A module that changes how your finance students work with AI — across the whole of finance, not just markets. They walk in curious and walk out able to operate a real AI workflow and stand behind the output.
AI can already do the analytical work — read the filing, draft the note, screen the universe, reconcile the ledger. What it cannot do is be accountable for being wrong. That gap is the career.
Industry has moved through analytics, data science, and machine learning and is now operating in generative and agentic AI. Most B-school curricula stop two stages behind — Excel modeling, a little Python, case studies from a decade ago. The student who can supervise an AI workflow and defend the decision is the one who gets hired.
This module closes that gap fast. It is whole-of-finance, hands-on, judgment-led, and grounded in Indian regulatory reality. Students operate and supervise real workflows in two domains — then stand up and defend their decisions.
The firms that recruit your students are deploying AI co-workers today. The signal a graduate brings to the interview room has changed.
“Tell me how you'd use AI to analyse our loan book for early-warning signals.” — your students should be able to answer that, and defend the answer.
The module is built on the part of the curriculum industry actually runs on today — taught in order, each move building on the last. No engineering from scratch: students read, run, configure, and supervise AI workflows.
The same scaffolding, run twice — proof the discipline is domain-independent. Both lanes are ready and taught by Anjan.
More domains on request. The same spine extends to Accounting & Controllership, Lending & Credit, and Treasury & Corporate Finance — available with a specialist co-faculty. Talk to me about your program's mix.
Every topic blends four modes: theory lectures, guided demos, DIY demos, and take-home work. Sessions teach the concept; labs put students at the keyboard operating the workflow themselves.
Running through all of it is the judgment layer — EGA: Evidence, Governance, Accountability. Observability, human-in-the-loop, auditability. This is the core thing the module certifies, and the reason an output can be trusted.
The module ends in a project viva: students defend their workflow and their decisions to a panel. A defensible signal of capability — not a folder of slides.
The module is structured and assessed, not just delivered. The certificate is co-brandable with your institution and is gated by a viva — a student earns it by standing behind their work.
GenAI → RAG → Agents, then one domain lane. Lectures teach; labs put students hands-on.
Take-home assignments build toward a domain project — a working, supervised AI workflow, not a deck.
A project viva gates the certificate. Co-brandable with your institution — a defensible signal of capability.
The module is designed to flex to your calendar. It can run as a concentrated on-campus block, or as a spread-out weekly schedule across a term — the content and the certification are identical; only the cadence changes.
A note on co-faculty. The two ready lanes — Investments & Wealth and Trading & Capital Markets — are taught directly by Anjan. Additional domain lanes (accounting, lending, treasury) are delivered with a specialist co-faculty who joins for the relevant sessions; details confirmed per engagement.
A practitioner-led module, taught on your campus, with a real, defended credential at the end. Whole-of-finance — not just markets.