Average Ratings 26 Ratings
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Description
Frogo delivers a comprehensive fraud prevention platform powered by AI, designed to protect organizations across multiple sectors such as iGaming, financial services, payments, e-commerce, and logistics. Its system monitors user behavior and transaction activity in real time to detect suspicious patterns like brute force logins, unauthorized promo code activations, chargebacks, BIN attacks, or affiliate manipulation. With flexible rule-based scoring, businesses can create or adjust fraud detection policies tailored to their unique risk profiles. Frogo’s multi-layered approach combines static and dynamic rules with predictive AI models, ensuring that both known and emerging fraud schemes are intercepted. The platform provides detailed analytics, customizable alerts, blacklists/whitelists, and investigation modules to empower fraud teams with actionable intelligence. It can also be configured for unique fraud cases, enabling industry-specific defenses. Companies benefit from reduced chargebacks, improved customer trust, and optimized revenue streams by stopping fraud before it causes significant damage. Backed by ISO27001 certification, Frogo ensures compliance, data security, and reliability for enterprises handling sensitive financial and personal information.
Description
Benford's law serves as a tool for uncovering patterns indicative of improper disbursements. It involves examining audit trail reports from QuickBooks or other bookkeeping software to pinpoint unusual activities like voids and deletions. Additionally, it entails identifying multiple payments made for identical amounts on the same day. A thorough review of payroll runs is conducted to detect any payments exceeding the established salary or hourly rates. Payments made on non-business days are also scrutinized. Statistical calculations help in identifying outliers that may suggest fraudulent activity, and duplicate payments are tested for validation. Vendor files in accounts payable are analyzed for names that may be suspiciously similar, and investigations are conducted to uncover fictitious vendors. Comparisons of vendor and payroll addresses are evaluated using Z-Scores and relative size factor tests. While data monitoring and surprise audits have shown to significantly reduce fraud losses, only 37% of organizations implement these critical controls. For businesses employing fewer than 100 individuals, the average loss due to fraud is estimated at $200,000, highlighting that smaller enterprises often lack the necessary resources to effectively detect and address fraudulent activities. Consequently, it is essential for small businesses to adopt more robust fraud detection mechanisms to safeguard their financial integrity.
API Access
Has API
API Access
Has API
Screenshots View All
No images available
Integrations
No details available.
Integrations
No details available.
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
$1,400 one-time payment
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Frogo.ai
Founded
2025
Country
Cyprus
Website
frogo.ai/
Vendor Details
Company Name
MedCXO
Country
United States
Website
medcxo.com/fraud/
Product Features
Fraud Detection
Access Security Management
Check Fraud Monitoring
Custom Fraud Parameters
For Banking
For Crypto
For Insurance Industry
For eCommerce
Internal Fraud Monitoring
Investigator Notes
Pattern Recognition
Transaction Approval
Product Features
Fraud Detection
Access Security Management
Check Fraud Monitoring
Custom Fraud Parameters
For Banking
For Crypto
For Insurance Industry
For eCommerce
Internal Fraud Monitoring
Investigator Notes
Pattern Recognition
Transaction Approval