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Registration is now open for our 40th Annual Conference! 

About Us
Data-Driven Solutions
Model Validation & MRM
Asset/Liability Management

Interview with the Quants - Step Behind the Curtain

Chase Ogden

Quantitative Consultant

Darling Consulting Group

As a consultant with DCG’s Quantitative Risk Analysis and Strategy team, Chase brings over a decade of programming and modeling expertise. Chase provides a unique perspective of the entire analytics lifecycle, having served in a variety of roles from model developer to senior leader of enterprise-wide, cross functional analytics implementations.

As a practitioner at large and mid-sized financial institutions, Chase has experience in a wide array of modeling approaches, applications, and techniques, including: asset-liability models, pricing and profitability, capital models, credit risk and allowance models, operational risk models, deposit studies, prepayment models, branch site analytics, associate goals and incentives, customer attrition models, householding algorithms, and next-most-likely product association.

Chase is a graduate of the University of Mississippi and holds Master’s degrees in International Commerce Policy and Applied Statistics from George Mason University and the University of Alabama, respectively. A teacher at heart, Chase frequents as an adjunct instructor of mathematics and statistics.

Mark Haberland

Managing Director

Darling Consulting Group

Mark is a Managing Director at Darling Consulting Group. In this role, Mark works directly with financial institutions to strengthen their asset liability management process. He provides support to clients in the areas of liquidity risk management, capital, ALM modeling and reporting and regulatory compliance. He is a top-rated speaker and frequent author and conducts customized workshops and educations sessions for ALCOs and boards.

Sam Chen

Quantitative Consultant

Darling Consulting Group

As a Quantitative Risk Consultant at Darling Consulting Group, Sam has validated a variety of risk models for large financial institutions—including risk rating, stress testing, allowance and deposit models—from both a statistical and business perspective. Sam has also combined his background in econometrics with his experience in credit risk to help DCG enhance its community bank credit stress testing methodology.

Before arriving at DCG, Sam served as a Senior Consultant in SunGard’s Risk & Performance group, where he developed models in multiple areas of financial risk, with a focus on credit and interest rate risk. Sam designed SunGard’s Dodd-Frank Act stress testing model selection algorithm and has also created custom PD and LGD models, including a suite of models currently implemented at a top 15 U.S. bank (by asset size).

Sam graduated cum laude with a bachelor’s degree in economics with mathematical applications from Princeton University. While at Princeton, he was the recipient of the John Glover Wilson Memorial Award for his thesis studying the economics of bargaining.

Model complexity and the increased reliance on results have raised the bar dramatically in terms of expected performance and whether models are working as intended. But it’s not always easy to know what the right next step should be. Have you ever wished you could phone a friend and ask?

In this timely webinar, DCG Managing Director Mark Haberland will lead a panel of two of DCG’s senior Quantitative Consultants, Chase Ogden and Sam Chen, to discuss current trends in model validations and what you should be on the lookout for in 2024.

Chase and Sam will take live questions from attendees on any risk management topics, such as:

  • What should be required of a validation?

  • How often should validations be performed for “high risk” models?

  • What do some validations find that others miss?

  • What is the impact of poor model performance?

If you have questions on your mind, feel free to send them to Mark prior to the webinar at or you can ask them live during the session.

We look forward to bringing some clarity to model risk management and the importance of monitoring model performance.

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