For many financial institutions, implementing Current Expected Credit Losses (CECL) standards can be challenging — not because of the model itself, but because of the reliability of the underlying data. Incomplete and inconsistent data from disconnected data sources make it difficult to produce reliable results with a high degree of confidence.

A common frustration in complying with CECL standards is identifying what data is required. The level of detail involved with different methodologies varies, and financial institutions may find that their current systems, databases, and reporting processes are not sufficient.

Strong CECL data requirements serve as more than a compliance function. They can help banks strengthen forecast accuracy to better allocate financial resources. This article discusses the types of CECL data and how different methodologies impact requirements. 

What Are CECL Data Requirements?

CECL data requirements are the information needed for a bank to estimate credit losses under CECL standards. This typically includes:

• Historical loan performance
• Loan portfolio composition
• Relevant economic information

Data drives the reliability of CECL model outputs. No matter how complex a model is, it won’t produce reliable estimates if the underlying data is inaccurate, outdated, or incomplete. Notably, most CECL implementation challenges are a result of the data inputs, rather than the model itself.

Strong data and documentation practices further improve confidence with relevant stakeholders and support CECL compliance efforts. With transparency in the reliability of the data, institutions can more easily explain the steps they have taken to ensure a robust CECL process.

3 Core Types of CECL Data

Most CECL models rely on three main types of data: historical, current portfolio, and forward-looking. Combined, they help financial institutions estimate future credit losses.

Historical Data

Historical data shows how loans have performed over time. It commonly includes information across different loan segments, such as:

• Past-due and nonaccrual data
• Charge-off rates
• Recovery rates

Financial institutions typically use this data to identify trends and create a baseline for expected losses.

Current Portfolio Data

Current portfolio data reflects a bank’s current risk exposure based on its loan portfolio, rather than just historical averages. It can include:

• Outstanding loan amounts
• Loan types
• Collateral
• Borrower credit scores

Properly segmenting data is critical, as certain loan and borrower profiles behave differently.

Forward-Looking Data

Economic factors that may impact portfolio performance must be incorporated into CECL requirements when developing forecasts of expected future losses. Financial institutions often use economic forecasts, including publicly available forecasts such as those published by the Federal Open Market Committee (FOMC), to support these estimates. Inputs can include:

• Unemployment trends
• Interest rates
• Inflation
• Collateral values and loan-to-value (LTV) ratios

Selecting the right forecast inputs can help CECL models account for changing market conditions, rather than assuming the future will mirror past performance.

How CECL Data Requirements Vary by Methodology

CECL data requirements will depend on the methodology a bank uses. For example, some methods only require historical averages, while others need more detailed loan-level data.

Some methodologies include:

• Weighted Average Remaining Maturity (WARM) is a simple approach that uses historical loss rates and remaining loan life assumptions.
• Discounted Cash Flow (DCF) requires more detailed data. Common examples include cash-flow projections, payment schedules, prepayment assumptions, and discount rate calculations.
• Probability of Default and Loss Given Default (PD/LGD) require additional loss data, such as probabilities of default, collateral valuations, and recovery estimates.

Generally, simpler methodologies can be easier to implement, but they may not be as accurate. On the other hand, more complex methods can be more time-consuming, but may provide more accurate and detailed outputs.

Common Challenges With CECL Data

Data management issues often surface when it comes time for CECL implementation. Challenges with data consistency, accuracy, and accessibility across teams often lead to issues such as:

• Incomplete Historical Data: Data can sometimes be lost or difficult to retrieve, especially after mergers or transitions to new systems. This can make it difficult to establish reliable baseline loss estimates.
• Inconsistent Data: Across multiple teams, departments, and systems, inconsistencies in data can result in reconciliation issues, leading to a reduced level of confidence in the results. 
• Data Silos: Teams that work in silos may use different sources of data for assumptions. This can lead to discrepancies in methodologies. 
• Limited Forward-Looking Inputs: Reasonable and supportable economic forecasts can be difficult to implement, especially in volatile market conditions. 
• Manual Processes and Spreadsheets: Manual processes reduce operational efficiency, as well as increase the risk of input errors that can lead to audit issues. 

How to Evaluate Your CECL Data Readiness

Beyond verifying that the necessary data exists, financial institutions must evaluate their CECL data readiness. They should ensure the data is accurate, complete, and compatible with the chosen methodology.

At the same time, institutions should be mindful that processes need to be updated to remain reliable amid changing economic conditions.

Banks can start by identifying where data originates, how it moves across systems and teams, and whether assumptions are applied consistently. Manual workarounds should be carefully evaluated, as they often introduce unnecessary operational risks.

Questions to ask include:

• Is there sufficient historical depth? Loss estimates should be supported by sufficient historical data to establish baseline trends across the entire portfolio. 
• Is the data segmented properly? Loans should be grouped in a manner to reflect similar risk characteristics. 
• Are assumptions consistent across models? CECL assumptions should be aligned with related processes such as forecasting, ALM, and stress testing. 
• Can the institution clearly explain its inputs and assumptions? Data sources and qualitative adjustments should be documented and applied consistently.
• Are data collection and reporting processes consistent? Institutions should be able to produce results using the same process each reporting cycle without relying heavily on manual workarounds.

How to Improve CECL Data Management

A first step to improving CECL data management is to eliminate systems and teams that operate in silos. With a centralized data source, every team can confidently retrieve information without needing to reconcile discrepancies later in the process.

Automating data collection can also improve efficiency by saving teams time that would otherwise be spent manually entering data. An added benefit is that data automation can support more timely reporting, so teams can access information quickly.

Institutions should also ensure that assumptions are aligned across all teams, such as asset liability management (ALM), budgeting and planning, and stress testing. This is especially important when developing CECL reasonable and supportable forecasts.

Empyrean supports these data processes by providing a unified platform that enables teams to work from a single source of truth. The result is improved consistency, reliability, and operational efficiency.

Why Data Integration Matters for CECL

Data integration plays a key role in building a robust CECL process. By eliminating disconnected systems, data inconsistencies become far less common. With more reliable information, fewer gaps exist to undermine confidence in CECL model outputs.

Integrated data environments help institutions adapt as methodologies and portfolio complexity evolve. This improves accuracy and alignment across CECL, ALM, budgeting, planning, and stress testing functions.

Integration also simplifies audits, CECL model validation, and scenario analysis. As part of policy and procedure documentation, institutions should maintain a data flow document that shows where CECL data originates, how it moves through systems, and how assumptions or adjustments are applied. This makes it easier to document the rationale behind certain assumptions and methodologies.

How Empyrean Simplifies CECL Data Requirements

Strong CECL data requirements begin with consistent, reliable information across all teams within a financial institution. Empyrean’s CECL platform provides banks with a centralized source of data that connects each team with the same information.

Empyrean’s platform also enables automated workflows to improve efficiency and consistency in reporting. Combined with centralized data, institutions can create audit-ready results with a greater degree of confidence in accuracy and assumptions.

Since data is fundamental to an effective CECL platform, systems must be adaptable for current and future needs. Integrated data environments help institutions adapt as methodologies and portfolio complexity evolve.

Explore Empyrean CECL to learn about improving CECL workflows. Or request a demo to see how it can work with your institution.

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