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What is T-NEX Fraud Detection?

T-NEX Fraud Detection is a holistic solution designed for transaction monitoring and case management, specifically crafted for banks, insurance firms, and other regulated industries. It utilizes customizable rules to scrutinize incoming transactions, compiles supporting evidence, and merges related alerts into cohesive customer cases for fraud and anti-money-laundering teams to scrutinize. The platform boasts AI-assisted triage functionality, which generates a risk score, provides rationale, and suggests actions, while meticulously tracking model and prompt versions for future reference. Analysts interact with customer context and evidence, making informed decisions to either assign or escalate cases, document their conclusions, and prepare reports as needed. Furthermore, the system facilitates backtesting of rules against historical transaction data prior to their deployment. A comprehensive audit trail records all actions and their justifications, guaranteeing that human analysts retain the ultimate authority in decision-making, as the system lacks the ability to block payments or cards. This careful incorporation of AI into the review process showcases T-NEX's distinctive HUMAINOLOGY methodology. With a collective experience exceeding two decades in governance, risk, and compliance software, the T-NEX team is highly capable of addressing intricate fraud-related issues. Their unwavering commitment to innovation allows the platform to adapt and respond effectively to the ever-changing regulatory requirements, ensuring ongoing relevance and efficacy. The integration of advanced technology and expert insights sets T-NEX apart as a leader in the field.

What is Fraugster?

Minimize instances of fraud while boosting revenue with Fraugster's cutting-edge AI-powered fraud protection solutions. Our advanced technology enables accurate real-time decision-making that significantly improves performance, business outcomes, and customer satisfaction. With Fraugster, retailers can efficiently authenticate customer identities and assess fraud risk at every phase of the customer journey, starting from account creation all the way through to the final checkout. Our diverse array of fraud prevention solutions is easily available via a REST API, allowing you to choose the option that best meets your needs and receive prompt decisions in just 15 milliseconds. By employing a behavioral science-based methodology, our distinctive AI replicates the thought processes of human analysts while leveraging machine scalability. This guarantees that legitimate customers experience a hassle-free process, while potentially fraudulent transactions are effectively identified and blocked. Adopting Artificial Intelligence is vital for the future of fraud prevention, and with Fraugster's FraudFree, merchants can significantly improve their operational efficiency and safeguard their revenue even more. By relying on our innovative solutions, businesses can confidently navigate the intricate landscape of the digital market, ensuring a safer environment for both themselves and their customers. This approach not only builds trust but also fosters long-term loyalty among consumers.

Media

Media

Integrations Supported

Integrations Supported

Payaut

API Availability

API Availability

Has API

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

Not specified

Supported Platforms

SaaS

Customer Service / Support

Not specified

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub
Online Training
On-Site Training

Company Facts

Organization Name

T-NEX

Date Founded

2024

Company Location

Germany

Company Website

t-nex.de/en/institutions/products/fraud-detection/

Company Facts

Organization Name

Fraugster

Date Founded

2014

Company Location

Germany

Company Website

www.fraugster.com

Categories and Features

Fraud Detection

Not specified

Categories and Features

Fraud Detection

Not specified

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