Provides an AI-powered risk decisioning platform that integrates machine learning models and behavioral analytics to manage onboarding, fraud, credit, and compliance risks in real time. By reducing false positives, accelerating approval rates, and enabling autonomous risk management, it helps fintechs and banks streamline operations and enhance security.
Funding
$20M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Financial institutions and fintechs face increasing challenges in managing various risks, including onboarding, fraud, credit, and compliance, often relying on disparate systems and manual processes. This can lead to inefficiencies, increased false positives, slower approval rates, and difficulty in adapting to evolving regulatory landscapes and sophisticated fraud tactics.
Solution
Oscilar provides an AI-powered risk decisioning platform that consolidates and automates the management of onboarding, fraud, credit, and compliance risks. The platform leverages machine learning models and behavioral analytics to provide real-time risk assessments, streamline operations, and enhance security. By integrating with existing data providers and data warehouses, Oscilar offers a unified, 360-degree risk view, enabling faster and more accurate decision-making. Its no-code, low-code, and natural language interface empowers risk teams to autonomously optimize risk strategies and adapt to changing conditions without extensive engineering support.
Target Audience
Oscilar primarily serves fintechs, banks, credit unions, and sponsor banks seeking to streamline risk management processes, reduce fraud losses, and improve compliance outcomes.
Features
- AI-powered risk decisioning across onboarding, fraud, credit, and compliance
- One-click integrations with data providers and data warehouses for a unified risk view
- No-code, low-code, and natural language interface for autonomous risk management
- Specialized machine learning models tuned to specific risk use cases
- Cognitive Identity Intelligence leveraging device intelligence and behavioral analysis
- AI Explainability and Root Cause Analysis to understand drivers of risk changes
- Powerful testing capabilities including backtesting, A/B testing, and shadow testing
- Real-time risk assessment and decisioning