Risk teams building stress testing frameworks often hit the same wall, according to Kidbrooke. They hold a credible macroeconomic baseline, whether from a national forecast, board-approved assumptions or an in-house economics team, but lack a defensible method for converting it into the downside, central and upside scenarios their processes require.
Spreadsheets can nudge a policy rate or inflation figure manually, yet they rarely capture historical relationships between variables, the uncertainty around each path, or the evidence trail auditors eventually demand.
Kidbrooke’s Economic Scenario Generator (ESG) was built to close that gap. Already powering pension forecasting, investment advice and portfolio analytics across the WealthTech platform through thousands of Monte Carlo simulations, the ESG has been extended for regulatory and credit risk stress testing. New additions include a granular macroeconomic layer, the central bank policy rate as a standalone variable, a flexible inflation measure institutions can align to internal definitions, and richer GDP and unemployment models. Baselines typically span five years, extendable as methodologies mature.
Kidbrooke argues the harder problem is conceptual. Whether for IFRS 9 expected credit loss modelling, internal capital adequacy or a regulatory submission, institutions must decide how severe each scenario should be and, separately, how much weight it carries. These are distinct governance decisions, and blending them is where audit trails quietly unravel. If an analyst tweaks a downside path’s severity to hit a target ECL number, the scenario becomes difficult to explain and harder to defend when a regulator asks why it looks the way it does.
KidbrookeONE keeps the two apart. Severity is set through percentile levels drawn from the simulated distribution, giving each path a consistent, statistically grounded meaning across reporting periods. Weighting remains a governance judgement that can shift with the economic outlook without disturbing how scenarios were constructed.
The firm has also moved the ESG out from behind the API. A guided workflow lets analysts start from their own baseline, lock fixed assumptions, and project remaining variables using the model’s statistical relationships, working through policy rate, inflation, GDP and unemployment in sequence. A single control balances model influence against analyst judgement, with paths responding instantly, and users can interrogate the correlations driving each projection rather than taking them on faith.
Every scenario set carries a visible record of its baseline, anchored variables, model influence applied and percentile selected, exportable as structured CSV files with dates, sources and version information for downstream models
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