Trusted by institutions building next-gen finance
plug and play
Leverage pre-integrated data sources, models, and templates for a fast and simple implementation.
Pre-Built Integrations
Leverage KYC, KYB, CDD data sources and watchlists for a streamlined onboarding setup.
Out-of-the-Box AML Scenarios and Rule Templates
Easily adapt to your fintechs diverse payment types, customers, products and geographies with Oscilar’s pre-built scenarios, templates, and models.
Simplified Case Management
Streamline case management by leveraging comprehensive case cards, built-in evidence collection and automated SAR filing.
flexible and accessible
Easily create and test tailored workflows to address your fintech’s unique risk profile.
AI-Powered Workflow Creation
Describe your workflow and rules logic in natural language and watch these come to life.
No-code and Low Code Interface
Easily drag-and-drop pre-made and custom logic to build risk models.
Powerful Testing Suite
Run backtests, A/B tests, and unit tests in one click to validate existing and new risk models and their impact on your custom business KPIs.
tailor-made models
Improve detection accuracy and reduce false positives with the most advanced ML models and templates.
Alert Prioritization ML Model
Prioritize alerts based on their significance and potential risk, facilitating response management.
Unsupervised Anomaly Detection Model
Detect patterns indicative of money laundering, mule activities, layering, and other illicit behaviors.
reporting assistance
Save countless hours with AI-driven SAR reporting
Automated SAR Generation
Accelerate SAR-filing with auto-populated case information and AI-generated narratives.
One-Click Escalation
Single click escalation of SAR/UAR alerts to sponsor bank.
Customers see results with Oscilar.
FAQ
Glad you asked.
What does an AML compliance platform for fintechs do?
An AML compliance platform helps a fintech apply risk-based controls across onboarding, screening, transaction monitoring, alert review, investigations, SAR workflows, and program reporting. It should connect relevant customer and transaction data, support different products and payment rails, and preserve an audit trail of policies and decisions. The exact controls depend on the fintech's activities, licenses, geographies, and bank-partner requirements.
How does AML compliance for a fast-growing fintech differ from AML at a traditional bank?
The underlying obligation to manage financial-crime risk does not become lighter because a company is technology-led. Fintechs often face a different operating pattern: digital onboarding, rapid volume growth, new products and geographies, lean teams, multiple payment rails, and sponsor-bank oversight. Their AML systems therefore need fast integration and iteration, but policy changes still require testing, approval, documentation, and clear ownership.
Should a fintech choose an AML-first tool or a platform that also covers fraud?
Choose an AML-first tool when the need is narrow, the surrounding stack is stable, and separate teams can manage the integrations. Choose a combined fraud and AML platform when the same customer, device, payment, and network signals inform both functions, or when duplicated investigations create cost and blind spots. The tradeoff is broader governance and implementation scope in exchange for shared context. Oscilar combines fraud and AML decisioning, workflows, and case management in one configurable platform.
How quickly can compliance teams change policy as products and geographies expand?
Speed should come from modular, versioned policy design, not from bypassing governance. Teams should be able to adapt scenarios, thresholds, data sources, and escalation paths by product, customer segment, payment rail, and geography; backtest the change; obtain approval; and release it with a recorded version history. Oscilar provides configurable workflows and testing so governed changes can move without an engineering project.
What proof should a fintech request that AML operations can scale without proportional headcount growth?
Ask for evidence tied to your own baseline: alerts per thousand transactions, analyst touch time, time to disposition, escalation rate, case aging, SAR preparation time, and quality-review findings. Separate measured customer results from projections, and test representative historical alerts with agreed success criteria. Oscilar supports alert prioritization, automated evidence collection, case workflows, and SAR preparation; the strongest validation is a controlled proof of concept showing where automation removes work without weakening coverage or human oversight.
How should a fintech coordinate AML operations with its sponsor bank?
The parties should agree on required controls, data access, reporting metrics, approval boundaries, escalation paths, SAR responsibilities, and evidence-retention standards before volume grows. A shared platform can make program health, alerts, cases, policy versions, and remediation visible to both teams with appropriate permissions. It does not replace the contractual division of work or the sponsor bank's oversight responsibilities.
![](data:image/svg+xml,<svg display="block" role="presentation" viewBox="0 0 48 48" xmlns="http://www.w3.org/2000/svg"><g d="M 0 48 L 0 0 L 48 0 L 48 48 Z M 44.447 0 L 3.544 0 C 1.584 0 0 1.547 0 3.459 L 0 44.531 C 0 46.444 1.584 48 3.544 48 L 44.447 48 C 46.406 48 48 46.444 48 44.541 L 48 3.459 C 48 1.547 46.406 0 44.447 0 Z M 14.241 40.903 L 7.116 40.903 L 7.116 17.991 L 14.241 17.991 Z M 10.678 14.869 C 8.391 14.869 6.544 13.022 6.544 10.744 C 6.544 8.466 8.391 6.619 10.678 6.619 C 12.956 6.619 14.803 8.466 14.803 10.744 C 14.803 13.013 12.956 14.869 10.678 14.869 Z M 40.903 40.903 L 33.787 40.903 L 33.787 29.766 C 33.787 27.113 33.741 23.691 30.084 23.691 C 26.381 23.691 25.819 26.588 25.819 29.578 L 25.819 40.903 L 18.712 40.903 L 18.712 17.991 L 25.537 17.991 L 25.537 21.122 L 25.631 21.122 C 26.578 19.322 28.903 17.419 32.362 17.419 C 39.572 17.419 40.903 22.163 40.903 28.331 L 40.903 40.903 Z" fill="transparent" height="48px" id="adFk4VB3K" width="48px"><path d="M 0 48 L 0 0 L 48 0 L 48 48 Z" fill="transparent" height="48px" id="DghPi7XP3" width="48px"/><path d="M 44.447 0 L 3.544 0 C 1.584 0 0 1.547 0 3.459 L 0 44.531 C 0 46.444 1.584 48 3.544 48 L 44.447 48 C 46.406 48 48 46.444 48 44.541 L 48 3.459 C 48 1.547 46.406 0 44.447 0 Z M 14.241 40.903 L 7.116 40.903 L 7.116 17.991 L 14.241 17.991 Z M 10.678 14.869 C 8.391 14.869 6.544 13.022 6.544 10.744 C 6.544 8.466 8.391 6.619 10.678 6.619 C 12.956 6.619 14.803 8.466 14.803 10.744 C 14.803 13.013 12.956 14.869 10.678 14.869 Z M 40.903 40.903 L 33.787 40.903 L 33.787 29.766 C 33.787 27.113 33.741 23.691 30.084 23.691 C 26.381 23.691 25.819 26.588 25.819 29.578 L 25.819 40.903 L 18.712 40.903 L 18.712 17.991 L 25.537 17.991 L 25.537 21.122 L 25.631 21.122 C 26.578 19.322 28.903 17.419 32.362 17.419 C 39.572 17.419 40.903 22.163 40.903 28.331 L 40.903 40.903 Z" fill="rgb(20, 20, 20)" height="48px" id="Qi9pnIQwR" width="48px"/></g></svg>)
![](data:image/svg+xml,<svg display="block" role="presentation" viewBox="0 0 48 48" xmlns="http://www.w3.org/2000/svg"><path d="M 34.653 0 L 41.4 0 L 26.659 16.847 L 44 39.772 L 30.422 39.772 L 19.788 25.868 L 7.62 39.772 L 0.869 39.772 L 16.635 21.752 L 0 0 L 13.922 0 L 23.535 12.709 Z M 32.285 35.734 L 36.023 35.734 L 11.891 3.826 L 7.879 3.826 Z" fill="rgb(20, 20, 20)" height="39.77194px" id="A8r2uGwai" transform="translate(2 3.808)" width="44px"/></svg>)
