Simukon generates stateful financial behavior, mutates fraud scenarios, and probes the models and rules you already run—helping teams find blind spots before deployment and model changes.
Historical validation is constrained by known attack patterns and available labels. Rare, novel, and fast-changing behaviors leave gaps that are difficult to measure with historical data alone. Simukon creates a controlled environment for exploring those gaps.
The highest-impact failure patterns may have the fewest historical examples.
A new attack pattern can affect customers before enough confirmed cases exist for retraining and validation.
Increasing sensitivity can also increase legitimate declines, reviews, and customer friction. Both outcomes need to be measured.
Simukon adds an independent simulation and testing layer around existing models and rules. It generates financial behavior, searches for bypass patterns, and converts discovered failures into repeatable regression tests.
Choose a scenario. Simukon varies behavioral and transactional conditions, measures the model's response, and searches for combinations it consistently approves.
Run identical scenario versions, seeds, thresholds, and evaluation windows across vendor models, in-house rules, and candidate systems. Compare their strengths and trade-offs using repeatable evidence.
Simukon models financial entities and relationships that persist over time. Accounts develop histories. Devices move between users. Merchants, beneficiaries, sessions, and payment behavior remain connected. This makes it possible to test sequences and attack paths that isolated rows cannot represent.
Fraud is Simukon's first proving ground: behavior changes quickly, failures are costly, and outcomes are measurable. The same core infrastructure could extend to other financial-risk domains as those products are developed.
From fraud-model stress testing to simulation and assurance infrastructure for financial risk.
We are working with a small number of fraud and risk teams to run scoped simulations against their existing models. If that is relevant to your team, tell us a little about your setup.