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Description
IBM Safer Payments empowers organizations to design tailored, intuitive decision models that allow for quicker adaptation to new threats and enhanced fraud detection with improved accuracy and speed, all while eliminating the need for external vendors or data scientists. This solution greatly speeds up the optimization of modeling by offering the necessary analytics and simulation tools for ongoing business performance monitoring and adjustments to evolving fraud patterns. Clients experience impressive detection rates coupled with minimal false positives after integrating our system into their operations. Users can construct, evaluate, validate, and implement machine-learning models in just days instead of months, freeing them from vendor dependencies. The platform can process thousands of transactions every second, ensuring an enterprise-level solution that boasts 99.999% uptime and exceptional throughput. Its open architecture allows for the importation of detection models, model elements, and intellectual property, all while providing a comprehensive interface for developing new models. Additionally, it supports a wide range of data science, machine learning, or artificial intelligence methodologies, making it a versatile tool for any organization looking to enhance their payment security. Ultimately, this flexibility ensures that businesses can stay ahead of potential fraud threats more effectively than ever before.
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
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
IBM
Founded
1911
Country
United States
Website
www.ibm.com/products/safer-payments
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