SCMHRD · MBA Finance · Semester 3 Intensive

AI Finance
Mastery

A 6-hour one-day intensive on LLMs, Agents, Agentic AI & Enterprise AI — engineered for the SCMHRD Sem 3 Finance batch. Post-lunch, students ship four live builds: Trading Algos, Earnings Dashboard, Portfolio Review, and Basel Report.

Curated with the help of Dr. Dipasha Sharma · Faculty, SCMHRD
6 Hrs
Duration
2
Modules
12
Lessons
4
Live Builds
SCMHRD Campus · Hinjewadi, Pune

Where the next generation of finance leaders is built.

SCMHRD Campus — Hinjewadi, Pune
Ranked Top B-School · GHRDC 2025
SCMHRD Auditorium
SCMHRD Auditorium · Guest Lectures & Seminars
SCMHRD MBA Finance Cohort
MBA Finance Cohort · SCEW Session
Curriculum Map

Designed for the Sem 3 Finance Batch

Every module maps directly to SCMHRD Semester 2 + Semester 3 Finance subjects — so AI augments, not replaces, your core knowledge.

Sem 2
Security Analysis & PM
Markowitz → ML factor models
Sem 2
Financial Modelling
DCF → Agent-driven valuation
Sem 2
Corporate Finance
Capital structure with LLM advisor
Sem 2
Financial Derivatives
Greeks + Monte Carlo agents
Sem 2
Banking & Insurance
RAG over policy + product docs
Sem 2
Fixed Income
Yield curves + duration bots
Sem 3
Risk Management
Basel IV · FRTB · SA-CVA
Sem 3
Wealth Management
IPS auto-generator + tax planner
Sem 3
Equity Research
Earning dashboards + memos
Sem 3
M&A and Restructuring
Synergy models + deal screen
Sem 3
Treasury & Forex
FX agents + hedge optimisation
Sem 3
Behavioural Finance
FinBERT sentiment + signals
Industry Signal

What Top Banks Are Building Now

The same systems you'll learn to build are being deployed at scale across global finance — right now.

🏦
JPMorgan Chase
$17B AI Spend · 2025

LLM Suite live for 200K+ employees. IndexGPT for research. AI-driven trading desks across FICC.

📊
Morgan Stanley
RAG Advisor Pipeline

GPT-4 over 100K+ wealth documents — advisors retrieve compliance-approved insights in seconds.

💼
Goldman Sachs
Agent Pilots Live

Marcus AI for retail. Internal coder-agents for the trading floor. 95% PE adoption forecast by 2026.

🌐
Global MNC Banks
Basel IV · FRTB Stack

Citi, Deutsche, UBS — SA-FRTB capital, SA-CVA, Pillar 3 disclosures all moving to agentic pipelines.

🇮🇳
Kotak · HDFC · Axis
India AI Banking

Wealth bots, KYC automation, Hindi/regional FinBERT, and credit underwriting agents in production.

📡
Bloomberg / Refinitiv
BloombergGPT · 50B

Domain-specialised LLM trained on 363B finance tokens — the future of terminal workflows.

One-Day Intensive · 10 AM – 5 PM

Two Modules. One Transformative Day.

Pre-lunch foundations on LLMs, Agents & Enterprise AI. Post-lunch — four live Python builds: Trading Algos, Earnings Dashboard, Portfolio Review, and Basel Report.

0110 AM – 1 PM
Pre-Lunch · 3 Hours · Foundations
Introduction to LLMs, Agents & Enterprise AI
Build the conceptual foundation: how Large Language Models work, what makes an Agent different from a chatbot, how Agentic AI plans & acts, and how Enterprise AI is deployed inside banks. Closes with hands-on Prompt Engineering for finance.
1.1 Introduction to LLMs · transformers · GPT-4 · Claude · BloombergGPT
1.2 Agents — tool use, function calling, memory
1.3 Agentic AI — planner · executor · critic loops
1.4 Enterprise AI — security, RAG, MCP, governance in banks
1.5 Prompt Engineering for finance — patterns & anti-patterns
1.6 Live demo: equity-analyst agent end-to-end
022 PM – 5 PM
Post-Lunch · 3 Hours · Build & Apply
Finance Applications in AI
Four production-grade Python builds, then a live group case study and an AI-Finance interview round. Each build maps to a real desk at a top-tier bank; both assignments are graded for SCMHRD internal evaluation.
2.1 Trading Algo System — Python codes (momentum, mean-reversion, signal stack)
2.2 Backtesting engine — Sharpe, drawdown, turnover
2.3 Earnings Dashboard — yfinance + Claude + Streamlit
2.4 Portfolio Review bot — allocation, factor exposure, rebalancing memo
2.5 Basel IV / FRTB Report — SA-FRTB capital, SA-CVA, Pillar 3
2.6 Group Case Study + Interview Round (graded — see Assessment)
📝 Graded Group Assessment · 50 marks
30
marks
Live Case Study
Group analysis of a real AI-in-Finance scenario (e.g. JPMorgan IndexGPT roll-out, Morgan Stanley advisor RAG, Basel IV transition at an MNC bank). Submit memo + 5-min pitch.
20
marks
Interview Questions
Group response to a panel of AI-in-Finance interview questions (LLM intuition, RAG vs fine-tune, FRTB, Python case, ethics). Scored on accuracy + clarity.
8 Live Builds

AI Projects That Get You Hired

Every project ships as a working Python repo + PDF report — portfolio-ready for placements.

🏛️
Basel IV / FRTB Report Bot
Automated SA-FRTB capital, SA-CVA, Pillar 3 disclosures + CET1 dashboard for an MNC bank book.
FRMRiskRegulatory
📈
Earning Dashboard Agent
Streamlit + Claude bot — pulls yfinance data, runs ratios, writes the earnings memo. End-to-end.
EquityAgentStreamlit
💎
Wealth Report Generator
Client profile → IPS, allocation, SIP plan, tax optimisation. Auto-PDF in under 60 seconds.
WealthPDFIPS
🧠
FinBERT Sentiment Engine
Real-time news → bull/bear scoring → factor signal feeding a long-short portfolio model.
NLPFinBERT
📊
Monte Carlo VaR / CVaR
10K-path simulation engine producing CRO-grade tail-risk reports with stress scenarios.
Market RiskVaR
🤝
M&A Synergy Modeller
Multi-agent deal screen — accretion/dilution, control premium, synergy buckets, fairness memo.
IBM&A
💱
Treasury / FX Hedge Bot
Live FX, exposure aggregation, optimal forwards/options hedge recommendation with P&L impact.
TreasuryFX
📚
Equity Research RAG
RAG over annual reports + concalls + filings — answers any question with citations, like an analyst.
RAGEquity
Take-Home Toolkit

Finance Prompt Starter Kit

Six institutional-grade prompts, worked on a real company — Infosys (NSE: INFY) — so you can paste and run them today. Swap the name for any company you're covering. Click a card and the full prompt lands on your clipboard.

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🤖 Agentic AI Research Analyst
ROLE: You are a senior sell-side IT-services analyst at a top-tier investment bank, known for conservative, evidence-first work. Coverage: Infosys Ltd (NSE: INFY, BSE: 500209).

INPUTS: attached are Infosys' FY25 Integrated Annual Report and the Q4 FY25 and Q1 FY26 earnings-call transcripts. Use ONLY these documents plus any market data I paste below — do not use memorised figures, even for well-known numbers.

PROCESS: (1) Plan first — list the sections you will produce before writing anything. (2) Extract: revenue (reported and constant-currency growth), operating margin, net profit, FCF and FCF conversion, large-deal TCV, attrition, headcount — for FY23–FY25, citing the page for each figure. (3) Value three ways: DCF (state your WACC build-up, terminal growth and why they fit an Indian IT major); EV/EBIT versus TCS, HCLTech and Wipro; P/E versus Infosys' own 5-year band. (4) Read both transcripts for guidance changes and management tone on GenAI-led deals — quote the exact sentences. (5) Re-check every calculation line by line before finalising.

OUTPUT (in this order): a 3-line investment thesis; a financial summary table; the valuation triangulation with a football-field range in ₹; the top 5 risks ranked by impact (include client-concentration and GenAI cannibalisation of time-and-materials revenue); BUY / HOLD / SELL with a 12-month target and the three assumptions that would break the call.

GUARDRAILS: cite page/section for every number; if a figure is not in the documents, write "not disclosed" — never estimate silently; separate fact from inference; end with your confidence (high / medium / low) and why. This is an internal draft — it is not investment advice.
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📈 Earning Dashboard Generator
ROLE: You are a Python engineer pair-programming with a finance MBA who cannot code — explain what you build as you go, in plain English.

TASK: Build a single-file Streamlit dashboard (app.py) for Infosys Ltd, yfinance ticker "INFY.NS" (fallback: ADR "INFY" if NSE data is unavailable — say so on-screen when the fallback is used).

DATA: pull 5 fiscal years via yfinance: total revenue, EBITDA (or operating income if EBITDA is missing), net income, free cash flow, total debt, cash & equivalents. IT-services extras where available: operating margin and dividend history. Handle every missing field gracefully — show "n/a", never crash. Note on-screen that yfinance reports Infosys in USD for the ADR and ₹ crore for INFY.NS — label the currency on every figure.

COMPUTE: YoY growth per line; EBITDA/operating and net margins; ROE and ROCE (display the formulas on-screen); net cash position (Infosys is debt-light — show net cash rather than net-debt/EBITDA when debt ≈ 0).

UI: (1) header with name, live price, 52-week range and market cap; (2) four KPI cards — revenue growth, operating margin, ROE, net cash; (3) revenue & margin trend chart; (4) FCF vs net income bar chart (quality-of-earnings view); (5) a "Quarter in 200 words" memo generated strictly from the computed numbers — flag any metric that moved >10% YoY with a one-line driver only if visible in the data, otherwise write "driver not visible in data".

QUALITY: comment every block in plain English; list pip requirements; end with exact run instructions (pip install streamlit yfinance plotly, then streamlit run app.py).

GUARDRAILS: the memo may only reference numbers shown on the dashboard — no invented drivers, no price targets, no recommendations.
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🏛️ Basel IV FRTB Report
ROLE: You are a market-risk officer at the Mumbai trading desk of an international bank, preparing a management-level SA-FRTB pack. Basel text excerpts (MAR21) are attached for reference.

SAMPLE TRADING BOOK (use exactly these positions):
• GIRR: long 10Y G-Sec, DV01 ₹2.5 crore; payer position in 5Y INR OIS, DV01 ₹1.1 crore
• FX: long USD/INR forwards, notional USD 500 million, 3-month tenor
• Equity: long Nifty 50 futures, delta ₹800 crore; short single-stock futures on INFY, delta ₹120 crore
• CSR: 5Y AAA corporate bond book, credit-spread DV01 ₹0.6 crore
• Commodity: long gold futures (MCX), delta ₹150 crore
• Vega: short 3-month Nifty options, vega ₹4 crore per vol point

TASK: (1) Compute the SA-FRTB charge per risk class — delta, vega and curvature separately — showing at each step the sensitivity, the risk weight applied and the correlation treatment, citing the MAR21 paragraph relied on. (2) Aggregate across buckets under the low / medium / high correlation scenarios and take the maximum. (3) State which positions would additionally attract SA-CVA if traded OTC, and what counterparty data you would need. (4) Deliver: a Pillar 3-style disclosure table in ₹ crore; the capital charge as a % of a stated CET1 base of ₹40,000 crore; a 5-bullet management summary in plain English, including which single position drives the most capital and one hedge that would reduce it.

GUARDRAILS: every treatment must cite its MAR21 paragraph from the attached text; where the sample book lacks an input (e.g. curvature shocks need full revaluation), list it under "Data gaps" and continue — do not assume values silently; label every assumption; show workings, not just results.
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💎 Wealth Management IPS
ROLE: You are a SEBI-registered investment adviser and certified financial planner drafting a formal Investment Policy Statement.

SAMPLE CLIENT (use exactly this profile): Ananya Kulkarni, 28, single, no dependants; MBA (Finance) working at a global bank's Pune GCC; gross income ₹26 LPA, new tax regime (30% slab); existing corpus: ₹5 lakh in fixed deposits + ₹3 lakh in a large-cap mutual fund; monthly investible surplus ₹60,000; employer NPS available.
GOALS: (G1) emergency fund of 6 months' expenses (₹4.2 lakh) within 12 months; (G2) home down-payment of ₹40 lakh in Pune by 2033; (G3) retirement corpus at age 55. Stated risk comfort: moderate — accepted a 20% notional drawdown in the risk questionnaire but has never experienced a bear market.

PRODUCE, as numbered IPS sections: (1) objectives & constraints — liquidity, horizon, tax, legal; (2) risk-profile assessment — reconcile the questionnaire score with her lack of bear-market experience; justify, don't restate; (3) strategic asset allocation with % bands (equity / debt / gold / international) per goal horizon; (4) instrument shortlist by CATEGORY (index funds, flexi-cap MFs, gilt & corporate-debt funds, SGBs) with selection criteria — categories, not scheme names; (5) a month-wise SIP plan splitting the ₹60,000 across G1–G3, with every expected-return assumption stated; (6) tax notes — new vs old regime impact on 80C/ELSS, NPS 80CCD(1B), current equity LTCG rules — citing the section; (7) rebalancing bands and annual review triggers; (8) risks and disclaimers.

GUARDRAILS: no guaranteed-return language anywhere; every return figure is labelled as an assumption with its basis; flag anything requiring a human adviser's sign-off; verify tax rules against the current Finance Act — if unsure, say "confirm with a tax adviser" rather than assert. Format as a formal, client-ready document.
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📚 RAG over Annual Report
ROLE: You are a research assistant answering ONLY from the document excerpts provided in this conversation — Infosys' FY25 Integrated Annual Report (the free PDF from infosys.com → Investors).

QUESTION: "What did Infosys disclose in FY25 about (a) revenue from generative-AI-led engagements, (b) voluntary attrition, and (c) the FY26 revenue-growth guidance? Answer each part separately."

RULES: (1) Use only the supplied excerpts — no outside knowledge and no memory of Infosys, even where you are confident; your memorised attrition number may be from the wrong year. (2) For any part the excerpts do not answer, reply exactly: "Not supported by the provided document", then name the report section that would likely contain it (e.g. Management Discussion & Analysis, CEO letter, Risk Management). (3) If two passages conflict — e.g. standalone vs consolidated figures, or IT-services attrition vs group attrition — present both with citations and flag the conflict; do not silently choose one.

OUTPUT FORMAT, per part (a)/(b)/(c): Answer (2–4 sentences) → Evidence (verbatim quote) → Citation (page and section) → Confidence: high / medium / low with one line on why. End with: Missing — anything relevant the document does not cover.

GUARDRAILS: every number in your answer must appear verbatim in the quoted evidence; no arithmetic on retrieved numbers unless you show the calculation and label the result "computed, not quoted"; if guidance is given in constant currency, say so explicitly.
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🎯 Interview Prep — AI in Finance
ROLE: Act as a demanding hiring manager at JPMorgan's Mumbai corporate centre interviewing me for an "AI in Finance" analyst role on the equity-research enablement team. Stay in character until I write "END INTERVIEW".

CONTEXT YOU MAY PROBE: my course project — a RAG question-answering tool over Infosys' FY25 annual report that answers with page-level citations, refuses questions the document can't support, and was tested against 25 known Q&As.

FORMAT: exactly 5 questions, asked ONE at a time — wait for my answer before continuing. Ramp the difficulty: (1) explain to a portfolio manager, in 90 seconds, how an LLM produces an answer and why it can be confidently wrong; (2) for our research library, argue RAG vs fine-tuning — cost, freshness, auditability — and pick one; (3) my Infosys tool cited "page 83" for an attrition number that was actually on page 41 — name this failure type and give two controls that would catch it; (4) mini case: design an agentic workflow that produces a first-cut earnings note for INFY within one hour of results hitting the exchange — probe my tools, checkpoints, loop-stop rules and where a human signs off; (5) governance: which evals, thresholds and sign-offs I would demand before this goes live under the bank's model-risk policy.

AFTER EACH ANSWER: score me 1–5 on accuracy, structure and depth; give the model answer in 5 lines; ask one sharp follow-up if my answer was vague.

AT THE END: a report card — overall score, my three biggest gaps, a 2-week study plan targeting them, and the single line I should say about my project in the real interview.

TONE: professional and exacting. No flattery, no encouragement padding.
👤 Your Instructor

Not a visiting lecturer. The architect.

Built the systems. Now teaches them.

"I spent 12 years building the exact systems students will learn — from live trading desks to fund automation at institutional level. This course is not theory. It's a practitioner's field guide."
Tejas Jadhav, CFA, FRM is a finance professional with 12+ years spanning wealth management, market risk, climate risk technology, and banking at Citi, HDFC Bank, and leading Swiss institutions. He holds an MMS from SIMSREE (Sydenham Institute of Management & Research), Mumbai. In the MBA entrance exams, he scored 99.92 percentile in CMAT and an unprecedented 800/800 in MAT for 4 consecutive attempts.
As course architect for Agentic AI & Advanced Analytics in Finance at ISBMS Pune, he designed curriculum benchmarked against Wharton, Harvard, and LBS. Author of "Claude AI for Finance Professionals" — an Amazon bestseller used by analysts across India and the US.
📘
Claude AI for Finance Professionals
Amazon · AI in Finance Series
Amazon Bestseller
  • 🏦
    Market Risk & FRTB
    Built FRTB Basel IV market-risk solutions at institutional level — Citi & Swiss Banking.
  • 🌿
    ESG & Climate Risk Technology
    Designed AI-powered ESG and climate risk platforms for institutional finance.
  • 💼
    Wealth Management
    Managed HNI portfolios — equities, structured products, fixed income, direct P&L accountability.
  • 🤖
    AI Finance Author & Educator
    Bestselling books · course architect at ISBMS · teaching MBA Finance at SCMHRD & beyond.

The Finance of Tomorrow
Starts Today.

Six hours. Three modules. Eight builds. One transformation for your Sem 3 placement season.

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