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AI Portfolio Risk & CECL Allowance Calculation CECL Models

Automating the CECL close and where human review matters

Kate Randazzo
October 23, 2026
0 min read

CECL automation: What to automate and what to keep under review 

For many banks and credit unions, getting the CECL calculation closed on time involves familiar steps: preparing data, refreshing assumptions, running calculations, reconciling results, explaining changes, and getting management approval. When those steps depend on spreadsheets, email, manual handoffs, and one person’s memory, small delays can add up and leave less time for review. 

 

This article covers these key topics: 

Why the CECL close can become a bottleneck 

Close pressure often builds through a series of small tasks rather than one major problem. Perhaps a data file arrives late, or someone needs to confirm which version of an assumption or support file is final. Sometimes, the calculation must be rerun after a reviewer spots an unexpected result. 

Each step takes time, and each handoff can create more waiting. By the time results are ready for discussion, the team may have little time left to ask questions that management and examiners will ask: Does this movement make sense? What changed in the portfolio?  

This is where CECL automation can be useful. CECL automation uses configured workflows to reduce manual work during the allowance close. The goal of automating the CECL close process is to accelerate repeatable work through clear ownership and checkpoints. This gives reviewers more time to understand the results and support the allowance conclusion. 

See how Abrigo supports recurring activities, comparison narratives, and workflow controls in this webinar.

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Map the process before automating it 

Before choosing a tool or a task to automate, trace one complete CECL cycle from data arrival through management approval. Write down the steps, handoffs, and dependencies. 

Look for: 

  • Spreadsheets and emails used to move work along 
  • Waiting periods between tasks or reviewers 
  • Repeated data checks and calculation runs 
  • Steps that depend on someone remembering what to do next 
  • Questions that surface late in the close 

Mapping the process can show where work gets stuck and whether a step is stable enough to automate. Automation works best when the entire team agrees on the process, inputs, and decision points. If the rule changes each quarter or no one agrees on who owns the next step, software alone will not resolve the underlying issue. 

Separate repeatable execution from judgment 

A useful way to evaluate the CECL workflow is to distinguish steps that follow a stable rule from those that require people to interpret what they see. 

Stage 

Automation may help with 

Human role 

Data preparation 

Monitoring for inputs and organizing recurring steps 

Reconcile data and confirm it is complete and appropriate 

Calculation 

Running configured, repeatable activities 

Validate assumptions and investigate exceptions 

Results review 

Comparing periods and surfacing changes or potential drivers 

Assess materiality and add institution-specific context 

Approval 

Routing tasks, documenting steps, and prompting reviewers 

Resolve questions and approve the allowance conclusion 

The specific workflow depends on the institution’s process and configuration. The key is to define where an automated step ends and a person must review or make a decision.

Build controls into the workflow 

For each step in the CECL process, define task owners, due dates, evidence to retain, review gates, and the conditions that should pause a workflow. For example, an automated CECL process might monitor for data and prepare recurring activities, then pause until a reviewer confirms the data is complete and the approved inputs are ready. After the calculation runs, reviewers can reconcile results and investigate exceptions before the work moves to approval. 

Automation can organize tasks and evidence, but it does not, by itself, guarantee compliance or audit readiness. Visible checkpoints make the process easier to follow and repeat, thereby improving consistency. They also keep accountability with the people responsible for the work. 

Use AI comparison as a starting point for review 

AI can help reviewers organize period-over-period analysis. For example, Abrigo’s AI-powered comparison narrative highlights changes between calculations and suggests potential drivers. Reviewers can then verify the figures, add institution-specific context, refine the draft, and approve the final narrative. 

While it excels at identifying change, AI may not recognize the circumstances behind it, such as a portfolio sale, an underwriting adjustment, a new market, or an unusual relationship. Reviewers must determine whether the narrative accurately reflects what occurred and explains the results. 

AI can support CECL analysis, but management remains accountable for all assumptions, decisions, and the final allowance conclusion. 

Start with one manageable pilot 

A small pilot can help the team test both the process and the controls before expanding automation. Consider this sequence: 

  1. Map one close. Document the steps, handoffs, waits, spreadsheets, and reruns. 
  1. Classify the work. Separate repeatable execution from tasks that require interpretation or approval. 
  1. Define the control. Name the owner, trigger, evidence, review gate, and conditions that should stop the workflow. 
  1. Pilot one stable activity. Choose a recurring step, such as detecting data readiness, launching a repeatable calculation sequence, or preparing a first draft of a comparison narrative. 
  1. Measure the result. Track cycle time, manual touches, reruns, exception visibility, approval turnaround, and time spent preparing the allowance committee narrative. 

Measuring more than speed can show whether the change made the process easier to review and manage. Did reviewers see results earlier? Were exceptions easier to find? Did the team have more time to prepare for the allowance committee? 

The first pilot does not need to redesign the entire close. It should solve a real bottleneck and show whether the team can maintain clear controls as work moves through the process. 

Use recovered time for better analysis 

A well-designed CECL close automates work that follows a clear, repeatable process and keeps reconciliation, challenge, and approval visible. The benefit is a faster close that leaves more room for experienced reviewers to understand portfolio changes, prepare for management questions, and support the allowance conclusion. 

Before the next CECL close, choose one recurring activity and ask: Is it stable? Is it repeatable? Can we clearly define the control? If so, it may be a practical place to begin. 

This blog was written with the assistance of an AI large language model. It was reviewed and revised by Abrigo's subject-matter expert.

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FAQs

What parts of the CECL close can be automated?

Financial institutions may automate stable, repeatable tasks such as monitoring for data, refreshing approved inputs, launching configured calculations, routing tasks, and preparing period-over-period comparison narratives. Data reconciliation, exception review, and final approval should remain visible human checkpoints. 

What CECL decisions should remain with management?

Management should own decisions that require judgment, including interpreting portfolio changes, evaluating assumptions, resolving review questions, and approving the allowance conclusion. Automation can organize information and calculate the effects of decisions, but it should not make those decisions. 

How can AI help review changes in the allowance?

AI-supported comparisons can surface changes between calculations and suggest potential drivers. Reviewers should validate the figures, add institution-specific context, and determine whether the explanation accurately reflects what happened. 

How should a financial institution begin automating its CECL process?

Start by mapping one close from data arrival through management approval. Identify a stable, recurring step, define its owner and review controls, then pilot it. Measure changes in cycle time, manual touches, reruns, exception visibility, and review turnaround. 

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The information, content and materials provided through this website are for informational purposes only and are not intended to constitute legal advice. Customers should consult with their legal counsel regarding the application of laws and regulations to their specific circumstances.

About the Author

Kate Randazzo

Senior Content Marketing Manager
Abrigo
Kate Randazzo is a Senior Content Marketing Manager at Abrigo, where she collaborates with industry thought leaders to develop digital content for banks and credit unions. Drawing on her background in strategic communications and content marketing, she translates complex financial topics into practical insights that help financial institutions better serve

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About Abrigo

Abrigo enables U.S. financial institutions to support their communities through technology that fights financial crime, grows loans and deposits, and optimizes risk. Abrigo's platform centralizes the institution's data, creates a digital user experience, ensures compliance, and delivers efficiency for scale and profitable growth.

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