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Exposing Invisible Risk: Safeguard Your CECL Model from Validation Failure

Model validation frequently exposes significant deficiencies in an institution's Current Expected Credit Loss (CECL) framework. All too often, these critical findings stem from a single root cause: the "Black Box" problem, where management cannot access or explain the granular details of their model's inner workings.
Upcoming

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DATE

Tuesday, December 15, 2026
2:30 pm - 3:30 pm

INSTRUCTOR

Justin Umscheid

FORMAT

Webinar

$299.00 or 1 Token

Includes: Live Access, 30 Days OnDemand Playback, Presenter Materials and Handouts

  • Accounting/Reporting
  • Compliance Officer
  • Controller/Accountant
  • Loan Operations Manager/Specialist
  • Risk Manager
  • Senior Management

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This webinar targets the four most impactful areas where examiners raise red flags:

  • Data Integrity
  • Qualitative Adjustments (Q-Factors)
  • Methodology
  • Governance

We will move beyond the theoretical requirements to provide actionable, best-practice solutions for resolving these challenges.

Attendees will discover how to transition from opaque spreadsheets to a defensible, audit-ready posture. We will also demonstrate how a robust, integrated technology solution—such as the ARCSys platform—can automate these best practices, providing the necessary audit trail, data granularity, and analytical rigor required to satisfy regulatory scrutiny and drive confidence in your ACL.

What You'll Learn

  • Diagnose the root causes of the most frequent CECL validation findings, specifically focusing on the risks associated with data opacity and unsupported Qualitative Adjustments (Q-Factors).
  • Construct an audit-ready defense for data integrity by implementing best practices for reconciliation and lineage documentation that directly resolve data findings.
  • Substantiate forward-looking forecasts and Q-Factor adjustments with reasonable and supportable evidence to ensure the model’s conceptual soundness is visible to examiners.
  • Justify critical methodological assumptions—such as Probability of Default (PD) and Loss Given Default (LGD)—using documentation techniques that mitigate findings related to model mechanics.

Who Should Attend

Attendees should be employees from financial institutions who are involved in CECL processes.
Justin Umscheid

Instructor Bio

With more than a decade of accounting and implementation experience, Justin serves as Vice President of Client Services at ARCSys Technologies. He excels at guiding clients through the onboarding process for CECL, ensuring a smooth transition. Justin's core focus is to support institutions with their allowance calculations, provide expert accounting guidance, and offer specialized consulting for back-testing, validations, and stress and sensitivity analysis. His comprehensive approach fosters a deeper understanding of the client's financial data as a key part of the ARCSys service model.

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