Top Financial Institutions trust Arthur for ML Performance
Arthur is the AI Performance Company for financial services. Our platform monitors, measures, and improves machine learning models to deliver better results for top industry use cases: fraud/KYC, forecasting models, fair lending, robo-advisory programming, credit worthiness, customer service, and more.
Credit Approvals & Underwriting
Anti-Money Laundering, KYC
Customer Engagement & Service
Measuring ML Performance for Financial Services in the Age of Hyperautomation
In this guide, learn how you can proactively mitigate legal, regulatory, and reputational risk while saving money and driving business goals through real-time optimization of MLOps models for financial services and banking use cases.
Measuring ML Performance for Financial Services
Download this guide to learn how your financial services organization can maximize revenue and minimize risk with comprehensive model monitoring.
Trusted by leading enterprises
Fortune 100 leaders across financial services, healthcare, retail, and tech trust Arthur to monitor and improve their ML models to drive business impact.
“Thanks to Arthur, we know that our preventative care models are fair, and that we can catch any potential issues before they impact our members…and the Arthur platform allows us to detect and fix data drift before it becomes a real problem.” — Chief Analytics Officer, Humana
"The biggest challenge is looking at this from a reputational risk perspective. The last thing we want is to be on the front page of the news with a bias issue.” — Global Tech VP
“Arthur is 6-9 months ahead of the competition and there was a clear preference for their UX among our data scientists.”— Head of Global Artificial Intelligence
Accuracy & Data Drift
Measure model performance with custom and out-of-the-box metrics for computer vision, NLP, and tabular models.
Detect, diagnose, and react to data drift before it impacts results with univariate & multivariate metrics.
Proactively improve model accuracy and track and compare model versions over time.