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What is VALID Systems?

VALID Systems delivers a comprehensive platform enhanced by artificial intelligence for managing risks and preventing fraud, which is specifically tailored for financial organizations and consists of three crucial modules that address the wide range of modern threats. The Transactions module leverages machine-learning algorithms and rule-based systems to detect check fraud, evaluate risks linked to remote deposit capture, identify account takeovers, and recognize threats in digital channels in real-time, utilizing data consolidated from various consortiums to enhance accuracy and enable quick decision-making processes. The Liquidity & Lending module optimizes credit decision-making and oversight of overdraft risks, applying behavioral analytics alongside flexible underwriting models to refine small-dollar loans and credit offerings while balancing customer satisfaction with loss prevention strategies. In addition, the Data Intelligence module continuously evaluates model performance and shifts in data, ensuring transparency, identifying AI biases, and maintaining regulatory compliance through intuitive dashboards that deliver in-depth insights. By adopting this all-encompassing strategy, financial institutions are empowered to proactively address evolving risks and uphold trust with their customers, ultimately fostering a more secure banking environment. This integrated system not only mitigates potential threats but also enhances the overall efficiency of financial operations.

What is Censius AI Observability Platform?

Censius is an innovative startup that focuses on machine learning and artificial intelligence, offering AI observability solutions specifically designed for enterprise ML teams. As the dependence on machine learning models continues to rise, it becomes increasingly important to monitor their performance effectively. Positioned as a dedicated AI Observability Platform, Censius enables businesses of all sizes to confidently deploy their machine-learning models in production settings. The company has launched its primary platform aimed at improving accountability and providing insight into data science projects. This comprehensive ML monitoring solution facilitates proactive oversight of complete ML pipelines, enabling the detection and resolution of various challenges, such as drift, skew, data integrity issues, and quality concerns. By utilizing Censius, organizations can experience numerous advantages, including: 1. Tracking and recording critical model metrics 2. Speeding up recovery times through accurate issue identification 3. Communicating problems and recovery strategies to stakeholders 4. Explaining the reasoning behind model decisions 5. Reducing downtime for end-users 6. Building trust with customers Additionally, Censius promotes a culture of ongoing improvement, allowing organizations to remain agile and responsive to the constantly changing landscape of machine learning technology. This commitment to adaptability ensures that clients can consistently refine their processes and maintain a competitive edge.

Media

Media

No images available

Integrations Supported

Fiserv
Snowflake
TD Bank Merchant Solutions

Integrations Supported

API Availability

Has API

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided
Free Trial Offered?

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training

Training Options

Documentation Hub
Online Training

Company Facts

Organization Name

VALID Systems

Date Founded

2003

Company Location

United States

Company Website

validadvantage.com

Company Facts

Organization Name

Censius

Date Founded

2021

Company Location

United States

Company Website

censius.ai/

Categories and Features

Check Fraud

Not specified

Fraud Detection

Not specified

Categories and Features

AI Observability

Not specified

Machine Learning

Not specified

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