Model Risk Management for Machine Learning Models

Address the key model risk management and validation challenges when deploying data science and machine learning models in the enterprise.


  PRMIA Training Partner- QuantUniversity

    Instructor: Sri Krishnamurthy, CFA, CAP   
   Delivery: Self-Directed
  Case studies and Lab activities
   Duration: 5 Lessons | 1.5 hours/lesson 
       Completion time: 6 months from date of purchase  
    Standard Registration Fee: $649.00
  PRMIA Network Fee: $549.00 
  Use discount code: PRMIA100

   Value Add: Capstone Project, 2 sessions, $150.00  
 Question/Need support?  Our training partner is here to help

Be sure to add the Capstone Project at check out for only $150 more!
 Register Now!

  (You will be redirected to our training partner's registration system.)

About the Course & Learning Objectives 
The financial industry has been adopting AI and machine learning at a rapid pace. Alternative datasets including text analytics, cloud computing, algorithmic trading are game changers for many firms exploring novel modeling methods to augment their traditional investment and decision workflows. As more and more open-source technologies penetrate enterprises, quants and data scientists have a plethora of choices for building, testing and scaling models. While there is significant enthusiasm, model risk professionals and risk managers are concerned about the onslaught of new technologies, programming languages, and data sets that are entering the enterprise. With little formal guidance from regulators on how to validate models and quantify model risk, organizations are developing their own home-cooked methods to address model risk management challenges.

In this course, we aim to bring clarity on some of the model risk management and validation challenges with data science and machine learning models in the enterprise. We will discuss key drivers of model risk in today’s environment and how the scope of model risk management is changing. We will introduce key concepts and discuss aspects to be considered when developing a model risk management framework incorporating data science techniques and machine learning methodologies in a pragmatic way.

Upon completion of this course, you will be able to:
• Role of Machine Learning and AI in financial services
• Model Risk Management challenges and best practices for machine learning models
• Validating machine learning models: Quantifying risk, best practices and templates
• Regulatory guidance and the future
• Practical case studies with sample code

Optional Capstone Project
Participants will go through a guided exercise to perform model validation on a chosen machine learning model of their choice. Guidance will be provided in scoping and implementing the project. Two lessons in total. 

Your Instructor: Sri Krishnamurthy, CFA, CAP

Sri is the founder of QuantUniversity, a data and quantitative analysis company. He has more than 15 years experience in analytics, quantitative analysis, statistical modeling and software development. He is a quantitative specialist with significant experience in designing data mining and analytic systems for some of the world’s largest asset management and financial companies

Sri has worked at MathWorks as a Computational Finance Consultant where he worked with more than 25 customers providing asset management, energy analytics, risk management and trading solutions. Prior to that, Sri was a consultant at Endeca (now Oracle) in their Analytics Group and at Citigroup in their Fixed-Income Group building large-scale analytical and trading systems.He is a Charted Financial Analyst and a Certified Analytics Professional.

He is an active member of the Boston Security Analysts society and QWAFAFEW.

Sri is the creator of the Fintech Certificate Program & Analytics Certificate Program and teaches graduate courses in Quantitative methods, Data science and Analytics and Big Data at Babson College, Northeastern University and Hult International Business School.

Program Content

Module  Topic

Module 1

Machine Learning and AI: A Model Risk Perspective
•  Drivers of Model Risk in the age of data science and AI
• Machine Learning vs Traditional quant models
• How has the world changed?
• A tour of Machine Learning and AI methods
• Supervised vs Unsupervised Learning (Regression, Neural Networks, XGBoost, PCA, Clustering)
• Deep Learning & Reinforcement Learning (Keras, Tensorflow, PyTorch)
• Automatic Machine Learning & Machine Learning APIs (Google, Comprehend, Watson)
• ML on the cloud vs On-prem
• Models redefined: Data, Modeling environment, Modeling tools, Modeling process

Module 2 Model Risk Management for Machine Learning Models-Part 1
• ML Life cycle management
• Tracking
• Metadata management
• Scaling
• Reproducibility
• Interpretability
• Testing
• Measurement
Module 3 Model Risk Management for Machine Learning Models-Part 2

The Decalogue: Ten things to think about when developing your model governance framework when integrating Machine Learning models (cont’d):
• Integrating Data Governance and Model Governance
• Development Models vs Production Models
• Fairness, Reproducibility, Auditability, Explainability, Interpretability & Bias
  o How do we objectively measure these?
  o Review of the Apple-Goldman Sachs credit card debacle
• Machine Learning options and considerations
  o AutoML (Data Robot,, etc.), ML as a service (Google, Comprehend, Watson) and home-cooked custom models
• ML and Governance: Roles and Responsibilities redefined
Managing models in the day of Covid19
• Perspectives on point-forecasts, validation and fat-tails!


Module 4 Pragmatic Model Risk Management for AI/ML models
• Challenges and best practices for pragmatic model management within the enterprise
• Working with open source projects
• Working with vendor models and machine learning APIs
• Quantifying model risk for machine learning models
• Model risk management for deep-learning models
• Validation criteria and best practices
• Templates for Model Validation for machine learning models
Synthetic data for Model Risk Management
• Use of Synthetic datasets
Module 5 Hands-on Case study
Learn from the past: How does Supervised machine learning work?
• Validating a Credit-risk machine learning model A case study illustrating a model validation of a credit risk model involving machine learning
• Working with Regression, Neural Networks, and Random Forest models
• Development models vs Production models
• Sample templates and worksheets will be provided
• Roadmap for the MRM team to upskill and keep abreast of changes in the AI and ML landscape Training, education, and expectation setting Future outlook: Regulation, Sandboxes, Frameworks
• Review of recent regulatory efforts
• How should companies proactively plan for changes and the future?
Module 6
Capstone Project-Part 1: Scoping and design (Fee)
Put your newly learned skills to practice while being mentored through the process. Participants will go through an  exercise to perform model validation on a machine learning model of their choice.
Module 7
Capstone Project-Part 2: Demonstrate your skills (Fee)
Participants will demonstrate their findings and obtain feedback from instructors and/or industry participants.

PRMIA Network Fee: $499.00 using
 discount code: PRMIA100 
Be sure to add the Capstone Project at check out for only $150 more! 

Register Now! 
(You will be redirected to our training partner's registration system.)

Who should attend?

Model Risk professionals, Model validators, Regulators and Financial professionals new to data-driven methodologies
Quantitative analysts, investment professionals, Machine learning enthusiasts interested in understanding model risk and governance aspects in fintech, insurance and financial organizations

QUANTUNIVERSITY Registration Policies

This course is being delivered by a PRMIA Training Partner, QuantUniversity. All registrations, payments, and course operations will be managed exclusively by QuantUniversity. When registering for the course, you will leave and use QuantUniversity's registration system, proprietary learning management system, and QuSandbox for labs. Questions and requests should be directed to QuantUniversity at

Please review QuantUniversity's cancellation and refund policy at the time of purchase, as their policies supersede PRMIA's registration policies.  

Need Support? 

Contact our training partner for questions about the course, group registrations and technical support

Analytics for a cause initiative - Thank you QuantUniversity for supporting future risk leaders!

QuantUniversity is a proud sponsor and contributor to educational scholarships, valued at $30,000, to students from eight countries and 12 chapters, participating in the PRMIA Risk Management Challenge for 2020 and 2021!  Learn more about this and other outreach supported by QuantUniversity.

PRMIA Network Fee: $549.00 using discount code: PRMIA100
Be sure to add the Capstone Project at check out for only $150 more! 

Register Now!
(You will be redirected to our training partner's registration system.)


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