Ambriel

About Ambriel

Ambriel is a cutting-edge fraud intelligence and risk management platform designed to empower digital businesses to operate securely while adhering to regulatory requirements. This sophisticated tool is ideal for industries such as fintech, marketplaces, retail, and iGaming, providing a comprehensive solution that protects users without introducing friction into legitimate activities. Ambriel combines advanced behavioral analytics, device intelligence, sanctions screening, and real-time risk scoring into a single, powerful ecosystem. It enables organizations to proactively detect, score, and prevent fraud before it can negatively impact revenue or damage reputation. By analyzing transactions, user behaviors, devices, and network signals, Ambriel uncovers hidden risk patterns and automates mitigation strategies, making it an essential tool for any business looking to safeguard its operations and maintain customer trust.

Features of Ambriel

Advanced Fraud Detection

Ambriel employs AI-driven risk scoring to identify suspicious activities in real time, leveraging data from over 200 sources. This feature ensures that businesses can protect their revenue before potential fraud cases escalate.

Sanctions & PEP Screening

With automated checks against over 100 global sanctions, Politically Exposed Persons (PEP), and crime lists, Ambriel streamlines compliance processes. This feature eliminates the need for manual checks, enabling businesses to meet regulatory requirements efficiently.

Continuous Monitoring

Ambriel provides 24/7 tracking of transactions and accounts, alerting organizations to unusual patterns before they develop into costly fraud cases. This proactive approach helps in maintaining a secure environment for both the business and its customers.

Seamless Onboarding

This feature allows businesses to customize onboarding flows with automated checks, ensuring that only trusted customers and sellers are allowed onto the platform. This not only enhances security but also improves the overall user experience.

Use Cases of Ambriel

Onboarding & Registration Fraud

Ambriel effectively detects fake accounts, synthetic identities, and bot-driven signups during the onboarding process. By identifying these threats early, businesses can prevent exploitation of their platforms.

Payment & Transaction Fraud

With real-time monitoring of transactions, Ambriel helps businesses spot anomalies, prevent chargebacks, and block fraudulent transfers. This vigilance protects revenue and fosters customer confidence in the payment process.

Bonus & Promotion Abuse

Ambriel combats multi-accounting, referral scams, and exploitation of promotional incentives. By ensuring fair rewards, businesses can maintain their promotional integrity and customer trust.

Claims & Payout Fraud

This use case involves flagging suspicious claims and payout attempts through automated risk scoring, particularly beneficial for fintech and insurance sectors. By automating this process, businesses can reduce losses from fraudulent claims.

Frequently Asked Questions

How does Ambriel ensure compliance with regulations?

Ambriel integrates automated sanctions and PEP screening, allowing businesses to stay compliant with global regulations without manual checks. This feature significantly simplifies the compliance process.

Can Ambriel help in preventing account takeover?

Yes, Ambriel identifies unusual login behaviors, device changes, and credential stuffing attempts, thus providing robust protection against account takeovers and ensuring customer security.

What industries can benefit from using Ambriel?

Ambriel is designed for a diverse range of industries including fintech, retail, eCommerce, iGaming, insurance, and cryptocurrency, making it a versatile solution for various businesses.

How quickly can I implement Ambriel?

Ambriel offers seamless onboarding processes that can be customized to fit your specific needs. Most businesses can quickly integrate Ambriel into their existing systems and start reaping the benefits in a short time frame.