Leverage the power of unsupervised machine learning to uncover correlated patterns and reveal hidden connections between accounts before sophisticated fraud attacks can launch.
dCube is the complete AI-powered fraud management solution that enables the proactive defeat of emerging fraud.
Get detailed, real-time fraud signals produced through advanced contextual detection and holistic analysis, and take proactive steps to defeat both known and unknown fraud.
This is part one of a three-part blog post series highlighting some of the key things to look for when it comes to choosing a third-party fraud prevention solution. In this post, we go over topics such as multi-layer protection, target use cases, global reach and data, etc.
Accelerate feature engineering, leverage global intelligence from more than 4.2B user accounts, and draw on advanced deep learning features to increase coverage and reduce false positives.
First in a three-part series focusing on fraud modeling. The series covers pre-modeling, modeling, and post-modeling.
Combine sophisticated out-of-box features and advanced AI and machine learning-enriched features to build powerful rulesets for comprehensive fraud detection.
Leveraging the power of machine learning to build intelligent solutions that empower organizations to proactively defend their businesses, their customers, and their data. There are many ways to understand AI and machine learning, and as…
Stop application and transaction fraud, account takeover, money laundering, and more.
Learn how leading financial institutions are using ML to proactively detect card application fraud.
Build and maintain trust by stopping fraud before reputational damage occurs.
Every company is different, and every attack is different. When it comes to defeating fraud, success is determined organization by organization. From mass registrations and fake listings, to ATO and spam, to promo abuse and bot attacks,…
Keep your platform safe and secure by purging spam and harmful posts.
Understand the range of modern fraud attacks to ensure complete coverage for your organization.
Eliminate fraud losses and provide great experiences to loyal customers by proactively detecting and preventing promotion abuse, bot attacks, account takeover, and more.
Read this case study to learn how DataVisor detected hundreds of thousands of fake accounts with 99.5% accuracy.
Ensure platform safety, protect good customers, and reduce fraud losses.
When a top delivery services company that processes over 6 billion packages annually succumbs to mass registration attacks, it’s a complex problem that demands an advanced solution. Fortunately, using dVector from DataVisor, the company…
Prevent financial loss from fraudulent subscribers, protect against ATO, and stop spam and phishing attacks.
5 stories. 5 victories against fraud. See how organizations across industries are proactively defeating attacks.
Detect fraudulent claims and applications, spot collusion, and prevent dishonest agents from deceiving insurers.
Uncover and block fraudulent reservations in real time, prevent losses from loyalty program fraud, and protect good customers.
As malicious bot attacks become more sophisticated, one airline is fighting back.
Leverage proprietary unsupervised machine learning technologies to proactively detect fast-changing attack patterns and capture entire fraud rings before financial loss occurs.
Detect transaction fraud in real time to prevent financial loss, and allow legitimate transactions to go through with no friction.
Discover the key to implementing AI-powered solutions that live up to the hype.
Enhance detection and fluidly adapt to new and evolving money-laundering tactics. Maintain compliance with full transparency and explainable results.
Detect, deter, and defeat known and known money-laundering attacks.
Analyze context and linkages to discover fraudster activity early, at the registration stage. Create frictionless experiences for good customers, and purge damaging content.
Fake listings and mass registrations were wreaking havoc on a global online marketplace that operates in 40+ countries and has over 350 million monthly active users. With their reputation in jeopardy and their good customers leaving, the…
Detect and prevent sophisticated ATO attacks before damage happens. Eliminate false positives, and consistently deliver friction-free experiences to good customers.
Predict and prevent suspicious activity, reduce overhead and boost review efficiency, and block fake accounts at the gate to protect good customers throughout the lifecycle of their journey.
Protect your platform and your users from spam, scams, and toxic content. Ensure extraordinary experiences for good customers by maintaining a fraud-free environment.
A top U.S-based online reviews platform with over 170 million total reviews was suffering severe financial and reputational damage due to massive amounts of fake reviews and spam. The company was incurring increased compliance risk, and…
Get reliable results with high accuracy, boost efficiency, and implement scalable solutions to cover multiple use cases. Match the speed, scale, and complexity of modern, bot-powered fraud.
Consistently produce reliable results with high accuracy, capture fraudulent accounts at the point of registration, and implement scalable solutions for multiple regions and businesses. Ensure that valuable promotions reach real customers, and power ongoing business growth.
Discover all the ways our customers are staying ahead of fraud by embracing AI-powered solutions that enable their organizations to know the unknown.
Get experts insights on how to deploy cutting-edge fraud solutions to defeat even the most sophisticated modern attacks.
Discover advanced strategies for managing the rapidly-evolving fraud attacks plaguing the modern banking sector.
Delve deep into proprietary research to ensure your organization stays ahead of malicious threats.
The DataVisor Q2 2019 Fraud Index Report is here.
Learn from leading experts in the fields of AI, machine learning, and fraud prevention, as they provide rich insights on fraud trends and solutions.
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The DataVisor approach combines cutting-edge AI and machine learning technologies to correlate fraudulent and suspicious patterns across billions of accounts in real time. Patented and proprietary unsupervised machine learning (UML) algorithms work without labeled input data to automatically detect new and previously unidentified fraud and abuse patterns.
What is UML?
The DataVisor Unsupervised Machine Learning Engine processes all events and account activities simultaneously to analyze patterns across hundreds of millions of accounts. This enables detection of suspicious connections between malicious accounts, even when those accounts are incubating, mimicking legitimate user activities, or changing attack techniques. This also allows the UML Engine to detect all the members of an attack ring at once, ensuring the attack is fully stopped.
At the core of the platform is DataVisor’s UML Engine that combines clustering techniques with graph analysis to discover correlated fraudulent and suspicious patterns from unlabeled data.
The DataVisor Platform, from computation to data access and storage, is built on the latest big data infrastructure stack. Apache Spark, HDFS, Hadoop, Apache HBase and Elasticsearch are all being used to support the system in different capacities.
The DataVisor Global Intelligence Network leverages deep learning technologies to provide real-time, comprehensive digital intelligence based on the industry’s widest set of digital data.
The DataVisor UML Engine is designed to operate in batched and real-time modes. While they are built using the same algorithms, they are designed for different optimization goals. The batch system targets maximal coverage and accuracy, while the real-time system also addresses latency and throughput requirements.
Explore the latest in fraud intelligence.
An overview of how to choose between supervised and unsupervised ML.
As attacks grow in scale and velocity, businesses are forced to evolve their fraud detection methods from manual detection involving blacklists and rule engines to machine learning algorithms that can detect known and emerging types of…