Overview
Simulation training gives financial crime analysts a safe environment to practise realistic decisions before live work. They investigate evidence, apply policy logic, choose an outcome, record their rationale and receive feedback.
Scenarios cover screening and CDD, transaction monitoring, EDD, SAR writing and case simulation. Team leads can review readiness, decision quality and repeated errors to target coaching and calibration.
Screening and CDD
Transaction monitoring
Enhanced
due diligence
SAR writing
Case simulation
What we cover
How realistic practice builds decision quality
Realistic cases that build judgement
How we train safely
Training uses synthetic names, transactions, account details and supporting evidence so analysts can practise without using real customer data or live production cases.
Synthetic data, no live exposure
The simulator can validate the decision, rationale, supporting evidence, links and completion steps, giving analysts feedback before they take responsibility for live cases.
Checks beyond the final action
Measurable readiness
with clearer feedback
Our features
Analysts practise screening, CDD, transaction monitoring, EDD, SAR writing and case simulation through varied casework-style scenarios.
Scenario-based training across fincrime
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Use domestic and international case variants, including rarer true-positive examples.
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Target known training gaps through scenario packs and new combinations of evidence.
Dashboards show completed work, pass and fail rates, analyst and scenario performance, average handling time, historical outcomes and progress.
Readiness reporting and targeted coaching
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Identify repeated errors and which scenarios create the most difficulty.
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See who is ready for live work and where more support or calibration is needed.
Simulation training helps teams build analyst readiness, improve decision consistency and strengthen rationale and evidence quality. Realistic practice provides a safer learning environment before live exposure.
Managers gain clearer coaching conversations and QA calibration data. Repeated scenarios also support a stronger progression from junior analysts to more complex judgement, as AI takes on repetitive work.
Expected outcomes from repeatable practice
Decision quality, evidence and training oversight
Synthetic practice avoids using real customer or live production data.
Training validates actions, rationale, evidence, links and completion steps.
Immediate feedback and saved decisions give team leads clear material for coaching.
Dashboards help teams assess readiness, repeat errors and where QA calibration is needed.
How simulation training works
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Choose a scenario
Screening, TM, EDD, SAR
or case simulation
Investigate
Review facts and supporting evidence
Decide and record
Select an outcome and explain the rationale
Receive feedback
Review actions, evidence and missing steps
Track readiness
Use results to direct coaching and QA