TAZI Claim Fraud Detection Solution™
TAZI AI Systems, Inc. · Intelligence & Research
Certification per Microsoft Marketplace.
Evidence tier Source Confirmed · 7 captures on record
What the publisher says
As described on Microsoft Marketplace.
Detect Fraud Patterns, Prevent Loss of Wealth.
Detection of fraud patterns and root causes in auto insurance claims is not easy. Fraud exists, but it is time-consuming and difficult to identify what is fraudulent. Using batch machine learning, new fraud cases are impossible to catch. Many different variables need to be used together for effective fraud detection.
Show the rest of the publisher’s description (11 more lines)
Using TAZI patented technologies, spend less time on clean claims, detect possible fraudulent claims more effectively.
Detecting Continuously Changing Fraud Patterns
Continuous Learning for taking the right preventive actions at the right time with a robust continuously learning AutoML platform.
Reducing False Alarms
You can allow your knowledge of the industry to be integrated into machine learning through our Learning from Human functionality, which allows to adopt dynamic changes with domain expert’s feedback in addition to continuous learning capability. Experts can explore and correct the fraud model. Claims managers spend less time on clean claims and can concentrate more on the suspects.
Learn Root Causes of Fraudulent Transactions
Understandability for interactive and actionable explanations to expert teams. Auto Insurance professionals have access to visual sunbursts that allow them to understand which claim and why is fraudulent and take specific actions that will be logged in the system.
Business Usable AutoML Solution
Auto-Insurance professionals can now use machine learning safely and easily. You can build your models in minutes or integrate your existing Python models to further boost performance and your team’s efficiency. Also, customizable ML Dashboards allows each user to keep track of their key indicators.
All this built on self-maintaining algorithms, allowing accuracy to be maintained over time with no need for costly maintenance.
At a time when we are surrounded by uncertainty, let TAZI Claim Fraud Detection help you to prevent loss of wealth.
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Compliance
- FedRAMPConfirmedNot listed90%, registry-checkedNo FedRAMP Marketplace entry matched this vendor's domain, checked 2026-08-27registry recordas observed 2026-08-27
Confirmed means matched to a public authoritative registry. Claimed means the vendor or its listing states it, not yet cross-checked. A framework not shown was not found in any source we hold, which is not evidence against it. Not listed means a scoped registry check found no match for this vendor's domain: a No is a scoped registry check, not a compliance judgment. Confidence bands: 95% domain-verified, 90% registry-checked, 80% self-attested, 70% weak signal. Self-attested items marked “vendor's site” are gathered from the vendor's own website and are not verified by us.
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Unknown means this listing does not publish a repository. It is not a statement that the code is closed, and a linked repository is not a claim that the publisher wrote it: the registry computes that relationship privately and does not publish it.
Evidence risk is the share of the build you cannot see before you deploy, not a security rating. Sign in to see the layer-by-layer basis for this band.

