Inscribe provides AI Fraud Analysts that automate fraud detection and risk assessment for finance organizations, significantly reducing manual review times by 99%. This technology enables risk teams to efficiently verify applicant details and assess transaction risks, allowing them to focus on onboarding trustworthy customers.
Funding
$38.7M 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.
+1Founders
Product
Problem
Financial organizations face challenges in efficiently detecting fraud and assessing risk during onboarding and underwriting processes. Manual review of applications is time-consuming, resource-intensive, and prone to human error, leading to potential fraud losses and slower customer onboarding.
Solution
Inscribe provides AI Risk Agents and Risk Models that automate fraud detection, applicant verification, and risk assessment for financial institutions. The AI Risk Agents can read, write, and reason like human analysts, autonomously performing routine tasks and coordinating outputs from various systems. By connecting the dots across systems, Inscribe enables risk teams to focus on approving more customers faster, reducing manual review times by up to 99% and improving overall efficiency. The platform leverages machine learning models trained on a diverse network of real-world financial documents to identify fraud and credit risks undetectable by human reviewers.
Target Audience
Inscribe primarily targets fintech companies, banks, and lenders seeking to automate their risk management processes, reduce fraud losses, and improve operational efficiency.
Features
- AI Risk Agents that automate complex onboarding and underwriting workflows
- AI-powered fraud detection, document parsing, and cashflow analysis
- Automated verification of applicant details and assessment of transaction risks
- Generation of auditable risk reports
- Integration with existing systems to connect data points and streamline processes
- Continuous operation (24/7) and scalability to handle increasing application volumes
- State-of-the-art machine learning models trained on a large dataset of financial documents