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Description
Dregs offers fraud detection solutions specifically designed for SaaS applications. By utilizing a lightweight JavaScript tracking script, it fingerprints devices and records user interactions, scoring each identity based on four key dimensions: Humanity (detection of bots and automation), Authenticity (identification of fake profiles and temporary email addresses), Uniqueness (detection of duplicate accounts linked via shared devices, IP addresses, and sessions), and Behavior (comparison of user activity against expected behaviors).
The scoring system is transparent, derived from individual observations by the analyzers that can be reviewed and adjusted, rather than relying on a vague risk score. Alerts can be configured to notify teams via a dashboard, email, Slack, or webhooks, and the implementation process is structured in four stages — Scoring, Alerts, Escalation, and Autonomy — ultimately allowing for automated responses such as rate limiting and account quarantining.
Dregs effectively addresses issues such as free-trial exploitation, fraudulent promotions and referrals, duplicate account creation, stolen credit card testing, SMS pumping, credential stuffing, and data scraping. Additionally, the setup process is user-friendly, featuring a self-serve model that includes a free trial and clearly outlined pricing options, making it accessible for teams to integrate into their operations. This comprehensive approach ensures that businesses can protect themselves against various forms of fraud effectively.
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
Integrations
JavaScript
Pricing Details
$17/month
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
Dregs LLC
Founded
2025
Country
United States
Website
dregs.com
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