AI/ML, A Common Sense Approach to Model Validation

AI/ML, A Common Sense Approach to Model Validation
Thought Leadership Webinar:  More and more the models we use are incorporating Machine Learning (ML) into their analytics. Artificial Intelligence (AI) is also beginning to be used for decision making, leading to a potential “Black Box” model. This webinar introduces concepts for validating these models without having a deep technical background in ML or AI.
 
  Date:
November 3, 2021

  Time:
10:00 - 11:00 a.m. EDT
2:00 - 3:00 p.m. GMT
 

  Presented By:
John Hurlock
Founder & President, Smarter Risk Management

 Session Length:
60 minutes

 

About This Webinar

The terms “Artificial Intelligence (AI)” and “Machine Learning (ML)” have entered our general lexicon and are being used by software vendors and model developers to describe why their modeling is better and will give you a competitive edge. These approaches are touted as using “big data” to analyze (ML) and then make decisions (AI) that result in everything from improving loan decision making to driving a car. Some models are complex enough to require a sophisticated approach to validation. Other models need to be understood by those without an ML or AI background.

This webinar is aimed at the generalist who needs to understand how they can acquire, use and then validate a model without having an ML or AI background. We will present common sense approaches that may be used with models that have a component of ML/AI embedded in them. These approaches will provide a good foundation for use with all models, but especially models that deploy ML/AI. At the end of this Webinar, you will be provided with an outline and a list of questions that can be asked when dealing with an ML/AI model. Note – This Webinar is not designed to be a technical webinar and will not cover specific algorithms or complex ML/AI concepts.

  • Learn what is meant by ML and AI and how these approaches are being deployed
  • Gain an understanding of how ML/AI work within a model
  • Understand what you should know about ML/AI
  • Receive an outline of how to approach an ML/AI model from a non-technical perspective
  • Learn the key questions to be asked when validating the model
  • Be prepared to present information about an ML/AI model that will be understood by other non-technicians

About Our Experts  

  
 
 

John Hurlock is the Founder and President of Smarter Risk Management (SRM), a boutique risk management consultancy. SRM is focused on developing and delivering risk management services and solutions to financial institutions, government entities, and industries throughout the United States and in international emerging markets.

Through his proprietary SMARTER approach to risk management, John applies a comprehensive methodology that consists of structured and consistent risk management processes. This results in practical strategic, financial, and operational benefits that strengthen the organization and improve overall and specific risk management.

He has worked with organizations that include community and regional banks, trillion dollar international financial institutions and government entities across North America, Africa, and the Middle East. His work includes the development, migration to and validation of various models, addressing regulatory orders and issues, and he been heavily involved in the establishment of risk analytics, including risk-based capital, credit origination and servicing, portfolio stress testing, asset liability management, operational risk and the various Basel Accords.

John has almost 40 years of experience, the first 15 years of which were spent as a community/regional banker. Prior to launching Smarter Risk Management, John directed Risk Management services at institutions including Bancware Corporation, Metavante Corporation and Sheshunoff Consulting + Solutions.

John received his MBA and undergraduate degree at the University of Wisconsin. He is an Adjunct Professor at Webster University’s MBA program and has facilitated training sessions for EuroMoney Learning Solutions. He is a member of PRMIA and the Risk Management Association (RMA).

 

Continued Risk Learning Credits: 1

PRMIA Continued Risk Learning (CRL) programs provide you with the opportunity to formally recognize your professional development, documenting your evolution as a risk professional. Employers can see that you are not static, making you a highly valued, dynamic, and desirable employee. The CRL program is open to all Contributing, Sustaining, and Risk Leader members, providing a convenient and easily accessible way to submit, manage, track and document your activities online through the PRMIA CRL Center. To request CRL credits, please email [email protected].

  Registration  
  Membership Type Price  
       
  Members (Sustaining, Corporate, RIM & Contributing)
COMPLIMENTARY  
  Non Member $30 USD
 
       

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When
11/3/2021 10:00 AM - 11:00 AM
Where
Thought Leadership Webinar
 

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