New service line — Institutional Workshops & Faculty Development Programmes. Now accepting engagements for the 2026–27 academic year and FY27 corporate L&D calendars.
Institutional Workshops & Faculty Development Programmes
Ten Excel-based programme tracks in derivatives, valuation, forecasting, volatility, portfolio optimisation, fixed income, credit risk and Basel — delivered on campus or online for business schools, universities, banks and financial institutions. Every model is built from a blank worksheet, on real-time market data, in front of the room. Nothing is handed out pre-built.
10Programme tracks
3Formats — 1 day to 12 weeks
28+Yrs HSBC Global Banking & Markets
100%Excel-first, hands-on
4Institutions currently taught at
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Why This Is Different
Most quantitative finance training is either theoretically rigorous but unimplementable, or software-driven and opaque. These programmes take the third path: participants build every model themselves in Excel — cell by cell, on live market data — so the mathematics stays visible and the intuition survives the session.
Built, not shown
Method
No pre-baked templates handed out at the end. Bootstrapped zero curves, binomial lattices, GARCH likelihood surfaces and efficient frontiers are constructed live from an empty sheet, with every intermediate step auditable.
Regulator-current
Content
Basel III/IV endgame, FRTB, IRRBB, LCR/NSFR, IFRS 9 / Ind AS 109 ECL and ICAAP presented as they are actually implemented under supervisory scrutiny — not as summarised in a textbook.
Who We Work With — Two Engagement Tracks
Academic and industry audiences need the same mathematics but a different centre of gravity. The content is common; the cases, datasets and assessment are not.
Academic Track
B-Schools · Universities · Autonomous Colleges
- Student Workshops — applied electives and skill-bridge modules for MBA/PGDM Finance, M.Com, MSc Economics, BBA and integrated programmes.
- Faculty Development Programmes — 1-week (30 hr) and 2-week (60 hr) FDPs with pedagogy transfer, teaching notes, question banks and reusable datasets.
- Certification Bridge — CFA Level I–III, FRM Part I & II and Actuarial CM2/CB2 topic alignment, mapped module by module.
- Curriculum Support — designing or accrediting a Financial Engineering / Risk Analytics elective, including lab specification and assessment rubrics.
- Placement Readiness — Excel modelling tests, case interviews and live-market capstones that hiring panels actually set.
MBA / PGDM
FDP
NAAC / NBA evidence
Co-branded certificates
Industry Track
Banks · NBFCs · Insurers · AMCs · Fintechs
- Treasury & ALM — IRRBB, EVE and NII sensitivity, gap analysis, FTP, liquidity coverage, behavioural modelling of non-maturity deposits.
- Risk & Middle Office — VaR/ES, backtesting, FRTB standardised and internal models, stress testing, RAROC.
- Credit Risk & Modelling Units — PD/LGD/EAD estimation, scorecard build, Merton/KMV structural models, IFRS 9 ECL staging.
- Analyst Onboarding — a structured 4–8 week ramp for graduates joining markets, risk or model-validation desks.
- Model Validation Literacy — building second-line capability to challenge a model rather than merely re-run it.
Treasury / ALM
Risk & Validation
Own-data option
CPD-style hours
Professional bodies — chapter-level workshops for CFA Society, GARP, ICAI, IIBF and CA/CS study circles are delivered under the same catalogue, typically in the 1-day Masterclass format.
Delivery Formats — Choose by Calendar, Not by Ambition
Every one of the ten tracks below is available in all three formats. The topic list narrows or widens; the build-it-yourself method does not change.
| Format | Duration | Contact Hours | Best Suited To | Deliverable |
|---|---|---|---|---|
| Masterclass Single topic, single day |
1 day | 6 hours | Slotting into an existing academic calendar; a departmental seminar; a risk-team half-year offsite. Covers the core of one track at working depth. | Annotated workbook + participation certificate |
| Intensive Workshop Flagship format |
2–3 days | 12–18 hours | Full-track coverage from foundations to advanced applications. The standard FDP and corporate workshop format. Cohorts of 25–60. | Model suite + graded case + certificate |
| Certificate Programme Weekend or evening cohort |
6–12 weeks | 24–40 hours | Institution-wide capability building, credit-bearing electives, or a structured analyst academy. Two or three tracks can be combined into a pathway. | Capstone defence + co-branded certificate |
Modes: on-campus (preferred for workshop formats) · live online via Google Meet · hybrid.
Cohort size: 25–60 for hands-on formats; larger for lecture-mode masterclasses.
Prerequisite: one laptop per participant with Microsoft Excel (Analysis ToolPak and Solver enabled) and internet access for live data pulls. No licensed statistical software and no market terminal required. Python extensions optional throughout.
Cohort size: 25–60 for hands-on formats; larger for lecture-mode masterclasses.
Prerequisite: one laptop per participant with Microsoft Excel (Analysis ToolPak and Solver enabled) and internet access for live data pulls. No licensed statistical software and no market terminal required. Python extensions optional throughout.
Programme Catalogue — Ten Tracks Across Four Pillars
Tracks are modular. A three-day FDP typically takes one track end to end; a certificate programme combines two or three along a coherent pathway.
Pillar I — Derivatives & Markets 2 tracks
Track 01 — Financial Derivatives: Foundations to Advanced
1-day · 2–3 day · 10-week
CFA L1/L2 · FRM Part I
NSE · CME live data
Python optional
The full arc from contract mechanics to Greeks-based risk management — every payoff priced and every hedge sized in Excel on live index, stock and commodity data.
- Forwards, futures, margining, marking to market; the cost-of-carry model with dividends, storage and convenience yield
- Hedging with futures — minimum-variance hedge ratio, cross-hedging, tailing the hedge, basis risk, rolling the hedge
- Index and stock futures; calendar and inter-commodity spreads; arbitrage bounds
- Option payoffs, moneyness, put–call parity, upper and lower bounds
- Binomial option pricing — one-step to n-step CRR lattices, risk-neutral probabilities, American exercise, dividends, convergence to Black–Scholes
- Black–Scholes–Merton — assumptions, derivation intuition, full Excel implementation, dividend adjustment, implied volatility by Solver / Newton–Raphson
- Option strategies — covered call, protective put, bull/bear spreads, straddle, strangle, butterfly, condor, collar, ratio and calendar spreads, with live payoff diagrams and break-even analysis
- The Greeks — delta, gamma, vega, theta, rho; delta-hedging simulation, gamma scalping, P&L attribution across a rebalancing schedule
- Volatility smile and skew; introduction to exotics and structured payoffs
Track 02 — Interest Rate Derivatives & Swaps
1-day · 2–3 day · 8-week
CFA L2/L3 · FRM Part II
FBIL · CCIL · US Treasury
Treasury desks
Curve construction first, then everything priced off it — the sequence a rates desk actually follows.
- Money-market conventions, day counts, compounding bases; MIBOR / SOFR / repo mechanics and the LIBOR transition
- Bootstrapping the zero curve from deposits, futures and par swaps; discount factors, implied forward rates, interpolation choices
- Forward Rate Agreements — pricing, settlement mechanics, discounting the settlement amount, hedging a rollover exposure
- Interest rate futures — Eurodollar/SOFR futures, tick value, the convexity adjustment
- T-Bond futures — conversion factors, invoice price, cheapest-to-deliver determination, implied repo rate, quality and timing delivery options
- Interest Rate Swaps — mechanics, par swap rate derivation, building the swap curve
- Swap valuation — bond-differential method and FRA-strip method reconciled; mark-to-market of a seasoned swap; DV01 and duration of a swap
- OIS discounting and dual-curve valuation; CSA collateral and funding effects
- Currency swaps and cross-currency basis; caps, floors and swaptions priced with the Black model
- Hedging a swap book; asset-swap spreads
Pillar II — Valuation & Corporate Finance 2 tracks
Track 03 — Valuation: DCF and Relative Valuation
1-day · 2–3 day · 10-week
CFA L1/L2 · MBA Core
Live NSE/BSE filings
One disciplined framework applied across four asset classes, ending in a live listed-company valuation defended against the market price.
- Discounting from first principles; FCFF vs FCFE vs dividends — matching the cash flow to the right discount rate
- Cost of capital — CAPM, beta estimation and regression diagnostics, unlevering/relevering, country risk premium, synthetic credit rating, WACC build
- Equity valuation — DDM, Gordon growth, H-model, two- and three-stage FCFE, terminal value discipline and the implied-growth cross-check
- Firm valuation — full FCFF model, reinvestment rate, ROIC-driven growth, enterprise-to-equity bridge, treatment of leases, options and cross-holdings
- Bond valuation as a DCF special case; spread-implied value and credit-adjusted discounting
- Real estate — NOI build, direct capitalisation, cap-rate derivation, levered and unlevered IRR, DSCR, exit-cap sensitivity, development feasibility
- Relative valuation — P/E, PEG, EV/EBITDA, EV/EBIT, EV/Sales, P/B, EV/IC; the algebra behind each multiple and when it is the right one
- Peer-set construction and cleaning; cross-sectional regression of multiples on fundamentals
- Reconciling DCF and multiples; football-field presentation of the value range
- Sensitivity tables, scenario analysis and Monte Carlo overlay on the valuation output
Track 04 — Financial Modelling: Corporate Finance End to End
1-day · 3-day · 12-week
MBA · Analyst Onboarding
Live company financials
A complete, auditable three-statement model with the full corporate-finance decision layer built on top of it.
- Model architecture — inputs/calculations/outputs separation, naming conventions, formatting standards, error traps, documentation and version control
- Advanced Excel for modellers — XLOOKUP, INDEX-MATCH, OFFSET, SUMPRODUCT, dynamic arrays, data validation, Solver, Goal Seek, auditing tools
- Historical normalisation; revenue and cost driver builds; assumption discipline
- Three-statement model with full linkage, circularity resolution (interest–cash loop), and the balance-sheet check
- Supporting schedules — working capital, fixed assets and depreciation, debt with revolver and cash sweep, equity and share count
- Cost of capital; capital budgeting — NPV, IRR, MIRR, payback, discounted payback, profitability index, project ranking under capital rationing, real-options intuition
- Capital structure — operating and financial leverage, debt capacity, Modigliani–Miller with and without taxes, trade-off and pecking-order theory, optimal structure analysis
- Dividend policy, buyback modelling, and value-of-cash treatment
- M&A — accretion/dilution, synergy modelling, purchase price allocation, financing mix; LBO with sources & uses, debt schedule and returns waterfall
- Scenario Manager, one- and two-way data tables, Monte Carlo simulation, and a board-ready output dashboard
Pillar III — Quantitative Analytics & Forecasting 3 tracks
Track 05 — Forecasting with Time Series Models
1-day · 2–3 day · 8-week
MBA Analytics · FRM
RBI · FRED series
Classical decomposition through Box–Jenkins, with ACF/PACF read by eye and ARIMA estimated in Excel before any statistical package is touched.
- Time series components — trend, cycle, seasonality, irregular; additive vs multiplicative model selection
- Centred Moving Average (CMA) decomposition — ratio-to-moving-average, seasonal index construction and normalisation, deseasonalisation, trend fitting, reseasonalised forecast
- Smoothing methods — SMA, weighted MA, simple exponential smoothing, Holt's linear trend, Holt–Winters additive and multiplicative
- Stationarity — visual and statistical diagnosis, differencing, seasonal differencing, log and Box–Cox transforms, ADF unit-root test
- ACF and PACF — computing and plotting correlograms in Excel, confidence bands, and the identification rules that follow
- AR, MA, ARMA and ARIMA(p,d,q) — model identification, parameter estimation via Solver (conditional least squares / MLE), invertibility and stationarity conditions
- Diagnostic checking — residual ACF, Ljung–Box Q, normality; AIC/BIC/AICc model selection
- SARIMA (p,d,q)(P,D,Q)ₛ — seasonal identification, estimation, and the airline model as a worked case
- Forecast generation, prediction intervals, rolling-origin backtesting; MAPE, RMSE, MAE, MASE and Theil's U comparison
- Applications — sales and demand planning, inflation, FX, deposit growth and NII forecasting
Track 06 — Volatility Modelling & Forecasting
1-day · 2-day · 6-week
FRM Part I/II · Quant
NIFTY · India VIX
Conditional heteroskedasticity built from scratch — including maximum-likelihood estimation of GARCH parameters in a single Excel Solver run.
- Return construction, annualisation; stylised facts — volatility clustering, fat tails, leverage effect, aggregational Gaussianity
- Historical and rolling-window volatility; the window-length trade-off; the ghosting problem
- EWMA / RiskMetrics — recursive variance update, decay factor λ calibration, half-life interpretation
- ARCH(q) — motivation from the residual squared series, the ARCH-LM test, estimation and limitations
- GARCH(1,1) — ω, α, β interpretation, long-run variance, persistence (α+β), covariance stationarity, mean reversion in volatility
- Maximum likelihood estimation in Excel — building the log-likelihood column, Solver setup, starting values, convergence diagnostics and parameter stability
- Asymmetric extensions — GJR-GARCH and EGARCH; capturing the leverage effect
- Volatility term structure; multi-day forecasting and the square-root-of-time fallacy
- Feeding conditional volatility into VaR and Expected Shortfall; Kupiec POF and Christoffersen independence backtests
- Implied vs realised volatility, the variance risk premium, and India VIX construction
Track 07 — Dynamic Portfolio Optimisation
1-day · 2-day · 6-week
CFA L3 · Quant · AMC
NIFTY 50 live prices
Mean–variance from covariance matrix to efficient frontier — then the honest part: what breaks out of sample, and how practitioners fix it.
- Return and risk statistics; building the variance–covariance matrix in Excel arrays from price history
- Two-asset then N-asset portfolio mathematics; matrix algebra with MMULT, TRANSPOSE and MINVERSE
- Global Minimum Variance portfolio — Solver formulation and the closed-form matrix solution, reconciled
- Maximum Sharpe (tangency) portfolio; the Capital Allocation Line and the two-fund separation theorem
- Tracing the full efficient frontier; constrained frontiers — long-only, box, sector, cardinality and turnover constraints
- Dynamic reoptimisation — rolling estimation windows, rebalancing frequency, transaction-cost drag, weight stability analysis
- Estimation error and corner solutions; shrinkage estimators (Ledoit–Wolf intuition), resampled frontiers
- Robust alternatives — risk parity, equal risk contribution, minimum-correlation, and Black–Litterman view blending
- Performance attribution — Sharpe, Sortino, Treynor, Jensen's alpha, information ratio, maximum drawdown, Calmar
- Out-of-sample backtest of the optimised strategy against an equal-weight and index benchmark
Pillar IV — Fixed Income, Credit & Regulation 3 tracks
Track 08 — Fixed Income Analysis
1-day · 3-day · 10-week
CFA L1/L2 · FRM Part I
CCIL · FIMMDA · UST
From bond arithmetic to structured products and liability immunisation, priced on live G-Sec and US Treasury curves.
- Bond pricing, accrued interest, clean vs dirty price, day-count conventions, settlement mechanics
- Yield measures — current yield, YTM, YTC/YTW, realised compound yield, bond-equivalent and money-market yields, the reinvestment assumption
- Spot, forward and par curves; bootstrapping, no-arbitrage pricing, curve shape theories
- Macaulay duration, Modified duration, effective duration, dollar duration, DV01/PV01 and their derivation
- Convexity — measurement, the duration + convexity price approximation, positive vs negative convexity, convexity's value in volatile markets
- Key rate durations and hedging non-parallel curve shifts; butterfly and barbell structures
- MBS — pass-through mechanics, prepayment modelling (CPR, SMM, PSA), weighted average life, extension and contraction risk, negative convexity, OAS intuition, CMO tranching
- ABS — collateral types, credit tranching and the cash-flow waterfall, credit enhancement (overcollateralisation, excess spread, reserve accounts), WAL and loss modelling
- Convertible bonds — conversion ratio and value, investment value, parity and premium, busted vs equity-like converts, binomial valuation with credit-adjusted discounting, delta and hedging
- Callable and puttable bonds; option-adjusted spread and the binomial interest-rate tree
- Immunisation — single-liability duration matching, multi-period immunisation, cash-flow matching (dedication), contingent immunisation, rebalancing discipline and immunisation risk
- Credit spreads, spread duration, and relative-value analysis
Track 09 — Credit Risk Modelling
1-day · 3-day · 10-week
FRM Part II · Risk Teams
Live equity + balance sheets
Own-data option
Both traditions — reduced-form statistical scoring and structural market-based default modelling — built side by side and compared on the same obligors.
- Credit risk architecture — PD, LGD, EAD, EL and UL; through-the-cycle vs point-in-time; regulatory vs economic capital
- Data preparation — sampling and windows, missing values, WOE binning, information value, correlation screening, variable selection
- Logistic Regression PD — logit specification, odds and log-odds, maximum likelihood estimation in Excel Solver, coefficient interpretation and significance
- Scorecard development — scaling log-odds to points, factor and offset, points-to-double-odds, reject inference, score cut-off selection
- Model performance — confusion matrix, sensitivity/specificity, ROC and AUC, KS statistic, Gini, lift and calibration plots; PSI-based monitoring
- Rating grade construction, master scale calibration, migration matrices and transition-implied PD term structures
- Merton structural model — equity as a call option on firm assets, asset value and asset volatility, distance to default, theoretical PD, credit spread implied by the model
- Solving the simultaneous two-equation system for asset value and volatility in Excel (2×2 Solver)
- KMV model — default point construction (STD + ½ LTD), DD computation, empirical mapping from DD to Expected Default Frequency, and how it differs from Merton
- LGD modelling — workout vs market LGD, collateral haircuts, recovery rate distributions; EAD and credit conversion factors for revolving exposures
- IFRS 9 / Ind AS 109 ECL — 12-month vs lifetime ECL, SICR and stage allocation, forward-looking macroeconomic overlays, scenario weighting
- Portfolio credit risk — default correlation, the single-factor Vasicek model, credit VaR, concentration and granularity adjustment
Track 10 — Basel Framework & Enterprise Risk
1-day · 3-day · 12-week
FRM Part II · Banks · Treasury
RBI / BCBS current rules
The four regulatory risk pillars — credit, market, interest rate risk in the banking book, and liquidity — each with the actual capital charge or ratio computed line by line in Excel.
- Basel I → II → III → IV endgame: what changed, why, and what it cost; the three-pillar architecture; RBI implementation timelines
- The capital stack — CET1, AT1, Tier 2; capital conservation, countercyclical and G-SIB/D-SIB buffers; leverage ratio
- Credit risk capital — Standardised Approach risk weights vs Foundation and Advanced IRB; the Vasicek risk-weight function computed end to end (correlation, maturity adjustment, capital requirement K, RWA); the output floor
- Market risk — VaR and Expected Shortfall; parametric, historical simulation and Monte Carlo approaches; scaling, backtesting and the traffic-light regime
- FRTB — trading/banking book boundary, Sensitivities-Based Approach (delta, vega, curvature across risk classes), Default Risk Charge, Residual Risk Add-On, non-modellable risk factors, P&L attribution test, desk-level approval
- IRRBB — repricing gap analysis, Economic Value of Equity and NII sensitivity under the six prescribed rate shocks, behavioural modelling of non-maturity deposits and prepayments, basis risk, optionality risk, the outlier test
- Liquidity risk — LCR and NSFR computed line by line, HQLA Level 1/2A/2B classification and haircuts, run-off and roll-over assumptions, ASF/RSF factors, survival horizon, intraday liquidity, contingency funding plan
- Operational risk — the Standardised Approach, Business Indicator Component and Internal Loss Multiplier
- Stress testing and reverse stress testing — scenario design, macro-to-loss translation, capital impact, supervisory stress test mechanics
- ICAAP and ILAAP construction; Pillar 2 risks; capital planning, RAROC and risk-adjusted capital allocation across business lines
Suggested Multi-Track Pathways
Most certificate engagements combine two or three tracks. These combinations have proven coherent in delivery.
| Pathway | Tracks | Indicative Hours | Designed For |
|---|---|---|---|
| Treasury & ALM | 02 → 08 → 10 (IRRBB + Liquidity) | 36–40 | Bank treasury, ALM and balance-sheet management teams |
| Market Risk | 01 → 06 → 10 (Market + FRTB) | 32–36 | Middle office, market risk and model validation |
| Credit Risk | 09 → 10 (Credit) → 08 (spreads) | 30–34 | Credit risk modelling units, IFRS 9 teams, rating agencies |
| Investment & Corporate Finance | 03 → 04 → 07 | 36–40 | MBA Finance electives, equity research, corporate FP&A |
| Quantitative Analytics | 05 → 06 → 07 | 28–32 | MBA Analytics, data science in finance, quant desks |
| FRM Certification Bridge | 01 → 06 → 09 → 10 | 40+ | FRM Part I & II candidate cohorts |
| CFA Certification Bridge | 03 → 08 → 01 → 07 | 40+ | CFA Level I–III candidate cohorts |
Faculty Development Programmes — What Makes Them Different
Faculty do not need to be taught the theory. They need the implementation, the teaching assets, and the confidence to run the model live in their own classroom the following semester. Every FDP therefore carries a transfer layer that a student workshop does not.
The FDP Transfer Pack
- Session-by-session teaching notes — facilitator script, timing, common student misconceptions flagged, and the build sequence that works in a lab.
- Reusable, documented datasets — clean Excel datasets with a written refresh procedure, so the material stays current year on year without redesign.
- Question banks — graded problem sets, MCQs and exam-style numericals with fully worked solutions, mapped to stated learning outcomes.
- Assessment rubrics — model-audit checklists and marking schemes for Excel-based evaluation, which most departments lack.
- Syllabus templates — course outlines ready to place before a Board of Studies, with outcome mapping and prerequisite chains.
- 90-day support window — follow-up queries as faculty deploy the material, plus one scheduled online clinic session with the cohort.
FDPs are structured to fit standard 1-week (30-hour) and 2-week (60-hour) institutional schedules, with attendance records, pre- and post-programme assessment, participant feedback reports and a co-branded completion certificate — suitable for AICTE/UGC submission and NAAC/NBA documentation evidence.
Live Data — Every Model Runs on Data Pulled for Your Cohort
Datasets are refreshed before each engagement, so a workshop delivered this quarter prices off this quarter's curve. Where an institution has its own anonymised portfolio, loan-book or treasury data, models can be rebuilt on it under a confidentiality undertaking.
NSE / BSE — equities, indices, F&O
India VIX
RBI — policy rates, macro series
FBIL — MIBOR, G-Sec valuation
CCIL — G-Sec trades, repo
FIMMDA — corporate spreads
US Treasury — par yield curve
FRED — SOFR, CPI, macro
CME — futures & options
Company filings — annual reports, XBRL
BCBS / RBI — current regulatory text
How an Engagement Works
STEP 1
Scoping call
30 minutes to establish audience, prior exposure, calendar window and the outcome the institution is actually buying.
STEP 2
Tailored proposal
Written proposal with module map, session plan, learning outcomes, datasets, assessment design and commercials — within three working days.
STEP 3
Delivery
On campus or online. Workbooks distributed after each session. Every model built live, every participant on their own laptop.
STEP 4
Assessment & handover
Graded deliverable, feedback report to the institution, complete asset pack, co-branded certificates, 90-day support window.
Who Delivers
Prof. V. Ravichandran
Founder & Principal Faculty · Visiting Faculty — NMIMS Bangalore · BITS Pilani (WILP) · RV University Bangalore · Goa Institute of Management
28+ years of corporate finance and banking experience at HSBC Global Banking & Markets and Synechron, and 12+ years in academia. The distinction matters for an institutional buyer: content is drawn from nearly three decades of building, validating and defending the models these sessions teach — and regulatory material is presented as it is implemented under supervisory scrutiny, not as it is summarised in a syllabus.
Areas of specialisation span VaR and stress testing, Basel II/III and FRTB, PD/LGD/EAD frameworks, ICAAP, GARCH/EWMA/ARIMA modelling, Monte Carlo simulation, derivatives pricing, fixed income analytics and portfolio optimisation. Alongside institutional engagements, the Academy runs seven open-enrolment programmes for MBA, CFA and FRM candidates — which means the pedagogy is continuously tested against live student cohorts rather than designed once and shelved.
Financial Risk
Derivatives
Fixed Income
Basel III / FRTB
Excel Modelling
CFA · FRM Prep
Practical Questions
What does the institution need to provide?
A computer lab or a room where every participant has a laptop running Microsoft Excel (Analysis ToolPak and Solver add-ins enabled), a projector, and reliable internet for live data pulls. For online delivery, Google Meet. Nothing else — no licensed statistical software, no market data terminal.
Is a prior quantitative background required?
Tracks 01, 03, 04 and 08 assume only undergraduate finance and working Excel. Tracks 05, 06, 07, 09 and 10 assume basic statistics — mean, variance, regression, distributions. A short pre-read and a diagnostic quiz are issued a week ahead so the session starts at the right level. Where a cohort is mixed, a foundation half-day can be prefixed at no design charge.
Can tracks be combined or customised?
Yes, and most engagements are. A treasury team typically wants Tracks 02 and 08 plus the IRRBB and liquidity portions of Track 10. An MBA analytics elective usually takes Tracks 05, 06 and 07. See the suggested pathways. Custom combinations are scoped at proposal stage with no additional design fee.
How is Python handled?
Excel is the primary medium throughout, because it keeps the mathematics visible — a GARCH likelihood is far better understood as a column of cells than as a one-line library call. Every track has an optional Python extension: the same model reproduced in pandas / numpy / statsmodels / scipy, offered as supplementary notebooks or as an added session where the cohort wants it.
What are the commercials?
Institutional engagements are priced per programme rather than per participant, and vary by format, cohort size, mode of delivery and travel. Indicative ranges are shared on the scoping call and confirmed in the written proposal. Multi-track, multi-cohort and multi-year engagements are discounted. (Open-enrolment individual programmes remain at ₹10,000 — see Courses.)
Are certificates co-branded?
Yes. Certificates carry both the institution's and the Academy's marks, and state contact hours, module coverage and assessment outcome — usable in NAAC/NBA documentation and internal L&D records. Attendance registers and pre/post assessment reports are provided to the coordinating office.
Can models be built on our own data?
Yes, for industry engagements. Anonymised loan-book, portfolio or treasury data can replace public datasets so participants leave with a model calibrated to the institution's actual exposures. This is scoped separately and covered by a confidentiality undertaking executed before any data transfer.
What is the lead time?
Standard catalogue tracks require about three weeks' notice. Custom combinations or own-data engagements require four to six weeks. Academic-calendar bookings for the following semester are best confirmed one quarter ahead.
Request a Proposal
Tell us the audience, the calendar window, the tracks or outcomes you need, and the expected cohort size. You will receive a written proposal with a full module map, session plan, learning outcomes and commercials — usually within three working days.
Institutional Enquiry Form
Fields marked * are required. Everything else helps us scope the proposal more precisely — leave blank if undecided.
1 Your Institution
2 Audience & Scale
3 Programme Requirements
4 Finally
* Required fields. Your details are used only to prepare and send the proposal. Response within three working days.