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features
design
support

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Description

Fraud Detection Modern fraud prevention systems rely on documenting issues and establishing expert-defined rules to combat them; however, due to the vastness and ever-changing nature of fraud, this approach is often inadequate. POINTER introduces a forward-thinking strategy that swiftly identifies fraudulent activities by recognizing transactions that deviate from a user’s usual behavior. Utilizing a proprietary mathematical model along with advanced data mining methodologies, POINTER generates a dynamic Personal Symbolic Vector. Additionally, the incorporation of auto-learning techniques allows the system to adapt to realistic behavioral changes over time. This adaptability guarantees that user profiles remain current and that the most recent expert rules are applied for fraud detection. Furthermore, POINTER significantly reduces the number of false alerts, thereby minimizing the research time required by staff while simultaneously enhancing customer service. Institutions can also customize these parameters to achieve the most effective balance for their specific needs. Ultimately, this innovative approach revolutionizes the way organizations manage and mitigate fraud.

Description

Tackle advanced location fraud tactics that evade current GeoIP tools, including account takeovers and bot attacks. Boost your detection capabilities for genuine fraud while minimizing both false positives and false negatives by incorporating integrated location verifications. Upgrade your existing fraud and risk management frameworks by utilizing sophisticated location data signals. The system identifies intricate location spoofing techniques such as VPNs, data centers, anonymizers, proxies, and Tor exit nodes. Additionally, it recognizes when smartphone “home” location information is falsified during the creation of synthetic identities. It also supplies historical location data for devices or users to support chargeback dispute resolutions. Furthermore, it generates a unique fingerprint for each identified method of location fraud, enabling you to flag future transactions exhibiting similar patterns. This comprehensive approach not only strengthens defenses but also enhances overall security measures against evolving fraud tactics.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

No details available.

Integrations

No details available.

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs No 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

PotentiaN

Founded

2007

Country

United States

Website

www.potentian.com

Vendor Details

Company Name

GeoComply

Founded

2011

Country

Canada

Website

www.geocomply.com/products/risk-guard/

Product Features

Fraud Detection

Access Security Management Yes 
Check Fraud Monitoring No 
Custom Fraud Parameters Yes 
For Banking Yes 
For Crypto No 
For Insurance Industry Yes 
For eCommerce No 
Internal Fraud Monitoring Yes 
Investigator Notes Yes 
Pattern Recognition Yes 
Transaction Approval Yes 

Product Features

Fraud Detection

Access Security Management No 
Check Fraud Monitoring No 
Custom Fraud Parameters No 
For Banking No 
For Crypto No 
For Insurance Industry No 
For eCommerce No 
Internal Fraud Monitoring No 
Investigator Notes No 
Pattern Recognition No 
Transaction Approval No 

Alternatives

Alternatives

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