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AI/ML, A Common Sense Approach to Model Validation
November 3, 2021
10:00 – 11:00am EDT
2:00 – 3:00 GMT
Join SMARTER Risk President John Hurlock as he presents a 1 hour Thought Leadership Webinar in partnership with PRMIA.
As more and more models incorporate Machine Learning (ML) in their analytics and Artificial Intelligence (AI) for their decision making, the landscape of Model Validations is changing.
This webinar is aimed for the generalist who is looking for a commonsense approach that will help them understand how they can acquire, use, and then validate a model without having an ML or AI background.
Model Validation - Identifying Issues with Modeling
April 28, 2021
10:00 – 11:00am EDT
3:00 – 4:00 BST
Join SMARTER Risk President John Hurlock as he presents a 1 hour webinar in partnership with PRMIA.
John is a seasoned practitioner of Model Validations. Come and hear about the lessons that he has learned from validating hundreds of models over the last decade, learning important lessons from the issues that others have experienced and apply this valuable information to your own models.
Insights
Articles
Model Validation Changes Under CECL - Moving from Incurred Loss to Expected Loss
ALLL/CECL & COVID-19 - Reserving for Loan Losses During a Pandemic
Until recently determining the reserve has been a fairly straightforward and predictable event. Now as the financial industry enters into a “virus driven” recession. The time has come for Bankers to ask themselves two critical questions. “How do I reserve for this, given all the uncertainty that exists, and the (as of right now) lack of actual losses? And how do I support the reserve decisions I make?”
This article discusses ways to address the reserve over the next few quarters. By reviewing the current situation, what is unpredictable and predictable about it.
White Papers
Latest From The Get SMARTER! Blog
A Major Change to Credit Risk Incoming? Gartner Survey Highlights Alternative Data
Continuing with topics that might arise at the May 2022 Abrigo Think Big conference (in San Antonio) where SMARTER risk management, LLC. is pleased to be a sponsor, I’ll touch on the topic of Alternative Data in Credit Decisioning.
Fraud, AML, and Risk in Crypto
One big blind spot for many modern financial organizations is the risk associated with cryptocurrency. As we noted in our first blog on the topic, cryptocurrency is inherently risky for all sorts of reasons, but many financial institutions are beginning to dip their toes in the market. Cryptocurrency is hard enough on its own… but how do we recognize, investigate, and address fraud and prevent money laundering when cryptocurrency is involved?
The Frame of the Game: Explaining Risk Using the Framing Technique
Everything we think and do is guided by our bias. Every meeting, every environment, every conversation is steeped in the idea of the exact conditions of the world around us at the time. Even the most standard of conversation topics, such as “how’s the weather?” or “how’s your day?” are based on that context. If I’m a risk management professional who spends a majority of my days thinking about and analyzing risk, I want to talk about that with others. Contextually, though, I should understand that others have a different understanding and knowledge of risk aspects, so I should change the way I speak with them to be more (less) technical/jargon-y when discussing risk. How good I am at framing the topic of the discussion impacts quite strongly how successful I am as a risk manager.
Unsiloing Risk Management: How COVID-19 Affected the Future of Risk Infrastructure
As strange as it might seem, the end of the pandemic’s direct effect on economies appears to be coming to an end. However, its indirect impacts will continue to affect society for the foreseeable future, and the space of risk management is no exception to this. Since last March, financial institutions and corporations have needed to adapt the existing risk frameworks to navigate what can only be described as one of the most uncertain periods in modern history.
Mitigating Complexity: A Practical Approach to Validating AI/ML Risk Models
As I was presenting a webinar last month based around the complexities and common issues of risk modeling, a question came up surrounding the validation of models utilizing artificial intelligence and machine learning algorithms. In particular, the question regarded what sort of practical approaches we can take, and how those of us outside of the AI experts can understand a technical topic like this one. Or, at the very least, how we can (as non-experts) comprehend the gist of what’s required, and leave the more in-the-weeds aspects to those machine masters.
Back to Reality? Risk Management and Your Decision to Go Back to the Office
About a month ago, President Joe Biden announced that every U.S. adult will be eligible for COVID-19 vaccination no later than May 1st. Since that announcement, we’ve reached the point where most states have expanded access in an effort to reach this milestone, with...
A Pragmatic Approach to Selecting Risk Modeling Software
Unsurprisingly in this day and age, risk management and risk modeling are booming fields. With that boom, however, comes a whole host of modeling software options that are ever-so-slightly different, along with a large group of vendors trying to convince you that their product is the best fit for your organization. As you might have guessed, though, every single program is not the best program for you, nor is it an easy/simplistic process to determine exactly which one fits.
Risk is, or Risk is Not: Using Pascal’s Wager when Thinking about Risks
Pascal’s wager was one the first things that drew me to the risk management field. I have kept it in mind whenever I begin to think about a risk. For example, wearing masks during the COVID pandemic (more on this later). It can be applied to many different aspects of management. Let’s discuss a few of these.
AI/ML Ethics – What Will it Take to Trust the Model
Artificial Intelligence (AI)/Machine Learning (ML) and model risk management is a topic I’ve addressed in prior blogs. The image of HAL from 2001: A Space Odyssey and its nefarious actions is burned into many people’s minds as they think about AI. We are far, far away from this concept of AI and, since AI/L is still in its infancy, there are many things to mistrust about it. For many, if not most, people, these methods are black boxes: some data goes in, and some conclusions based on that data come out. A key to moving forward from the use of such tools is to establish the explainability of the model, ensuring that there’s at least a few members of the organization utilizing the technology that understand and can explain the methodology for both internal governance and external regulation reasons.
