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Banking & FS AI curriculum — credit, risk, service, compliance▌

Vocabulary matches RBI/SEBI-style discussions without offering legal advice: we teach operating patterns for accountable documentation and human review where models touch regulated decisions.

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instructional design: bloom’s taxonomy + measurable outcomes

Every module maps to explicit learning outcomes—not open-ended discussion without deliverables. We sequence along Bloom’s taxonomy (remember → understand → apply → analyze → evaluate → create): definitions and guardrails first, then applied exercises, then measurement and approvals. Facilitators run short checks for understanding after each block (2026 materials).

For organic and generative-engine visibility (GEO), we mirror patterns associated with stronger AI-search citation: answer-first sections, statistics where available, authoritative tone, clear H1–H3 structure, comparison tables when they reduce ambiguity, and FAQ blocks intended to pair with FAQPage JSON-LD. Teams produce briefs, scorecards, and checklists—not a generic “AI creativity” workshop.

program objectives

  • Map AI features to existing risk taxonomy (credit, market conduct, operational) with clear ownership.
  • Design pilot scopes that satisfy model risk and IT security gates common in banks.
  • Practice escalation when model drift or vendor incidents affect customer-facing journeys.
  • Pair executive sponsors with practitioner exercises that mirror servicing and ops realities—not generic chat demos.

how we deliver

  1. 1

    Discovery call & problem framing

    We align on sponsors, success metrics, and constraints (2026 tool landscape, data rules, procurement gates) before anything is scheduled company-wide.

  2. 2

    Stakeholder interviews & day-in-the-life context

    Short conversations with practitioners (not only leadership) so scenarios reflect real workflows—not generic slide demos.

  3. 3

    Curriculum design & artifacts

    Modular agenda, exercise scripts, evaluation rubrics, and governance checkpoints matched to your vocabulary (banking, FMCG, engineering, etc.).

  4. 4

    Engaged, hands-on delivery

    Facilitation-led sessions with live exercises, breakout prompts, and documented failure modes—minimum passive lecture time.

  5. 5

    Post-session support: documentation & next steps

    Written recap, pilot backlog, links to explainx.ai courses for scaled upskilling, and optional office hours so momentum doesn’t stop at the workshop.

modules

BFSI use-case patterns & control mapping

From RFP to internal memo: how to describe controls without overclaiming.

session outline

  • Representative workflows: origination assistance, servicing summaries, wealth advisory drafting aids.
  • Where retrieval must be grounded vs. where summarization is sufficient.
  • Data classes & residency: customer PII, employee data, and vendor subprocessors.

labs

  • Draft a one-page pilot brief with explicit rollback conditions.

beyond-catalog topics (custom)

  • Credit model vs. LLM orchestration separation patterns often implemented in hybrid architectures.
  • Vendor due diligence questionnaires tailored for Asia-Pacific deployments.

Human oversight & audit trail habits

Behavioral, not checkbox.

session outline

  • Sampling strategies for QA reviewers when throughput is high.
  • Versioned prompt libraries and change control expectations.

labs

  • Peer review queue simulation with deliberate ‘bad’ outputs.

beyond-catalog topics (custom)

  • Alignment with internal audit timelines for new GenAI features at different materiality levels.

quick contact

Scope or pilot this curriculum

Share sponsor, headcount, and cities — we reply with timing and options. Rough budget helps us match the right depth.

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faq

Do you advise on RBI / specific rule text?

We facilitate with your compliance partners; we don’t provide regulatory legal opinions.

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