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What is Fraud Barrier?

To reduce the incidence of bad debts and the related costs of collection and recovery efforts, it is vital to avoid granting risk segments to applicants who provide false information on their applications. Keeping losses from serious fraud and write-offs stemming from fraudulent applicants to a minimum is essential. Moreover, it is important to ensure that the processes for detecting fraud do not compromise customer service or delay decision-making. This entails closely examining suspicious cases, analyzing the outcomes of application assessments, and making well-informed decisions. By automating fraud detection and investigation processes, organizations can significantly boost their operational efficiency. Ensuring that interfaces are user-friendly is crucial for maintaining low resource demands and minimizing operational expenses. Furthermore, the system should be designed to automatically assign cases for deeper investigation and generate a fraud likelihood score to prioritize subsequent actions. Adopting these strategies will ultimately enhance the effectiveness of fraud management, leading to improved financial stability. By fostering a proactive approach, businesses can better safeguard their operations against fraudulent activities.

What is Eastnets PaymentGuard?

Utilizing an extensive analysis of historical data, PaymentGuard effectively detects and prevents fraudulent transactions. The platform harnesses sophisticated machine learning algorithms to scrutinize a vast array of customer data, including transaction specifics, device details, and geographical information, which facilitates the recognition of both existing and emerging fraud trends. By identifying these trends, the system is capable of predicting potential threats in real-time and promptly dispatching alerts that can be addressed through a user-friendly case management interface. This capability allows for the swift evaluation, forecasting, and disruption of any dubious payment activities without causing delays. PaymentGuard boasts a wide-ranging and regularly updated library of threat models that significantly improve the accuracy of fraud detection. Its intuitive case management system equips investigators with the tools necessary to respond to fraud incidents quickly and efficiently. In an ever-evolving industry, we strive to keep you ahead of the curve. Our dedication to pioneering technologies such as Blockchain, artificial intelligence, and cloud computing further strengthens the security and progression of our services, ensuring they meet your evolving requirements. Furthermore, our proactive strategy in technology integration guarantees that our clients are well-prepared to navigate the constantly shifting terrain of payment fraud prevention, enabling them to maintain a robust defense against emerging threats.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

API Availability

Pricing Information

Pricing not provided
Free Trial Offered?

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Not specified

Training Options

Documentation Hub
Online Training

Company Facts

Organization Name

Scorto

Date Founded

2001

Company Location

United States

Company Website

www.scorto.com

Company Facts

Organization Name

Eastnets

Company Location

United States

Company Website

www.eastnets.com/products/paymentguard

Categories and Features

Fraud Detection

Access Security Management
Custom Fraud Parameters
For Banking
For Insurance Industry
Internal Fraud Monitoring
Investigator Notes
Pattern Recognition
Transaction Approval

Categories and Features

AML

Behavioral Analytics
Case Management
Compliance Reporting
Identity Verification
Investigation Management
Risk Assessment
Transaction Monitoring
Watch List

Fraud Detection

Not specified

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