Start with evidence
Define the customer, exposure, evidence window, and baseline before introducing a hypothetical change.
Explore how changing income, costs, payment timing, and connected exposure could affect a financial assessment. Keep the evidence, assumptions, and trade-offs visible before your team decides.
Explicit assumptions. Traceable calculations. Your judgment.
Evidence + assumptions + a reviewable comparison
A change in receipts can affect cash available for debt service. A delayed buyer payment can affect a seller’s liquidity. Useful simulation makes those relationships explicit, so your team can see what drives a result and what still needs investigation.
Define the customer, exposure, evidence window, and baseline before introducing a hypothetical change.
State which inputs move, by how much, and over what period. Keep the unchanged inputs visible too.
Compare the outputs, inspect the calculation, and identify the assumptions most important to the decision.
Try three fictional examples. Move the controls to compare a monthly baseline with an explicit change in income, costs, or collection timing.
A fictional business with CAD 100,000 in monthly receipts, CAD 70,000 in operating outflows, and CAD 15,000 in scheduled debt payments.
Scheduled debt payments and the observation period. No new borrowing, asset sales, or changes in payment timing are assumed.
| Input | Baseline | Scenario |
|---|---|---|
| Eligible receipts | $100,000 | $80,000 |
| Operating outflows | $70,000 | $70,000 |
| Scheduled debt payments | $15,000 | $15,000 |
| Residual cash | $15,000 | -$5,000 |
Residual cash = eligible receipts − operating outflows − scheduled debt payments.
Cash-flow coverage proxy: (($80,000 − $70,000) ÷ $15,000) = 0.67×.
A mechanical sensitivity example, not a forecast, default probability, or approval recommendation. Values are rounded to whole Canadian dollars.
Choose the financial question before choosing a model. Agree the methodology and delivery scope for your Amlok deployment.
Explore changes in operating receipts, costs, collection timing, and scheduled obligations. Examine which assumptions drive capacity and where a related customer or supplier adds concentration.
Discuss business cash-flow scenariosCompare consistent net-income, essential-expense, and debt-payment definitions. Explore income interruption or higher expenses without treating a single ratio as a universal approval rule.
Discuss consumer affordability scenariosExplore the financial effect of delayed collections, buyer concentration, or a principal’s changing liquidity. Keep seller cash flow, buyer non-payment, and insured claims as separate outcomes.
Discuss exposure scenariosA customer’s delayed payment may reduce a business’s available cash. The consequence depends on buyer concentration, payment terms, reserves, and the obligations falling due.
Use supported relationships and authorized evidence to establish which counterparties and obligations belong in scope.
Explain how an assumed change affects receipts, expenses, timing, or exposure. A graph connection alone does not establish a causal effect.
Keep unknown relationships and missing contract terms visible. Avoid counting the same exposure or shock more than once.
Part of an expected collection is deferred.
Less cash arrives within the review period.
Existing payments still fall due.
Can the available buffer absorb the timing gap?
Make the scenario review repeatable by keeping the scope, assumptions, and interpretation together.
Specify the decision, eligible cases, evidence cutoff, and financial period.
Choose a plausible change. Document severity, timing, affected inputs, and fixed constraints.
Inspect the baseline and scenario using the same definitions. Separate missing evidence from changed outcomes.
Review sensitivity, limitations, and alternative explanations. Record the institution’s conclusion.
EvidenceSame population and observation window
AssumptionsChanged inputs and fixed constraints
ComparisonBaseline, scenario, and difference
LimitationsMissing inputs and untested effects
DecisionInstitution review and rationale
Use a defined scenario to ask where capacity becomes sensitive, which exposures deserve closer review, or what a proposed policy change would mean for the same cases.
Examine the assumptions supporting repayment capacity and the conditions that could change the assessment.
Identify which segments or connected exposures warrant deeper investigation under a defined shock.
Compare alternatives with consistent criteria. Keep simulated eligibility changes separate from demonstrated repayment outcomes.
Insights supports the original assessment. Scenario exploration asks what would change under specified assumptions. Signal follows new evidence as the actual relationship evolves.
Organize the evidence and review the financial context.
Risk graphExplore supported connections behind the exposure.
SignalCompare new evidence with the original baseline.
Bring your data sources, product definitions, and review requirements. We’ll discuss the simulation approach and delivery scope that fit your institution.
Scenario analysis helps your team explore how explicit changes to financial inputs or policy assumptions affect a documented baseline. It supports underwriting discussions, portfolio reviews, and questions about resilience. It does not establish that a scenario will happen.
No. They are fictional, deterministic examples in Canadian dollars. Each uses the displayed inputs and formulas. They are not live customer records, model forecasts, default probabilities, or evidence of Amlok’s predictive performance.
Start with the decision you want to support, the evidence available, and the output your team needs. Agree the simulation methodology, delivery scope, and integrations with Amlok for your deployment. The examples here demonstrate the analytical approach rather than promise a preconfigured production workflow for every product.
Use assumptions relevant to the product, evidence window, and contractual terms. State what changes and what remains fixed. For example, a rate change should only affect a payment where the contract and timing allow repricing. Review related assumptions together to avoid double-counting the same shock.
A policy review can compare the same eligible cases and evidence snapshot under clearly defined alternatives. Keep missing evidence, exceptions, and changes in case composition visible. A different number of cases meeting a threshold is not proof of better repayment outcomes.
A simulation changes assumptions to explore a hypothetical result. Signal monitoring evaluates newly observed evidence against an existing baseline. Keeping those two views distinct lets your team compare a possible change with a change that has actually been observed.
Your institution owns policy, approvals, terms, pricing, coverage, and any customer action. Scenario results inform that judgment. The evidence, assumptions, limitations, and decisions should remain distinguishable in the review.
A point-in-time decision does not need to become a blind spot for the life of the deal.
