Financial models that boards can interrogate and lenders can trust.
Decision-grade financial models, feasibility studies, scenario and sensitivity analysis, Monte Carlo simulation and data transformation for boards, investors and lenders.
Financial Modelling & Data Analytics
A financial model is only useful if the people relying on it can follow the logic, test the assumptions and understand the range of outcomes. Many models circulating in the region are neither transparent nor stress-tested, which erodes confidence when it matters most: in front of an investment committee, a lender or a regulator.
ECT builds three-statement models, project finance and feasibility models, valuation models and budgeting tools using disciplined modelling standards. We layer scenario and sensitivity analysis on top and, where uncertainty is material, run Monte Carlo simulations so decision-makers see probability distributions rather than a single point estimate.
Our analytics practice also cleans, transforms and reconciles large datasets, from loan tapes and receivable ledgers to payroll and sales data, producing the reliable inputs that accounting models and management reporting depend on.
Define the decision
We clarify the question the model must answer and who will use it, then design outputs first.
Structure the model
Inputs, calculations and outputs are separated; drivers are explicit; formulas are consistent and documented.
Populate and test
Assumptions are sourced and referenced; the model is stress-tested and checked with integrity tests.
Present and hand over
Outputs are packaged for the audience, with a user guide and training for the team that will maintain it.
- Boards and investment committees evaluating projects
- CFOs preparing budgets, forecasts and bank submissions
- Investors and family offices assessing acquisitions
- Developers and contractors with project finance needs
- Government entities preparing business cases for new programmes
The technicality behind Financial Modelling & Analytics.
Straight answers to the questions finance teams, auditors and boards ask us most often.
What modelling standards do you follow?
We follow the principles of the FAST standard and ICAEW modelling guidance: separation of inputs, calculations and outputs; one formula per row copied across; no hard-coded numbers inside formulas; explicit flags and timelines; consistent sign conventions; and integrity checks that aggregate to a single model-health indicator. Colour coding distinguishes inputs, calculations, links and checks. This makes the model auditable and maintainable by your own team.
When is Monte Carlo simulation worth doing rather than simple scenarios?
Scenarios are appropriate when a small number of discrete futures capture the decision, for example three oil price paths. Monte Carlo simulation is worth doing when several uncertain inputs interact, when their correlations matter, or when the decision-maker needs a probability of a threshold outcome, such as the likelihood that a debt service coverage ratio falls below a covenant. We specify distributions for each uncertain input based on data or expert judgement, model correlations, run typically 10,000 iterations, and present distributions and exceedance probabilities alongside the deterministic base case.
How do you derive a discount rate for valuation or feasibility work in the GCC?
We build a weighted average cost of capital from a risk-free rate based on US Treasuries or local government bonds, an equity risk premium with a country risk premium for the specific market, a levered beta from comparable listed companies, a size or specific-risk premium where justified, and a cost of debt reflecting current local borrowing rates. Every input is referenced to a source and dated. For IFRS purposes the rate is converted to a pre-tax basis where the standard requires it.
Can you build models for bank financing submissions?
Yes. Lenders in the region expect a three-statement model with a debt schedule, covenant tests (DSCR, leverage, interest cover), sensitivity cases and a clear assumptions book. We build to lender templates where they exist and prepare the accompanying information memorandum or financial summary.
Do you review models built by others?
Yes. A model review covers structure, formula logic, hard-codes, circular references, timeline integrity, assumption reasonableness and output reconciliation. We deliver a findings log by severity and, where requested, correct the model.
What tools do you use for data analytics?
Excel with Power Query and Power Pivot for most finance datasets, Python (pandas) for larger or repetitive transformations and statistical work, and Power BI for dashboards. Deliverables remain in tools your team can maintain.
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