Optimize Your Business with Model Risk Management
Complimentary Webinar: Because traditional model development methods are time-consuming, tedious, and subject to user error and bias, it is important to have a more robust framework for minimizing model risk that delivers compliance with MRM (model risk management) regulations. AI and machine learning can systematically automate fundamental model validation tasks, such as sensitivity testing, stress testing, champion-challenger benchmark models, documentation and reporting, model monitoring, and inventory tracking. This allows your team to gain efficiency, align confidently with regulations, and improve your overall validation process so that they can focus on more complex tasks and mission-critical models.
Date:
July 13, 2021
Time:
11:00 a.m. - 12:00 p.m. EDT
4:00 - 5:00 p.m. BST
Presented By:
Ryan Manville, AI Success Regional Director, DataRobot
Ben Miller, Customer Data Scientist, DataRobot
Moderator: Ken Radigan, CEO, PRMIA
Location:
Virtual Event
| About This Webinar |
In this webinar, we will discuss how to:
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Optimize and accelerate your model risk management framework
- Increase the efficiency of your model development and validation processes, while aligning modeling and validation processes to regulatory expectations for model risk management
- Reduce model risk, using automated machine learning to develop cutting-edge machine learning models
- Automate the necessary compliance documentation process with the click of a button
Thank you to our sponsor for providing this webinar to the PRMIA community.
| About Our Experts |
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Ryan Manville, AI Success Regional Director, DataRobot
Ryan is a leader within the banking practice at DataRobot, helping financial institutions leverage AI and Machine Learning for predictive analytics and data mining. Ryan has over 25 years experience in management consulting and banking, with broad banking domain knowledge and deep expertise in data and analytics. Previous positions included COO of a retail bank, management consultant at McKinsey & Company and over a decade leading analytical teams at American Express. Ryan is a graduate of the Kellogg School of Management at Northwestern University where he received a MBA and Tufts University where he received a Bachelor of Science in Electrical Engineering.
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Benjamin Miller, Customer Data Scientist, DataRobot
Ben has a strong background in data science, advanced technology, and engineering. Ben is a machine learning professional with demonstrable skills in the area of multivariable regression, classification, clustering, predictive analytics, active learning, natural language processing, and algorithm R&D. He has scientific training and application in disparate industries including; biotechnology, life sciences, pharmaceutical, environmental, human resources, banking, and media.
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Moderator: Ken Radigan, CEO, PRMIA
Prior to joining PRMIA, Ken served as the Chief Risk Officer (CRO) for US and Bermuda platforms of Aspen. He worked at AIG for over 25 years and was the CRO for the Casualty and Global Risk Solutions Divisions. Ken also served as Board Member, Treasurer, Chairs of the Finance and Ethics Committees, and Director of the New York Chapter for PRMIA. He is a lecturer at Columbia University’s Master of Professional Studies in Insurance Management.
Ken was the inventor of two U.S. Patents on Nuclear Decommissioning Insurance. He has a Bachelor of Arts degree with a major in Mathematics from the University of Dayton and has the following professional designations:
- Professional Risk Manager, PRM
- Chatered Property Casualty Underwriter, CPCU
- Associate in Risk Management, ARM
- Associate in Surplus Lines Insurance, ASLI
- FINRA Series Certificaations: 6,63,66, and 7
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