MLGraph: Develop linkage based rules/models to stop fraud in its tracks

Activate Linkage-Based Rules and Models for Enhanced Fraud Detection Exactly When It Counts: During New Account Setups, Money Transfers, and Account Access Events.

Unveil the reality: fraudsters strike methodically, targeting multiple accounts in synchronized assaults. Yet, why confront them one by one?

Harness the power of graphs to stop fraud at its inception. Act now to safeguard your assets with unparalleled insight.    

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Empowering Insights Through Link Analytics

ML-Graph stands at the forefront of fraud detection and mitigation, leveraging cutting-edge graph analysis and machine learning techniques to combat a spectrum of fraudulent activities. Through its dynamic graph-enabled rules, ML-Graph excels in identifying and addressing instances of first-party abuse, swiftly detecting anomalies and deviations in user behavior indicative of potential fraud.

Furthermore, ML-Graph's advanced capabilities extend to robust bot detection, enabling the identification and mitigation of automated fraudulent activities across diverse platforms.

Additionally, ML-Graph's innovative approach to fraud ring identification and mitigation empowers organizations to uncover and disrupt sophisticated networks orchestrating fraudulent schemes. By proactively analyzing interconnected data points and detecting patterns indicative of fraudulent behavior, ML-Graph ensures comprehensive protection against a myriad of fraud threats, safeguarding the integrity of businesses and their customers.
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Linkage based rules and Negative Lists

Linkage-based rules and Negative Lists are powerful tools for fraud detection. Linkage-based rules analyze data patterns to identify fraud, while Negative Lists flag known fraudulent activities in real-time. Together, they provide robust protection against fraud, ensuring secure business operations.

Graph Databases not needed

No need for complex Graph DB or additional languages. ML-Graph seamlessly integrates with your existing tech stack, including SQL, Oracle, and more, streamlining implementation and ensuring compatibility with your current infrastructure.

150+ Graph metrics and custom business metrics

We offer a comprehensive suite of over 150 graph metrics and custom business metrics that serve as the cornerstone for encoding the intricate structure of your graph data.  Leveraging these metrics as the starting point, our graph-based models are built using supervised learning techniques, ensuring accurate and effective predictions tailored to your specific business needs

Real-Time Execution

Connect entities real time, this enables Graph based rules at top of the funnel during UW vs case reviews during collection/escalations

Unlock the power of Graph Aware Rules, Models, and Blacklist activation without the need to transfer massive data to graph databases.

Stay steps ahead of fraudsters with our revolutionary Graph-Aware Rules – the frontline defense your business needs to detect and prevent fraudulent activities before they escalate.
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Frequently Asked Questions

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We specialize in resolving fraud and risk challenges with our deep expertise. Schedule a complimentary discovery call today to explore how we can help safeguard your business.
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What is ML-Graph, and how does it revolutionize data analysis and decision-making processes across industries?

ML-Graph is an innovative platform that combines machine learning algorithms with graph analysis techniques to unlock valuable insights from interconnected data. By leveraging the power of graphs, ML-Graph enables organizations to uncover complex relationships, patterns, and dependencies within their data, leading to more informed decision-making and actionable insights. Whether it's detecting fraud, optimizing supply chains, or personalizing customer experiences, ML-Graph offers a versatile solution that empowers businesses to extract maximum value from their data assets.

How does ML-Graph solutions leverage existing SQL databases to develop linkage-based models and rules for risk management in fintech?

Yes, analysts can develop linkage-based rules and models within ML-Graph solutions without requiring a dedicated graph database. ML-Graph leverages the capabilities of existing SQL databases to build graph edges, enabling analysts to focus on model development without the overhead of managing additional infrastructure.

How does ML-Graph empower the development of linkage-based rules to combat systematic fraud ring attacks in fintech?

ML-Graph empowers fintech organizations to develop linkage-based rules specifically designed to combat systematic fraud ring attacks. By analyzing interconnected data points and identifying suspicious patterns, ML-Graph enables the creation of rules that can effectively detect and mitigate fraudulent activities orchestrated by organized fraud rings.

What are the key advantages of using linkage-based rules developed with ML-Graph to detect and prevent systematic fraud ring attacks?

ML-Graph enhances fraud detection capabilities by leveraging linkage-based rules to identify complex fraud patterns that may otherwise go unnoticed. By analyzing interconnected data points and detecting anomalies indicative of fraudulent behavior, ML-Graph enables organizations to proactively detect and prevent fraud in real-time.

Tell me about some of the success stories?

As a patented solution, ML-Graph is not only actively deployed in production across both fintech startups and Fortune 500 enterprises but also stands as a formidable force in combating a myriad of fraudulent activities. From addressing instances of first-party abuse to stopping sophisticated botnet attacks, ML-Graph's advanced machine learning algorithms and network analysis techniques offer a resounding response to the diverse challenges posed by fraud across industries.

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