Frequently asked questions

Polarisk enables scenario-driven development for domains such as financial services and telecom. Teams use realistic scenarios to define risk behaviour, develop controls, and evaluate models.

Scenario Studio is publicly available and provides end-to-end, realistic multimodal scenarios that capture fraudulent cases - including the actors, context, and sequence of events. Synthetic text, documents, audio, images, and video bring these scenarios to life so teams can examine fraud behaviour and evaluate how their systems respond. Control Studio is coming soon and is being built for scenario-driven control development, evaluation, and review.

Yes. Scenario Studio is publicly available at scenariostudio.polarisk.ai. For a walkthrough or a specialized requirement, contact the Polarisk team.

Control Studio is still in private development, but we are open to learning about your requirements and hearing your feedback.

Financial crime, risk control, model development, and model risk management teams are our core audience. Scenario Studio also supports synthetic data generation for telecom and research or AI evaluation teams with domain-specific needs.

Yes. Its text, documents, audio, images, and video generation can be used to explore scenarios outside financial services. Share the research objective and evaluation requirements with us to discuss customization.

Emerging fraud detection, risk control development, model risk management, scenario-based audio generation, and fraud and anti-money laundering (FRAML) detection and investigation. Scenario Studio provides synthetic material for these workflows; Control Studio is the upcoming product for control development and review.

Yes, customization is available on request. Tell us your domain, scenario types, output needs, and acceptance criteria. We will confirm feasibility and scope, including any language, annotation, integration, or deployment requirements, before committing to delivery.

It is generated data designed around a defined situation: who is involved, what happens, and why it matters. The scenario gives reviewers a way to assess the meaning of an example, rather than judging only whether its surface features look realistic.

Historical datasets may contain few examples of emerging or rare behaviours. Scenario-driven data lets teams deliberately explore those cases and include legitimate lookalikes. Evaluating both can reveal missed detections and overly broad rules, giving developers specific questions to investigate and test.

The starting point is a question about behaviour. For example: should these two similar-looking interactions produce different alerts? Scenarios let teams vary relevant conditions deliberately and review the intended distinction before using the generated data.

No. Performance on generated scenarios is evidence about those scenarios, not proof of real-world effectiveness or complete coverage. Expert review, representative real-world evaluation, and ongoing monitoring remain necessary. Poorly designed scenarios can reproduce assumptions and blind spots.

No. Scenario Studio generates synthetic data for your workflows. Polarisk’s broader direction is to help improve controls and their evaluation, with Control Studio coming soon.

No. Synthetic scenarios complement existing data and evaluation methods. Review outputs for realism, privacy, and fitness for the intended use; generation alone does not guarantee that data is private, compliant, or representative.