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Skills frameworks are the structure organizations use to define, in shared language, what its people can do: the skills each role requires, at what level, and how proficiency is verified.
Employers expect 39% of workers’ core skills to change by 2030, according to the World Economic Forum’s Future of Jobs Report 2025, and in financial services, three further pressures compound that baseline.
The first is regulatory. Supervisors increasingly require documented evidence that staff are competent for the work they perform continuously, not at hire. The second is operational: business leaders need to redeploy talent ahead of restructuring rather than months behind it. The third comes from employees, who leave when they cannot see an internal path.
All three demands land on the same gap. Most financial services organizations cannot reliably answer basic questions about their own workforce’s capabilities.
The difficulty is not a lack of effort.
Consider the compliance officer who can model a complex derivative but cannot transfer that judgment to a junior analyst. Or the relationship manager whose client knowledge exists nowhere outside his own head. Work this divergent resists a common structure. Frameworks that look complete in a vendor demo routinely fail in a trading business for exactly this reason.
And they fail in a characteristic way. Not visibly, but through drift, technically live in a system while moving a little further from how the business operates each quarter, until the framework informs no real decision about hiring, mobility, or development.
When did someone in your organization last open the skills framework you built?
Why building a skills framework in financial services is so hard
Roles that don’t share a scale
Few industries hold such genuinely divergent work under one roof. A universal bank employs revenue businesses, control functions, and a technology organization that differ in kind rather than degree. And the capabilities that define each resist a common measure.
What makes a trader effective is judgment under pressure, exercised in seconds; what makes a compliance officer effective is the discipline to document why a decision was defensible when someone asks three years later.
The sector has measured this way for as long as anyone has looked: in the World Economic Forum’s first survey of skills stability by industry, Financial Services & Investors ranked as the least stable group measured; 43% of skills rated unstable, against 35% for industries overall.
A single framework has to describe all of this work. Most break in one of two directions. Some flatten everyone into competencies so generic they describe no one: “stakeholder management,” “analytical thinking.” Others splinter into role-specific silos that can never be compared against each other.
Data that was never built to carry this
Skills initiatives in established banks and insurers run on HR infrastructure designed for other purposes. Core HR lives in one system, learning records in another, regulatory certifications in a compliance tool that talks to neither, and a meaningful share of what people can actually do exists only in spreadsheets owned by individual team leads.
The pattern is general rather than anecdotal: in Sapient Insights Group’s annual HR Systems Survey, over 90% of organizations reported still using Excel at some level for HR data cleaning, analysis, and visualization.
The practical consequence: a question as simple as “how many people in our EMEA markets business are qualified to do X” becomes a three-week manual exercise involving four teams.
Regulation that turns competence into evidence
Each new requirement moves competence further from something a firm develops and closer to something it must prove:
- MiFID II requires firms to ensure, and demonstrate to their regulator on request, that staff giving investment advice or information hold the necessary knowledge and competence, with records of that competence submitted to competent authorities when asked.
- The FCA’s Senior Managers and Certification Regime (SM&CR) makes firms, not the regulator, responsible for assessing and certifying that named individuals are fit and proper for their roles. The regime is being streamlined, with the government moving prescriptive requirements like annual recertification out of primary legislation and into regulators’ rulebooks. That’s a shift that reduces the prescribed process while leaving the firm’s duty to evidence fitness and propriety fully intact.
- DORA, applying since 17 January 2025, pulls operational resilience and ICT skills into scope: the engineers and technology risk teams who once sat outside the competency conversation are now inside it.
- SR 11-7, the Federal Reserve and OCC’s supervisory guidance on model risk management, has made the specific capabilities of quant and validation teams a supervisory concern since 2011. This is not a new expectation firms are adjusting to, but a fifteen-year-old one they are still operationalizing.
In this sector, a skills framework functions as a development tool and, increasingly, as the evidence base a firm reaches for when a regulator asks it to prove its people can do their jobs.
A vocabulary that means different things on different floors
The least visible obstacle is semantic. “Risk management” describes one capability to a credit analyst sizing exposure on a loan book, another to a trader managing downside on a position, and a third to a compliance manager assessing regulatory breach.
The same two words covering three unrelated skills. The ambiguity multiplies across business units and geographies, where “advisor,” “associate,” and “manager” carry different weight and different regulatory meaning in New York, London, and Singapore.
A framework built on those unexamined terms inherits every ambiguity in them. Query it for “risk management” and it returns credit analysts, traders, and compliance managers as though they held the same capability.
Managers who staff from that list quickly learn not to, and the framework’s reputation rarely recovers. By the time people notice low adoption, they often diagnose it as a software or change management problem. In reality, teams created the underlying problem during the definition stage, long before they chose a system.
Why most financial services skills frameworks fail
The failure patterns are consistent enough to be recognizable. There are three, and most organizations that have attempted a framework have lived through at least one.
Generic taxonomies borrowed from HR vendors
A cross-industry skills taxonomy fails in financial services because the work that defines the sector is absent from it. A typical off-the-shelf taxonomy includes:
- “Stakeholder management,” “data analysis,” and “effective communication” in abundance
- Model validation and model risk
- Suitability and appropriateness assessment
- Trade surveillance and AML investigation
Organizations adopt these taxonomies because they appear complete, and apparent completeness is persuasive at the approval stage. The gap becomes visible only in operation, when the framework is queried with a sector-specific question (who can validate this model, who is certified for this product) and lacks the vocabulary to answer it.
Frameworks built by L&D without business buy-in
A framework built by L&D alone fails through non-adoption rather than inaccuracy. When skill definitions are written without sustained input from the business lines they describe, the result is internally coherent and unrecognizable to the people it covers:
- The trading desk never sees itself in it
- The relationship managers were never asked
- The risk function received a survey that went unanswered
Adoption follows trust, and trust follows authorship. Managers who had no hand in the definitions have no reason to prefer them over their own judgment, so they launch the framework, acknowledge it, and then ignore it when making decisions.
Frameworks too granular to maintain, too vague to use
A framework with hundreds of skills and multiple proficiency levels per skill fails on two operational counts:
- It cannot be kept current: every new product line, regulation, or reorganization dates a section of it, and the maintenance burden scales with its size
- It is too dense to apply: a structure a manager cannot use in a one-to-one review is a structure managers work around
High granularity increases initial accuracy and decreases the probability the framework is either current or used a year later.
The pattern underneath all three
All three failures share a design error: they optimize for the artifact instead of the decision. The framework becomes the deliverable, and whether anyone can make a better staffing, hiring, or development call because of it never enters the design.
The sections that follow describe the choices that reverse this, and they concern sequencing and governance more than software choice.
How to build skills frameworks that works in financial services
The organizations that get this right don’t have more budget or better consultants. They make a few different choices early, and those choices compound.
Start with a use case, not a complete taxonomy
The instinct is to map every skill in the enterprise before doing anything useful with any of them. Resist it. Completeness is a trap that keeps frameworks in development for a year and irrelevant on arrival.
The organizations that succeed pick one question that genuinely matters and build the minimum skills structure needed to answer it. Something concrete and pressing:
- Can we staff this regulatory remediation program from internal talent, or are we about to pay a contractor premium for skills we already have?
- Do we actually know which advisors are certified for the products they’re currently selling?
- If a key person on the model validation team leaves tomorrow, who covers the gap?
A framework that answers one of these earns the mandate to expand, and expansion from a working core is faster than launch from a complete design.
Involve business lines early as co-authors
Adoption of a skills framework correlates with authorship: definitions the business helped write are definitions the business recognizes and applies, while definitions produced outside it default to the non-adoption pattern described in the previous section.
Co-authorship means the people accountable for the work shape the skill definitions during design. The relevant contributors are those close enough to a function to know what competence in it actually consists of, at whatever level of the organization that knowledge sits.
The cost profile of this approach is front-loaded. Joint definition takes longer than expert drafting, and repays the difference over the framework’s life in adoption that does not need to be driven and definitions that do not need to be relitigated.
Co-authorship is also the stage at which semantic ambiguity is resolved. Terms that carry different meanings across functions surface as conflicts when the functions define them jointly, and each meaning gets recorded as a distinct skill. Ambiguity handled this way becomes structure; ambiguity deferred gets built into the taxonomy and discovered later as unreliable search results and distrusted data.
Build for governance before breadth
In a regulated sector, a framework’s value depends on whether it can serve as evidence, and evidentiary value comes from governance designed in at the start. Before expanding coverage, settle the questions that determine whether anyone can rely on an answer the framework gives:
- Who owns each skill definition and is accountable for keeping it current?
- How often is each one reviewed, and what triggers an off-cycle update?
- How is a skill assessment validated, rather than simply self-asserted by the employee?
These are the questions a regulator’s request will eventually test. A framework that cannot answer them holds opinions; one that can holds evidence and the distinction is set at design time, not at audit time.
Use skills technology to maintain consistency over time
Defining skills once is achievable in a project; keeping definitions stable across business units for years is an operating burden, and it is the point where most frameworks degrade.
Technology does this work best: it maintains a shared skills language across the organization, connects data scattered across core HR, learning, and compliance systems into a single, up-to-date view, and surfaces gaps against the questions the framework set out to answer.
This is also where the build-versus-maintain economics have shifted. Platforms designed for skills rather than retrofitted to them now carry the definitional layer as a maintained asset. Nestor, for example, runs on a structured library of more than 20,000 skills and competencies mapped to job roles, with AI flagging emerging and obsolete skills, so definitions update with the market instead of freezing at launch.
The technology’s function is not to replace judgment about what a role requires. It is to protect the consistency that judgment depends on after launch attention has moved elsewhere.
How Nestor supports skills frameworks in financial services
Everything above is achievable without any particular platform. But the four principles are far easier to sustain when the technology underneath is built for skills rather than retrofitted to handle them.
That’s the gap Nestor is designed to close.
One skills language, across every business unit
The semantic problem (one term, three meanings) does not stay solved by defining terms once; it returns with every new hire, team, and region. Nestor holds the definitional layer steady:
- A structured skills library mapped to job roles, so a skill carries the same definition everywhere
- Skills profiles built from multi-perspective evaluations (manager, peer, and self) rather than self-assessment alone, which is the difference between recorded opinion and usable evidence
- One shared language that new units inherit instead of reinterpreting
That second point answers the governance question directly: Section 4 explains how organizations validate assessments instead of relying on self-assertion, while multi-perspective evaluation puts that requirement into practice.
A usable framework in weeks, not quarters
One reason frameworks stall is the sheer time it takes to get from nothing to something usable.
Nestor maps skills to job roles in under three weeks, drawing on employee skills profiles and multi-perspective evaluations rather than a single self-assessment.
For a regulated organization that needs a current, structured picture of workforce capability, the kind of record you want behind you when you have to demonstrate competence, that speed is the difference between a framework that ships and one that stalls in design.
Evidence a regulated firm can stand behind
For the compliance and risk stakeholders in the room, the platform itself has to clear the bar its data will be used to meet. Nestor is SOC 2 Type II certified and GDPR-compliant.
It also connects to the systems of record already in place: Workday, SAP, ADP, and Personio among the HRIS integrations, alongside LMS connections, so the fragmented-data problem from Section 2 resolves into one current picture rather than another disconnected tool.
A starting point that matches the problem
Nestor’s solutions are modular, so you don’t have to commit to everything at once. You can begin with the single question that’s most pressing and expand outward as it proves its value:
- A regulatory remediation staffing gap
- An internal mobility or critical-role coverage risk
- A skills-gap analysis for one business line
Financial services firms are already using Nestor and G2 reviewers, including users in financial services, consistently cite customer support as a strength, which matters because the difficult part is not buying a platform but embedding one. The most useful first step is a specific problem from your own organization, put in front of the product.
Final Thoughts on skills frameworks in financial services
The organizations that get this right won’t just satisfy a regulator or fill a few roles faster. They’ll build something most of their competitors lack: a current, trusted, genuinely shared picture of what their people can actually do.
Your organization is defining the skills it will depend on three years from now through trading desk conversations, compliance reviews, and the quiet decisions about what each role really requires. Often by people who have no idea they’re defining anything at all.
The only real question is whether you’ll be able to see it happening while there’s still time to shape it.
Frequently asked questions about skills frameworks
What is the difference between skills frameworks and competency frameworks?
Skills frameworks defines discrete, verifiable abilities; a competency framework bundles skills, knowledge, and behaviors into broader role-level expectations. The terms overlap in practice, but the distinction matters in regulated sectors: mobility decisions and regulatory evidence both require skill-level granularity that role-level competency statements cannot provide.
What should a skills framework include?
Four elements at minimum: skill definitions mapped to roles, proficiency levels with observable criteria, a verification method for each skill beyond self-assessment, and governance; a named owner and review cycle per definition. Skills frameworks that include the first two and omit the last two produce the drift and distrust described above.
What is an example of a skills framework?
The most widely adopted public example is SFIA, the Skills Framework for the Information Age. It defines technology skills across seven responsibility levels. No equivalent standard covers financial services work end to end. Firms therefore typically combine a structured commercial library with definitions co-authored by their own business lines.
How often should skills frameworks be updated?
There is no fixed standard; the defensible pattern is a scheduled review, commonly annual, plus defined triggers for off-cycle updates: a new product line, a regulatory change, a reorganization. A framework without update triggers dates itself silently, since each of those events invalidates part of it immediately.
Who owns the skills framework in a financial services firm?
Ownership is split by layer, not held by one function. HR or L&D typically administers the framework; business lines own the skill definitions for their functions; risk and compliance own the definitions carrying regulatory weight. What cannot be split is accountability per definition, each one needs a named owner.
How long does it take to build skills frameworks?
Weeks for a use-case-scoped framework on a purpose-built platform; several quarters for an enterprise-wide manual build — and the second approach correlates with the failure modes described above. The practical benchmark: a working core answering one real staffing question inside a quarter, expanded from there.
