How a Smart System Organizes Fraud Detection Using Machine Learning
This patent describes a system that uses machine learning to analyze past transactions, group similar ones, and then automatically pick the best tool or service to check for fraud within those groups.
Original patent title: “System and method for a workflow orchestration platform for fraud detection”
This patent describes a system that uses machine learning to analyze past transactions, group similar ones, and then automatically pick the best tool or service to check for fraud within those groups. Granted in 2026.
Coverage
What does this patent actually cover?
The system acts as a smart organizer for fraud detection. It starts by taking in lots of past transaction information, called "historical transaction data." This data is then used to teach a "machine learning algorithm" how to spot patterns. Based on various details or "attributes" from these past transactions, the algorithm sorts them into groups, or "clusters." For each cluster, the system figures out which specialized tool or "backing application" (like an identity verification service or a specific fraud risk service) would be best to check for fraudulent activity. For example, if a cluster of transactions shows unusual international activity, the system might automatically route them to a backing application specialized in cross-border fraud analysis.
The gap
What does this patent NOT cover?
- Fraud detection systems that do not use machine learning to group or "cluster" historical transaction data.
- Systems that cluster transaction data but do not then automatically identify and use specific "backing applications" for those clusters.
- Workflow orchestration platforms that are not specifically designed for the purpose of fraud detection.
- Fraud detection methods that do not rely on historical transaction data for training their algorithms.
- Systems that only use simple rule-based logic for fraud detection without machine learning analysis.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The clever part is how the system uses machine learning to not just find patterns in transactions, but then automatically connect those patterns (the clusters) to the most appropriate specialized fraud-checking services, or "backing applications," for deeper analysis.
Schematic visualization of the patent's claim structure. Hand-drawn diagrams in progress for each landmark patent.
Where you've seen this
Real-world examples
Payment processing platforms
E-commerce fraud prevention suites
Financial institution risk management systems
Online banking security platforms
Why it matters
The bigger picture
Fraud costs businesses and consumers billions of dollars each year. This system matters because it aims to make fraud detection faster and more accurate by automating the process of analyzing transactions and applying the right specialized tools. By intelligently orchestrating different fraud services, it can help financial institutions and online businesses reduce losses and improve efficiency, allowing legitimate transactions to proceed more smoothly.
Filed
August 12, 2024
Granted
September 15, 2026
Market context
Who's building on this
Companies in this space
Companies deeply involved in payment processing and financial technology, such as Visa, Mastercard, Stripe, and PayPal, are constantly developing sophisticated fraud detection systems. Cybersecurity firms specializing in financial crime, like Sift, Forter, and Riskified, also build platforms that incorporate machine learning and workflow orchestration to combat fraud.
Market impact
This type of system aims to significantly improve the efficiency and accuracy of fraud detection across various industries. By automating the selection of specialized fraud services, it can reduce the need for manual review, speed up transaction processing, and ultimately lower financial losses due to fraud. It helps businesses manage risk more effectively and adapt to evolving fraud tactics.
Claim 1 — Plain English
What this patent covers
The system acts as a smart organizer for fraud detection. It starts by taking in lots of past transaction information, called "historical transaction data." This data is then used to teach a "machine learning algorithm" how to spot patterns. Based on various details or "attributes" from these past transactions, the algorithm sorts them into groups, or "clusters." For each cluster, the system figures out which specialized tool or "backing application" (like an identity verification service or a specific fraud risk service) would be best to check for fraudulent activity. For example, if a cluster of transactions shows unusual international activity, the system might automatically route them to a backing application specialized in cross-border fraud analysis.
The clever bit
The clever part is how the system uses machine learning to not just find patterns in transactions, but then automatically connect those patterns (the clusters) to the most appropriate specialized fraud-checking services, or "backing applications," for deeper analysis.
What it does not cover
- Fraud detection systems that do not use machine learning to group or "cluster" historical transaction data.
- Systems that cluster transaction data but do not then automatically identify and use specific "backing applications" for those clusters.
- Workflow orchestration platforms that are not specifically designed for the purpose of fraud detection.
- Fraud detection methods that do not rely on historical transaction data for training their algorithms.
- Systems that only use simple rule-based logic for fraud detection without machine learning analysis.
Patent timeline
Application submitted to the patent office
Patent officially issued
PatentBrief Score
Impact Score
Early stage
Citation count
0/40
No citations yet
Claim breadth
0/20
Narrow claimsclaimsThe numbered statements at the end of a patent that legally define what the inventor owns.Read more →
Recency
20/20
Granted within 5 years
Assignee scale
0/20
Independent or smaller assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →
PatentBrief Impact Score — based on citation count, claim breadth, recency, and assignee scale. Not a legal assessment.
Heuristic Value Estimate
What this patent might be worth
$19K – $61K
Midpoint $38K · 17.8 yr remaining · industry ×1.6
Heuristic only — blends forward/backward citation counts, claim scope, time remaining, litigation history, and CPC-derived industry baseline. Real valuations need a professional appraisal.
Claim text not yet imported for this patent
Concepts involved
Cite this patent
(2026). How a Smart System Organizes Fraud Detection Using Machine Learning (U.S. Patent No. 12,737,766). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/12737766/system-and-method-for-a-workflow-orchestration-platform-for-fraud-detection
Auto-generated from the patent record. Double-check author order and the issue date against the official USPTO document before submitting.
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Common Questions
Frequently Asked Questions
What does How a Smart System Organizes Fraud Detection Using Machine Learning cover?
This patent describes a system that uses machine learning to analyze past transactions, group similar ones, and then automatically pick the best tool or service to check for fraud within those groups.
When does this patent expire?
This patent is expected to expire on September 15, 2046, when the invention enters the public domain.
What problem does this patent solve?
Fraud costs businesses and consumers billions of dollars each year. This system matters because it aims to make fraud detection faster and more accurate by automating the process of analyzing transactions and applying the right specialized tools. By intelligently orchestrating different fraud services, it can help financial institutions and online businesses reduce losses and improve efficiency, allowing legitimate transactions to proceed more smoothly.
What does this patent NOT cover?
Fraud detection systems that do not use machine learning to group or "cluster" historical transaction data.
Patent monitoring