AI Business Analyst
91% skill overlap
To add: Applied generative AI in office work (prompting, evaluation, governance)
Your role
AI Transformation Analyst (Banking)
Full profileYour role
- L3 salary
- Not published yet
- Upcoming dated changes
- 8 changes ahead
What the role does
An AI transformation analyst in banking finds, designs and delivers uses of artificial intelligence in banking processes, such as document extraction, client service assistants, operations automation and analytics built on large language models. The role gathers requirements, maps current processes, defines success metrics, works with data scientists and engineers on prototypes, and supports controlled rollout with risk, compliance and model governance teams. It sits in transformation, operations change or data and AI teams.
Read a day in the life
A day in the life
The morning may start with a backlog review: which AI use cases are in discovery, which are being tested and which are waiting for risk approval. The analyst then meets an operations team to understand a manual process, for example reading confirmations or answering repetitive client emails. Together they map the steps, volumes, error rates and the data each step uses. That becomes a problem statement with metrics such as time saved and accuracy needed. After lunch the analyst works with a data scientist to test a prototype on a sample of documents, checking where the model is wrong and why. Results go into a short evaluation note. Later there may be a meeting with model risk or compliance to go through the controls: human review steps, data protection, logging and fallback. The day ends with updates to requirements, user stories and the project plan, and sometimes a demo for business sponsors.
Levels
- L1 Starting out
- L2 Independent
- L3 Senior or specialist
- L4 Leads a team or an area
- L5 Leads the function
Full level descriptions and job titles
The EFO level names are the common scale; job titles differ between employers.
| Level | What the work looks like | Typical years | Typical job titles |
|---|---|---|---|
| L1 Starting out | Documents processes and requirements, prepares test data and evaluation results, maintains backlogs and learns AI tools, banking processes and governance rules under supervision. | 0-2 | Banks: Analyst, Junior Analyst, Associate (asset servicing), Trainee, GraduateAsset servicers: Junior, Specialist (entry), Officer |
| L2 Independent | Owns discovery and requirements for a use case end to end, defines metrics, runs user testing with business teams and coaches new analysts on methods and tools. | 2-5 | Banks: Analyst / Senior Analyst, AssociateAsset servicers: Specialist, Senior Specialist, Senior Officer |
| L3 Senior or specialist | Shapes a portfolio of use cases, designs human review and control steps with risk teams, handles complex stakeholder trade-offs and represents the business in AI platform projects. | 5-9 | Banks: Senior Associate, AVP, Assistant ManagerAsset servicers: Senior Specialist, Expert, Team Lead (small team) |
| L4 Leads a team or an area | Leads an AI transformation team or programme, owns benefits, budget and delivery, ensures use cases meet model risk and compliance standards and is the escalation point for issues. | 8-14 | Banks: VP, Manager, Senior ManagerAsset servicers: Team Lead, Manager, Head of Section |
| L5 Leads the function | Accountable for AI adoption in a business line or region, sets priorities, standards and governance for AI use, and reports value and risk to senior management. | 12+ | Banks: Director, Executive Director, Managing Director, Head of functionAsset servicers: Head of Department, Director |
Skills
Where the skill weights come from
Each role lists its skills with a tier and a weight. The weights of one role add up to 100 percent.
Source: the EFO role catalogue, built from the ESCO classification and reviewed by the editors.
The percentage is the skill's share of the role; the bars are drawn relative to the anchor skill, which is shown full.
Critical
- Large language model applicationsWeight 13.3%Relevant to you
- Process mapping and redesignWeight 8.9%Relevant to you
- Product discovery and metricsWeight 8.9%Relevant to you
- Requirements gathering and documentationWeight 8.9%Relevant to you
- Trade lifecycleWeight 8.9%Relevant to you
Important and supporting skills 17
Important
- Change management and adoptionWeight 4.4%Relevant to you
- Experiment design and A/B testingWeight 4.4%Relevant to you
- Choosing the right metric for the questionWeight 4.4%Relevant to you
- Stakeholder managementWeight 4.4%Relevant to you
- Professional written communicationWeight 4.4%Relevant to you
- Business case and cost-benefit analysisWeight 4.4%Relevant to you
- Querying data with SQLWeight 4.4%Relevant to you
- Intellectual curiosityWeight 4.4%Relevant to you
Supporting
- Python for data analysisWeight 2.2%Relevant to you
- Machine learning fundamentalsWeight 2.2%Relevant to you
- Project tools (Jira, Asana)Weight 2.2%Relevant to you
- Data protection and privacy law (GDPR, CCPA, DPDP)Weight 2.2%Relevant to you
- Design judgment and trade-offsWeight 2.2%Relevant to you
- Communicating data to non-technical audiencesWeight 2.2%Relevant to you
Useful
- API design and integrationWeight 0.7%Relevant to you
- Visualisation tools (Power BI, Tableau)Weight 0.7%Relevant to you
- Verbal communication and presentingWeight 0.7%Relevant to you
Processes
The work the role takes part in.
- AI and automation
- Change and project delivery
- Securities settlement
- Trade capture and booking
- Trade confirmation and affirmation
What each process covers
- AI and automationMain
- Change and project deliveryMain
- Securities settlementMain
- Trade capture and bookingMain
- Trade confirmation and affirmationMain
Where the role concentrates
How AI is changing the role
This role exists because of AI, and its own tasks change fast: code assistants and model platforms let analysts build prototypes without large engineering teams, and requirements and documentation are drafted with generative tools.
Read the full assessment
This role exists because of AI, and its own tasks change fast: code assistants and model platforms let analysts build prototypes without large engineering teams, and requirements and documentation are drafted with generative tools. The World Economic Forum Future of Jobs Report 2025 names AI and big data as the fastest-growing skill. The European Banking Authority stresses that banks using AI need governance, explainability and data quality. Value sits in choosing problems worth solving, measuring real benefit, designing controls with risk and compliance teams under rules such as the EU AI Act, and helping people change how they work.
Dated changes for this role
- ESMA guidelines on allocations and confirmations for T+1Relevant to you
- CSDR settlement discipline rules updated for T+1 in three stepsRelevant to you
- ECB Pontes: DLT settlement in central bank moneyRelevant to you
- ECB digital euro pilot with 36 payment providersRelevant to you
- Switzerland and Liechtenstein move to T+1 with the EU and UKRelevant to you
Salary snapshot
Salary figures are not published yet
The L3 salary range for this role, with its sources, will appear here when the salaries section opens.
Certifications
- Microsoft Power BI Data Analyst PL-300
- Tableau Desktop Specialist
- SnowPro Core
- dbt Analytics Engineering
Concepts to know
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Do this job? Suggest a correction
Sources
Model riskModel risk is the potential for loss or poor decisions from models that are wrong in design or data, or used outside their purpose. Supervisors expect a model risk management framework covering inventory, tiering, independent validation, use limits and board oversight.
Explainability (AI)Explainability is the ability to describe, in terms a person can understand, why a model produced a given output, for example which factors drove a credit decline. It supports validation, human oversight, customer explanations under the GDPR and AI Act transparency duties.
AI Act (Artificial Intelligence Act)The AI Act, Regulation (EU) 2024/1689, is the EU law that sets rules for AI systems according to their risk: some practices are banned, high-risk systems face strict requirements, and general-purpose AI models have transparency duties. Credit scoring of individuals is a high-risk use.