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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.
Description
With the increase in payment channels, the avenues available for fraudsters have also expanded, raising the potential liabilities for banks significantly. The rise of real-time payments, Open Banking, and digital interactions only serves to escalate these issues further. Conventional anti-fraud measures struggle to effectively thwart payment fraud, as they typically depend on numerous static and reactive rules that are inadequate for identifying emerging fraud patterns and often generate excessive false alarms. By utilizing cutting-edge 3D artificial intelligence (3D AI) technology, the NetGuardians platform, known as NG|Screener, provides real-time surveillance of all bank payment transactions, enhancing fraud detection while minimizing false positives. This advanced system pinpoints suspicious payments linked to social engineering tactics or scams, such as invoice redirection, romance fraud, and CEO impersonation, while also correlating these incidents with indicators of digital banking fraud, including eBanking and mBanking sessions compromised by malware or takeover fraud stemming from identity theft. As the financial landscape continues to evolve, innovative solutions like NG|Screener are essential for safeguarding banks and their customers against increasingly sophisticated fraud threats.
API Access
Has API
API Access
Has API
Integrations
No details available.
Integrations
No details available.
Pricing Details
$1,400 one-time payment
Free Trial
Free Version
Pricing Details
No price information available.
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
MedCXO
Country
United States
Website
medcxo.com/fraud/
Vendor Details
Company Name
NetGuardians
Founded
2007
Country
Switzerland
Website
www.netguardians.ch/
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