As criminals find new ways to use technology to focus on potential victims, anti-fraud professionals must embrace new technologies to effectively navigate the evolving threat landscape.
But which technologies are best for managing fraud risk?
Which tools provide benefits that outweigh the costs?
Answers to questions like these are key to gaining management buy-in and successfully implementing new anti-fraud technologies. This benchmarking study helps organizations understand what anti-fraud technologies their peers are using and provides guidance for future adoption of anti-fraud technologies.
Technology Comparison Report
Which technologies are best in helping organizations manage fraud risk? How are organizations successfully leveraging the power of data and technology as part of their anti-fraud programs? Answers to these and other questions can be critical to gaining management buy-in and successfully implementing new anti-fraud technologies. Subsequently, ACFE conducted a benchmarking study to assist organizations understand what anti-fraud technologies their peers are using and to help guide future anti-fraud technology deployments. We hope that the knowledge contained in the Anti-Fraud Technology Benchmarking Report will help organizations effectively evaluate anti-fraud technologies to stay one step ahead of potential fraudsters.
Fraud and risk management
You take care of the business, we make sure of the scammers
Our fraud and risk management solutions can facilitate you expedite good transactions and block bad ones so you can focus on your business.
Simplify fraud detection with automatic risk assessment for every transaction
But how are you able to tell if the solution you’re using is up to the task? And what really drives the accuracy of a machine learning model’s risk score?
Advanced ML-driven risk score
Use historical customer identity information across industries and AI to create a highly accurate fraud score in less than a second.
Continuous adjustment of models
It continuously analyses and processes new data, then updates risk models to reflect the newest trends and quickly adapt to market conditions.
Automated risk strategies
It continuously optimizes and strengthens our risk models using high-quality data and high processing speed to automate fraud management from day one.
Robust data intelligence
Access to 141 billion Vianet transactions and elements from TC40 data provides a worldwide view of emerging fraud trends and automatically identifies good customers.
Rather than relying solely on a single statistical algorithm, CyberSource combines several different methods to leverage the unique strengths of every risk model and apply the best risk model to each transaction.
Based on Visa’s AI platform and enhanced with access to visa Net, one among the world’s largest single sources of transaction data, CyberSource’s machine learning generates highly accurate risk scores that it uses in real-time to automate fraud detection and identify good customers.
Focus on your business, we will handle fraud
For e-commerce businesses, staying before payment fraud is a difficult and ever-evolving battle. Fraudsters are becoming smarter, bolder and more sophisticated per annum – developing new tactics and techniques that quickly reduce the effectiveness of static anti-fraud measures. For more information, download our white book.
It allows you to receive more good orders from new customers
As a core a part of our machine learning, we use historical customer identity information across merchants and automate fraud detection by analysing one among the largest networks of transaction data.
We understand identities more effectively and track how they’re used over time – enabling you to receive more good orders from new customers while providing agile and accurate risk assessments focused on industries, regions and payment methods.
Find out how CyberSource’s ML-based solutions can help you
Accept more good transactions and streamline online shopping from the beginning Lower fraud management costs by reducing false positives and manual checks Allow you to focus on improving core business strategies and further optimize revenue
Unified Consortium Model – Decision Manager’s: Identity Behaviour Analysis Rules Suggestion Engine
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