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What is TOMIA?

To effectively manage financial risks and safeguard their reputations, it is essential for operators to address fraud in real-time, particularly in relation to roaming and international calls. Although many telecom providers have set up various preventative measures against fraud, the advent of new technologies reveals further weaknesses that require attention. The integration of defenses against these emerging threats is often a gradual process, causing operators to shift from conventional offline methods to leveraging network enforcement capabilities that can immediately stop fraudulent calls in progress. Systems based on Call Detail Records (CDRs), which depend on successful call logs, tend to neglect failed call attempts, which confines their effectiveness to a more reactive approach. Consequently, there is a growing urgency for operators to take proactive steps to tackle fraudulent activities throughout every phase of the calling process. For example, by monitoring the volume of call attempts made to known black-listed numbers, operators can effectively identify and intercept PBX hacked devices, as fraudsters often cycle through numerous numbers before achieving a successful connection. Additionally, this forward-thinking strategy allows for the detection of behavioral patterns that may signal larger fraud operations, thereby bolstering overall security measures. As fraud continues to evolve, the need for innovative solutions becomes increasingly vital for the telecommunications industry.

What is Rippleshot?

Rippleshot is a company dedicated to fraud detection and prevention, leveraging cutting-edge artificial intelligence and machine learning methods to aid financial institutions in proactively identifying and managing card fraud. Their flagship product, Sonar, analyzes millions of card transactions daily, effectively identifying compromised merchants and at-risk cards, which enables swift and accurate responses to potential fraud incidents. In addition, Rippleshot offers an AI-driven tool that allows financial institutions to implement effective fraud prevention strategies without needing extensive IT resources. By adopting these state-of-the-art solutions, banks and credit unions can substantially reduce losses related to fraud, minimize unnecessary card replacements, and enhance the overall experience for their cardholders. Through innovative, cloud-based technology that harnesses machine learning and data analytics, Rippleshot is transforming how financial institutions approach fraud detection, making it quicker and more efficient to uncover fraudulent activities. Their ongoing dedication to utilizing advanced technology not only helps institutions stay ahead in the relentless fight against fraud but also strengthens the trust and security associated with financial transactions, ultimately benefiting consumers and financial entities alike. As the landscape of fraud continues to evolve, Rippleshot remains committed to refining its offerings, ensuring that financial institutions are well-equipped to tackle emerging challenges effectively.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

API Availability

Pricing Information

Pricing not provided

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

Documentation Hub
Online Training
On-Site Training

Training Options

Documentation Hub
Online Training
On-Site Training

Company Facts

Organization Name

TOMIA

Date Founded

1999

Company Location

United States

Company Website

tomiaglobal.com/real-time-anti-fraud-raf

Company Facts

Organization Name

Rippleshot

Date Founded

2013

Company Location

United States

Company Website

www.rippleshot.com

Categories and Features

Fraud Detection

Not specified

Categories and Features

Fraud Detection

Not specified

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