> For the complete documentation index, see [llms.txt](https://docs.scaleconnect.com.au/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.scaleconnect.com.au/scaleconnect-docs/trust-centre/data-integrity.md). # Data Integrity ScaleConnect's simulation engine — the calculations behind every cashflow projection, tax figure, equity position, and borrowing power estimate — was independently audited and certified, and is regression-tested on every code change. *** ## Financial Model Certification The simulation engine was independently audited and certified by a globally recognised model auditing firm ranked #1 worldwide for model audit by deal count and deal value. ### Why we did it Financial modelling is a precision discipline. A rounding error in a tax calculation or a misapplied depreciation schedule doesn't just produce a wrong number — it produces a wrong decision. Our position is that a model that is 99% accurate is 100% unreliable, because you can't tell which 1% is wrong. ScaleConnect is not required to have its financial model independently audited. We chose to do it anyway, because brokers and their clients are making real financial decisions based on what the simulator produces. ### Who audited it The audit was conducted by a specialist financial model auditing practice with a dedicated team of over 100 model audit professionals across Sydney, London, Delhi, New York, Toronto, Johannesburg, and Paris. They are the same firm that audits financial models for major infrastructure projects, renewable energy financing, and institutional-grade transactions globally. ### How the audit worked The auditors used a **shadow modelling** methodology — an end-to-end independent rebuild and verification of the model's logic, which is substantially more rigorous than traditional "tick-and-bash" checking. The seven-month engagement verified that: * The model's logic and calculations are **materially arithmetically correct** and the results are accurate, complete, and consistent with the assumptions * The model generates accurate **profit and loss, cashflow, and balance sheet projections** along with key financial ratios * The model contains **no unintended inconsistent formulae** across all relevant time periods * The model contains **no unresolved circularities** ### What was covered | Area | What was tested | | ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------- | | **Personal cashflows** | Salary, living expenses, tax (including PAYG, Medicare, HECS), superannuation, side hustle income, personal loans, dependant offsets | | **Property cashflows** | Rental income, operating costs, loan repayments (P\&I and IO), interest calculations, vacancy modelling, cost escalation | | **Tax calculations** | Negative gearing, capital gains tax (including 50% discount and deemed acquisition), depreciation (Div 40 and Div 43), land tax, stamp duty | | **Loan mechanics** | Variable and fixed rates, refinancing, construction loan drawdowns, offset accounts, voluntary repayments, balloon payments | | **Portfolio aggregation** | Multi-property portfolio rollups, cross-property equity positions, combined cashflow projections | | **Event processing** | All property and personal event types — rate changes, sales, renovations, granny flats, partial rentals, bonuses, one-off expenses | | **Goal tracking** | Target property count, equity milestones, savings targets, debt reduction, borrowing power thresholds | ### The outcome The simulation engine received a clean Model Audit Opinion confirming that the model produces accurate outputs across all tested scenarios. Where the audit identified edge cases or boundary conditions, the model was updated and re-verified before the opinion was issued. This certification underpins every projection, AI review, and readiness assessment generated by the platform. *** ## Calculation Integrity Testing Beyond the initial certification, ScaleConnect maintains an automated testing pipeline that validates the simulation engine on every code change. ### How it works The financial model's calculations are tested against a reference model — the same certified baseline used during the independent audit. Every time a developer submits a code change that touches the simulation engine, the automated test suite runs: 1. **Scenario generation** — a library of test scenarios covering the full range of client situations (first home buyers, multi-property investors, joint owners, construction loans, downsizers, and more) 2. **Input extraction** — each scenario's inputs are fed into the simulation engine 3. **Output comparison** — the simulation engine's outputs are compared against the reference model's expected results 4. **Regression detection** — any deviation from the expected output blocks the change from being deployed The test suite covers personal cashflows, property cashflows, portfolio aggregations, tax calculations, event processing, and goal tracking — the same areas verified during the independent audit. ### What this means for you Every projection you see in ScaleConnect has passed through the same validation pipeline. When we add new features or update calculations, the existing test suite confirms that nothing else has changed. A rate change calculation that worked yesterday still works today. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.scaleconnect.com.au/scaleconnect-docs/trust-centre/data-integrity.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. 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