than those that map against it once and file the result away.

Example

A London-based commodities trading firm is preparing its annual workforce capability review ahead of a Skills Compact reporting cycle.

Its L&D lead uses the FSSC Skills Framework to map trading, operations and compliance roles against defined proficiency levels.

The exercise reveals that front-office traders and settlement staff have materially different AI literacy needs: traders need to understand the risks and limitations of AI-assisted pricing models, while settlement staff need to understand how to interpret and escalate AI-flagged exceptions correctly.

Working with the Head of Trading Operations and the Compliance and Risk Officer, the L&D lead produces a role-mapped skills gap report aligned to the Framework's categories.

This gives the firm a defensible, structured basis for its Skills Compact submission, along with a prioritised AI literacy training plan built around actual role requirements rather than generic training for everyone.

FAQs

• Is the FSSC Skills Framework mandatory?

  The Framework itself is not a standalone legal requirement in the way that specific regulatory rules are. However, it is closely tied to Skills Compact expectations, and firms face growing practical pressure to demonstrate alignment with it as evidence of workforce capability.

• How is the Framework different from the Skills Compact?

  The Skills Compact is the policy commitment — the broader agreement that financial services firms will invest in and evidence workforce skills, including AI literacy. The FSSC Skills Framework is the practical structure firms use to organise, benchmark and evidence progress against that commitment.

• Does the Framework cover AI skills specifically?

  Yes. AI and data literacy feature as explicit, growing categories within the Framework rather than being folded into generic technology skills. This reflects the level of regulatory and operational attention AI now receives across financial services.