AI Analyst

Crown Agents Bank is a vastly growing and regulated UK bank that connects emerging and frontier markets to the rest of the world, using FX and payments technology. We are transforming the way payments and FX move through emerging markets, reducing friction so that more money gets to those who need it. Emerging markets payments are usually challenging, expensive, unreliable and opaque.

Our solutions help fix these pain points. Ultimately, we connect traditionally hard-to-reach regions to global financial infrastructure, giving access to the best prices and the fastest, most reliable settlement.

FX and cross-border payments are often complex and expensive, especially when operating in emerging markets. Crown Agents Bank (CAB) wraps its deep and trusted relationships and strength of network around innovative digital capabilities, and cross-border transaction banking solutions to enable fintech, corporates, governments, development organisations and banks to move money to, from, and across often hard-to-reach markets.

Role Purpose

The AI Analyst is an early-career role for a high-potential graduate with a strong interest in AI and a desire to apply it to real business problems from day one.

The role sits within CAB’s AI Centre of Excellence (CoE) and will rotate across the breadth of CoE activity, including business analysis, AI adoption, prompt engineering, Power Platform automation, Copilot Studio agents, and data analysis.

Working under the guidance of the AI Implementation Lead and wider CoE team, the AI Analyst will contribute directly to the Bank’s AI delivery goals by helping progress the AI use case pipeline, supporting proof-of-concept and early delivery activities, and producing clear artefacts and updates that translate AI capability into practical business outcomes. The role is designed to build broad capability quickly while delivering tangible productivity, efficiency and insight benefits across the bank.

Role Responsibilities

Strategic & Commercial Alignment

• Contribute ideas to improve the value case and practical viability of proposed AI use cases (benefits, feasibility, scalability, adoption considerations).
• Help gather stakeholder feedback and shape POC scope to ensure use cases demonstrate measurable value and are deliverable within constraints.
• Stay informed on relevant AI and industry trends and share insights to help position CoE approaches and tooling choices appropriately.

Hands-On Delivery

• Build and deliver low-complexity automations and AI agents from the AI use case pipeline—starting with closely supervised work and progressing to increased independence as capability grows.
• Support solution delivery activities such as:
  • data preparation, basic analysis and quality checks
  • prompt iteration and testing
  • user testing, defect triage and iterative improvement
• Contribute to the shared accelerator/library by documenting components, prompts, reusable flows, patterns and “how-to” notes to enable reuse and handover.
• Assist with creation and maintenance of core CoE artefacts (e.g., use case library, playbooks, guidance and reporting content) to support scale and consistency.

Stakeholder Engagement & Communication

• Attend discovery sessions with business teams: capture requirements, confirm next steps, and follow up actions professionally.
• Act as a reliable point of contact for internal teams, maintaining clear and responsive communication.
• Prepare non-technical written updates and materials (briefings, slides, status reporting, short “what’s changed / what’s next” summaries).
• Support demos and workshops; over time, present prototypes and delivered solutions to end users and stakeholders to build confidence and adoption.

Risk, Quality & Compliance

• Follow the CoE’s quality approach (peer review, testing and any gating/approval steps) on all work products.
• Apply CAB standards under supervision, including AI Policy, GDPR, data classification and information security requirements.
• Conduct quality checks on POC and delivery artefacts to ensure accuracy, completeness and alignment to agreed requirements.
• Identify delivery risks (e.g., data issues, limitations, control gaps) and escalate early;…