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ML Observability for
Fraud Detection Models

Ensure your Fraud Detection Model works as intended so that you keep the customers that generate your revenue and profit -- and ditch the bad eggs.

Catch Fraudsters Before the Fraud

Fraud Detection Models are in a constant game of cat-and-mouse, so it doesn’t take long for issues to surface. Leverage Aporia’s customizable ML monitoring to detect drift, model performance, and deviation in model behavior to ensure that fraudulent customers are caught fast and that legitimate ones aren’t caught in the crossfire.

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Full Visibility of Your Fraud Detection Model in Production

Aporia provides you with a customizable dashboard experience that includes dynamic widgets, feature comparisons, and much, much more! With clear visibility of the situation, you can truly focus on the goal of reducing false positives, catching fraud early, and avoiding lost sales.

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Find the Root Cause of Any Issue

Insights into the minds of fraudsters helps your business stay one step ahead in the game of cat and mouse. Aporia’s Explainable AI makes it easy to explain your Fraud Detection Model predictions to all stakeholders and effortlessly investigate data points for root cause production issues so that the mouse doesn’t steal your cheese.

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Look what our customers have to say about us

“In a space that is developing fast and offerings multiple competing solutions, Aporia’s platform is full of great features and they consistently adopt sensible, intuitive approaches to managing the variety of models, datasets and deployment workflows that characterize most ML projects. They actively seek feedback and are quick to implement solutions to address pain points and meet needs as they arise.”

Felix D.

Principal, MLOps & Data Engineering

“As a company with AI at its core, we take our models in production seriously. Aporia allows us to gain full visibility into our models' performance and take full control of it."

Orr Shilon

ML Engineering Team Lead

“ML models are sensitive when it comes to application production data. This unique quality of AI necessitates a dedicated monitoring system to ensure their reliability. I anticipate that similar to application production workloads, monitoring ML models will – and should – become an industry standard.”

Aviram Cohen


“With Aporia's customizable ML monitoring, data science teams can easily build ML monitoring that fits their unique models and use cases. This is key to ensuring models are benefiting their organizations as intended. This truly is the next generation of MLOps observability.”

Guy Fighel

General Manager AIOps

“ML predictions are becoming more and more critical in the business flow. While training and benchmarking are fairly standardized, real-time production monitoring is still a visibility black hole. Monitoring ML models is as essential as monitoring your server’s response time. Aporia tackles this challenge head on.”

Daniel Sirota

Co-Founder | VP R&D

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