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Translating ML to Business Value in Financial Services

Watch the On-Demand Webinar

Join Keegan Hines, Arthur's VP of Machine Learning, as he shares lessons we've learned from improving responsible ML performance at complex global enterprises—plus stories from his time as Director of Machine Learning Research at a global financial institution and his many years in academia.


You'll leave with practical advice for using ML to drive meaningful business results across use cases like fair lending, fraud, and customer experience.

Download the Guide: Best Practices for Planning Your Next ML Project

AI offers the potential to solve our most complex business problems; but to truly deliver on this potential, enterprises need to rethink core operating principles and build new practices that translate data science to business value.  


In this guide, Arthur shares the step-by-step best practices for accelerating ML projects to solve business problems.

Best Practices for Enterprise AI

Download this comprehensive guide with 8 essential steps for planning your team’s next ML project. 

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.

Top Financial Services enterprises trust Arthur for ML Performance

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