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COINTURK FINANCE > Business > Galileo Unveils Anti-Fraud Tools for Financial Services
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Galileo Unveils Anti-Fraud Tools for Financial Services

Overview

  • Galileo launched two new anti-fraud tools for financial institutions.

  • Tools include real-time verification and machine learning capabilities.

  • Focuses on proactive and reactive fraud prevention strategies.

COINTURK FINANCE
COINTURK FINANCE 1 year ago
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Galileo Financial Technologies has announced the launch of two innovative anti-fraud tools aimed at banks, FinTechs, and businesses. These tools address the growing needs for real-time fraud detection and risk management services in a landscape increasingly dominated by digital transactions and evolving cyber threats.

Contents
Real-Time Verification and Risk ManagementMachine Learning for Enhanced Security

Galileo previously introduced other fraud detection solutions, but the latest offerings bring a more advanced and integrated approach. The focus has shifted from simple fraud detection to a comprehensive system that includes machine learning and real-time data analysis. This evolution in Galileo’s strategy aligns with broader industry trends where financial institutions are incorporating more sophisticated technologies to combat fraud.

Real-Time Verification and Risk Management

The first tool, Galileo Instant Verification Engine (GIVE), offers real-time verification of external bank accounts and ownership. GIVE can operate independently or integrate with Galileo’s payment risk platform, providing businesses the capability to stop fraud by verifying account details before transactions are processed. This integration enhances GIVE’s efficiency through the sophisticated Transaction Decision Rules Engine, which applies customizable models, rules, and profiles to automate fraud prevention decisions based on each business’s specific needs and risk appetite.

“GIVE is further enhanced by its integration with the Galileo Payment Risk Platform, which uses a sophisticated Transaction Decision Rules Engine,” the company stated. “This engine applies customizable models, rules and profiles to each transaction, allowing for real-time, automated fraud prevention decisions tailored to the specific needs and risk appetite of each business.”

Machine Learning for Enhanced Security

The second tool, Transaction Risk GScore, employs machine learning to assess the risk of card transactions in real time. This tool adds an additional layer of fraud signals to the payment risk platform, enabling customers to determine their risk appetite based on multiple model responses. According to Galileo, this machine-learning-based approach fundamentally changes how financial institutions can proactively defend against fraud.

“The client experience drives the engagement, and utilization of [banking] apps and programs,” Max Spivakovsky, senior director of strategy and operations, global payments risk management and onboarding at Galileo, mentioned.

Spivakovsky emphasized that financial institutions must adopt both proactive and reactive approaches in tackling fraud while providing a personalized customer experience. He noted that legacy solutions are becoming obsolete, stressing the need for a holistic perspective in fraud prevention strategies. The transition from banks maintaining all processes in-house to relying on FinTechs for real-time fraud prevention highlights this shift.

These new tools by Galileo represent a significant step toward more efficient and adaptive fraud prevention methods. As digital transactions continue to rise, financial institutions must stay ahead of cyber threats with advanced technologies. The integration of machine learning and real-time data analysis into fraud prevention provides robust security measures, helping to safeguard financial institutions from both financial and reputational risks.

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Disclaimer: The information contained in this article does not constitute investment advice. Investors should be aware that cryptocurrencies carry high volatility and therefore risk, and should conduct their own research.

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