Description

A focused course, tailored for you

Analyst-Grade Financial Modelling for Regulated Capital Markets

Build the structured analysis, model documentation, and audit-ready outputs that turn raw deal data into decisions your seniors and regulators can rely on.

You can build a model that prices the deal correctly. What you cannot yet do consistently is produce the accompanying documentation that survives a senior review, a risk committee question, or an APRA examiner asking why you chose that methodology.

$199 one-time

Tailored to your situation. Access within 24 hours. 30-day money-back.

Includes a hand-built implementation playbook delivered alongside course access, generated for your specific situation.

Why this course

At analyst level in a regulated investment bank, the technical build is only half the job. The other half is a set of structured artefacts that most analysts learn informally, if at all: an assumption register that logs every judgement call, a sensitivity table formatted to the exact standard the credit committee expects, a variance narrative that explains a six-month movement in three paragraphs, a model governance note that tells a future auditor what version of the model was used and why it changed. These artefacts are what separates an analyst whose work gets approved on the first pass from one whose memos come back with red circles. The training programmes that teach financial modelling focus almost entirely on the mechanics. None of them teach the documentation layer that makes the mechanics usable at institutional scale.

What you walk away with

Produce a model assumption register that a risk officer or external auditor can read without a briefing.
Structure sensitivity and scenario tables to the exact format a credit or investment committee expects.
Write a variance narrative that explains a material movement in under three paragraphs, with no ambiguity about cause.
Apply model governance documentation standards so your work is version-controlled and traceable from day one.
Identify the three most common documentation failures that cause senior review delays and eliminate them from your workflow.
Deliver a complete output pack, model file, assumption register, sensitivity table, and committee narrative, for any deal in your current pipeline.

The 12 modules

Module 1. What the Senior Review Is Actually Looking For

Before building anything, understand the exact standard your work is being held to. This module maps the four layers a senior analyst or MD evaluates when they open a model memo: arithmetic correctness, methodology defensibility, assumption transparency, and narrative coherence. Most analyst training covers the first layer. This module addresses all four, using real examples of memos that failed at layer two or three despite correct numbers, and explains what a rewrite of each would look like.

Module 2. The Assumption Register: Building a Defensible Audit Trail

Every financial model rests on judgement calls. The assumption register records each call, its source, its review date, and the output sensitivity to a change in that assumption. This module walks through the standard register template used in regulated capital markets, shows how to populate it from a live model, and covers the three assumption categories that generate the most auditor queries: market-rate inputs, management overlays, and extrapolated forward curves.

Module 3. Sensitivity and Scenario Tables That Committees Can Use

A sensitivity table listing every variable at every increment is not useful to a credit committee. This module covers the two-variable sensitivity matrix and named scenario table, both formatted to the standard expected in an institutional committee paper. Covers the rule of three: base case, plausible downside, extreme-but-credible stress. Includes labelling conventions that prevent the most common misread, where a column heading is mistaken for a row heading.

Module 4. Variance Narratives: Explaining a Number That Changed

When a valuation moves ten percent between committee papers, you will be asked to explain it. This module teaches the three-paragraph variance narrative: opening line states the movement in plain numbers, middle paragraph attributes causes in descending magnitude, closing paragraph addresses the forward implication. Includes a worked example from a corporate loan book where four factors contributed to a material impairment movement and a single unclear paragraph generated three committee questions.

Module 5. Model Governance: Version Control and Change Log Standards

Regulators and internal auditors expect financial models to carry a change log, a version number embedded in the output pack, and a named model owner. This module covers the minimum governance documentation for a model that feeds a regulatory or committee output, the distinction between a minor assumption update and a structural change requiring a full model review, and how to maintain a clean version history in shared file environments.

Module 6. APRA and ASIC Documentation Standards for Capital Markets Outputs

Australian prudential and markets regulators have specific expectations for how financial analysis is documented when it supports capital allocation, credit approval, or market disclosure. This module covers APRA CPG 220 and CPG 235 documentation expectations at analyst level, ASIC regulatory guide standards for model-based valuations, and the practical implications for structuring an assumption register and governance note for a regulatory submission or auditor review.

Module 7. The Credit Committee Paper: Anatomy of an Output That Gets Approved

Credit committees follow a consistent reading sequence: recommendation, deal summary, sensitivity table, risk section. This module maps that sequence onto the structure of a committee paper and shows how to write each section so the answer to the most likely question is already on the page. Covers three sentence types that slow approvals: hedge-loaded sentences, passive-voice methodology descriptions, and forward-looking statements without a stated range.

Module 8. Data Sourcing Documentation: Provenance, Date, and Reliability Rating

When an auditor asks where a market rate or comparable transaction multiple came from, the answer needs to be in the model file. This module covers the data provenance table, recording the source, publication date, and reliability rating of every external input. Includes the standard institutional reliability scale, distinguishes between terminal pulls, broker estimates, and management-provided figures, and shows how a provenance gap creates an audit finding even when the number is reasonable.

Module 9. Structuring the One-Page Executive Summary

Most analyst memos fail at the executive summary by summarising the methodology rather than the decision. This module teaches the four-element format used in regulated capital markets: recommendation in one sentence, key numbers in a three-line table, two principal risks with probability and impact, and one condition that would change the recommendation. Covers the difference between a summary that informs a decision and one that requires reading the full memo first.

Module 10. Internal Audit Readiness: What the Model Review Team Is Looking For

Internal audit reviews of financial models look for five elements: methodology note, assumption register, version log, independent validation evidence, and documented limitations. This module walks through each element, shows what a clean review file looks like, and covers the two most common audit findings on analyst-produced models, undocumented overrides and missing sensitivity ranges, with instruction on addressing both before the review rather than in response to it.

Module 11. Communicating Model Limitations Without Undermining the Recommendation

Every model has limitations. The question is how to disclose them honestly without creating the impression the recommendation is unreliable. This module covers the limitations section of a committee paper: framing a data gap, quantifying the impact of an unresolvable uncertainty, and recommending mitigants without hedging on the core recommendation. Includes examples from infrastructure project finance where the limitations section was the deciding factor in a committee's confidence level.

Module 12. The Complete Output Pack: Assembling Everything for a Real Deal

The final module walks through a complete output pack for a single deal: model file with version log, assumption register, data provenance table, sensitivity matrix, variance narrative, limitations section, and executive summary. Uses a composite infrastructure debt transaction as the worked example. The implementation playbook delivered alongside the course is structured for the deal type most relevant to your current role. You assemble one real output pack during the course, not a practice exercise.

How this addresses your situation

Specific modules that map to what you said you are dealing with.

Your MD circled the methodology note, not the numbers: Modules 1, 2, 7.

An APRA or internal audit query landed on a model you built: Modules 5, 6, 10.

A credit committee paper came back with questions before approval: Modules 3, 7, 9.

You are building documentation habits from the start of a new deal: Modules 2, 4, 8, 11, 12.

What you get with this course

12 written modules covering the full analyst documentation workflow in regulated capital markets.
Downloadable templates for every module: assumption register, sensitivity matrix, variance narrative, data provenance table, model governance log, executive summary, and limitations section.
A worked example deal (composite infrastructure debt transaction) used consistently across all modules.
The hand-built implementation playbook, structured for your current deal type, delivered alongside course access.

What you will have in hand by Day 1, Week 1, Month 1

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.

Before and after

Before

Models that price deals correctly but generate repeated senior review cycles because the methodology note, assumption register, or sensitivity table does not meet the institutional standard your committee or regulator expects.

After

A complete output pack for every deal, assembled to the standard that passes a senior review on the first submission, satisfies an internal audit, and holds up under an APRA documentation query, with every template pre-populated and a personal playbook for your current deal type.

What happens if you do not address this

The documentation layer is learned informally at most institutions, usually through repeated feedback on rejected memos. Each cycle costs a week of revision time and delays deal approval. More materially, analysts who cannot produce audit-ready documentation are excluded from the deal flow that leads to associate promotion because seniors cannot trust their outputs to go directly to committee.

Who it is for

Analysts and associate analysts in investment banking, corporate banking, asset management, or capital markets at a major regulated financial institution. You have the quantitative foundation and you are competent with Excel or similar modelling tools. You are being asked to produce outputs that stand up not just to your immediate team but to risk committees, internal audit, and prudential regulators, and you have mostly figured out how to do that through trial and error rather than through any structured instruction.

Who this is NOT for. Quantitative researchers building proprietary trading models. Data scientists in fintech who do not operate within a regulated capital markets framework. Anyone whose primary output is a dashboard rather than a structured analytical memo or committee paper.

How it arrives

Text-based course in the Art of Service learning environment, plus downloadable templates and worked examples for every module, plus the hand-built implementation playbook delivered alongside course access.

Time investment. Six to eight hours across the twelve modules. Most analysts complete two to three modules per session. The templates are designed to be used on your current deal during the course, so the time investment is partially offset by deal work.

Why $199 is the right number

CFA and FRM programmes teach financial theory and modelling mechanics. They do not teach the documentation layer: assumption registers, model governance logs, variance narratives, or committee paper structure. Internal training at most banks covers deal mechanics but not the artefact standards that govern how analyst outputs are reviewed, audited, and approved. This course covers only what the formal programmes and internal training leave out.

FAQ

Is this relevant if I work in asset management rather than investment banking?

Yes. The assumption register, sensitivity table, and model governance standards covered in this course apply to any context where a financial model feeds a committee decision or a regulatory output, which includes asset management portfolio analytics, infrastructure debt, and corporate lending.

Do I need a specific modelling tool or software?

No. The course covers documentation standards and output structure, not the mechanics of any particular modelling platform. The templates are in Excel format but the principles apply to any tool.

How is the implementation playbook tailored to me?

When you enrol, you specify your current deal type and role context. The implementation playbook is built for that specific context, with the template fields pre-labelled for the asset class and regulatory environment you are working in.

What if I have questions during the course?

Reply by email and I will answer directly. No ticketing system, no support queue.

30-day money-back guarantee. If after a week of working through the materials this is not what you needed, reply to the receipt email and a full refund is processed. No questions, no forms.

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.