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Commercial Bank: CECL Model Validation

A commercial bank required an independent validation of its CECL models. The models are embedded into three platforms (Trepp, Impairment Studio and Evolv) and included the following:

  • Trepp Default Model (Trepp DM) is used by the Bank to estimate the PD, LGD and EL of the CRE portfolio
  • Moody’s ImpairmentStudio – Lifetime Loss Rate (LLR) Model is used to calculate the Lifetime Loss Rate for the C&I portfolio
  • EVOLV – Lifetime Loss Rate (LLR) model is used to calculate the Lifetime Loss Rate for Capital Call and Venture Capital loans within the Commercial and Industrial (C&I) segment, Non-rated Commercial loans, Consumer as well as Municipal loans
  • EVOLV – Base Loss Rate (BLR) model is used to calculate quantitative allowance for 1-4 Family commercial loans and Personal loans for commercial use within the C&I segment Residential loans, HELOC and Indirect vehicle.

The Solution

Because the CECL models are embedded into three platforms, RiskSpan conducted an independent, comprehensive validation of all three platforms.

Our validation included components typical of a full-scope model validation, focusing on a conceptual soundness review, process verification and outcomes analysis.

Deliverables 

RiskSpan was given access to the models’ platforms, and workpapers, along with the models’ development documentation, and weekly Q&A sessions with the model owners.

Our review evaluated:

i. the business requirements and purpose of the model, and the metrics that used by the developer to select the best model and evaluate its success in meeting these requirements will be judged.

ii. the identification and justification for

  (a) any theoretical basis for the model structure;

  (b) the use of specific developmental data;

  (c) the use of any statistical or econometric technique to estimate the model; and

  (d) the criteria used to identify and select the best model among alternatives.

iii. the reasonableness of model-development decisions, documented assumptions, data adjustments, and model-performance criteria as measured at the time of development.

iv. Process verification to determine the accuracy of data transcription, adjustment, transformation and model code.

RiskSpan produced a written validation report detailing its validation assessments, tests, and findings, and providing a summary assessment of the suitability of the models for their intended uses as an input to the bank’s CECL process, based upon the Conceptual Soundness Review and Process Verification.


Regional Bank: AML/BSA Model Validation

A large regional bank required a qualified, independent third party to perform risk-based procedures designed to provide reasonable assurance that its FCRM anti-money laundering system’s transaction monitoring, customer risk rating, and watch list filtering applications were functioning as designed and intended.

The Solution

RiskSpan reviewed existing materials, past audits and results, testing protocols and all documentation related to the bank’s model risk management standards, model setup and execution. We inventoried all model data sources, scoring processes and outputs related to the AML system.

The solution consisted of testing each of the five model segments: Design and Development; Input Processing; Implementation; Output and Use; and Performance.

The solution also quantified risk and exposure of identified gaps and limitations and presented sound industry practices and resolutions. 

Deliverables

  • A sustainable and robust transaction monitoring tuning methodology, which documented the bank’s approach, processes to be executed, frequency of execution, and the governance structure for executing tuning and optimization in the AML model. This included collecting and assessing previous regulatory feedback.
  • A framework that included a formal, documented, consistent process for sampling and analysis procedures to evaluate the ALM system’s scenarios and change control documentation.
  • A process for managing model risk consistent with the bank’s examiner expectations and business needs.

Residential Mortgage REIT: End to End Loan Data Management and Analytics

An inflexible, locally installed risk management system with dated technology required a large IT staff to support it and was incurring high internal maintenance costs.

Absent a single solution, the use of multiple vendors for pricing and risk analytics, prepay/credit models and data storage created inefficiencies in workflow and an administrative burden to maintain.

Inconsistent data and QC across the various sources was also creating a number of data integrity issues.

The Solution

An end-to-end data and risk management solution. The REIT implemented RiskSpan’s Edge Platform, which provides value, cost and operational efficiencies.

  • Scalable, cloud-native technology
  • Increased flexibility to run analytics at loan level; additional interactive / ad-hoc analytics
  • Reliable, accurate data with more frequent updates

Deliverables 

Consolidating from five vendors down to a single platform enabled the REIT to streamline workflows and automate processes, resulting in a 32% annual cost savings and 46% fewer resources required for maintenance.


GSE: Earnings Forecasting Framework Development

A $100+ billion government-sponsored enterprise with more than $3 trillion in assets sought to develop an end-to-end earnings forecast framework to project and stress-test the future performance of its loan portfolio. The comprehensive framework needed to draw data from a combination of unintegrated systems to compute earnings, capital management requirements and other ad hoc reporting under a variety of internal and regulatory (i.e., DFAST) stress scenarios. 

Computing the required metrics required cross-functional team coordination, proper data governance, and a reliable audit trail, all of which were posing a challenge.  

The Solution

RiskSpan addressed these needs via three interdependent workstreams: 

Data Preparation

RiskSpan consolidated multiple data sources required by the earnings forecast framework. These included: 

  • Macroeconomic drivers, including interest rates and unemployment rate 
  • Book profile, including up-to-date snapshots of the portfolio’s performance data 
  • Modeling assumptions, including portfolio performance history and other asset characteristics 

Model Simulation

Because the portfolio in question consisted principally of mortgage assets, RiskSpan incorporated more than 20 models into the framework, including (among others): 

  • Prepayment Model 
  • Default Model 
  • Delinquency Model 
  • Acquisition Model: Future loans 
  • Severity Model  
  • Cash Flow Model 

Business Calculations and Reporting

Using the data and models above, RiskSpan incorporated the following outputs into the earnings forecast framework: 

  • Non-performing asset treatment 
  • When to charge-off delinquent loans 
  • Projected loan losses under FAS114/CECL  
  • Revenue Forecasts 
  • Capital Forecast 

Client Benefits

The earnings forecast framework RiskSpan developed represented a significant improvement over the client’s previous system of disconnected data, unintegrated models, and error-prone workarounds. Benefits of the new system included:  

  • User Interface – Improved process for managing loan lifecycles and GUI-based process execution  
  • Data Lineage – Implemented necessary constraints to ensure forecasting processes are executed in sequence and are repeatable. Created a predefined, dynamic output lineage tree (UI-accessible) to build robust data flow sequence used to facilitate what-if scenario analysis. 
  • Run Management – Assigned a unique run ID to every execution to ensure individual users across the institution can track and reuse execution results 
  • Audit Trail – Designed logging of forecasting run details to trace attributes such as version changes (Version control system – GIT, SVN), timestamp, run owner, and inputs used (MySQL/Oracle Databases for logging)  
  • Identity Access Management – User IDs and access is now managed administratively. Metadata is captured via user actions through the framework for audit purposes. Role-based restrictions now ensure data and forecasting features are limited to only those who require such permissions 
  • Golden Configuration – Implemented execution-specific parameters passed to models during runtime. These parameters are stored, enabling any past model result to be reproduced if needed 
  • Data Masking – Encrypted personally identifiable information at-rest and in transit 
  • Data Management – Execution logs and model/report outputs are stored to the database and file systems 
  • Comprehensive User and Technical Documentation – RiskSpan created audit-ready documentation tied to logic changes and execution. This included source-to-target mapping documentation and enterprise-grade catalogs and data dictionaries. Documentation also included: 
      • Vision Document 
      • User Guides 
      • Testing Evidence 
      • Feature Traceability Matrix 

Large Asset Manager: Implementation of Comprehensive, Fully-Managed Risk Management Reporting System

An asset manager sought to replace an inflexible risk system provided by a Wall Street dealer. ​The portfolio was diverse, with a sizable concentration in structured securities and mortgage assets. ​

The legacy analytics system was rigid with no flexibility to vary scenarios or critical investor and regulatory reporting.

The Solution

RiskSpan’s Edge Platform delivered a cost-efficient and flexible solution by bundling required data feeds, predictive models for mortgage and structured products, and infrastructure management. ​

The Platform manages and validates the asset manager’s third-party and portfolio data and produces scenario analytics in a secure hosted environment. ​

Models + Data management = End-to-end Managed Process

The Edge We Provided

”Our existing daily process for calculating, validating, and reporting on key market and credit risk metrics required significant manual work… [Edge] gets us to the answers faster, putting us in a better position to identify exposures and address potential problems.” 

                        — Managing Director, Securitized Products  


RS Edge for Loans & Structured Products: A Data Driven Approach to Pre-Trade and Pricing  

The non-agency residential-mortgage-backed-securities (RMBS) market has high expectations for increased volume in 2020. Driven largely by expected changes to the qualified mortgage (QM) patch, private-label securities (PLS) issuers and investors are preparing for a 2020 surge. The tight underwriting standards of the post-crisis era are loosening and will continue to loosen if debt-to-income restrictions are lifted with changes to the QM patch 

PLS programs can differ greatly. It’s increasingly important to understand the risks inherent in each underlying poolAt the same time, investment opportunities with substantial yield are becoming harder to find without developing a deep understanding of the riskier components of the capital structureA structured approach to pre-trade and portfolio analytics can help mitigate some of these challenges. Using a data-driven approach, portfolio managers can gain confidence in the positions they take and make data influenced pricing decisions 

Industry best practice for pre-trade analysis is to employ a holistic approach to RMBS. To do this, portfolio managers must combine analysis of loan collateral, historical data for similar cohorts of loans (within previous deals), and scenariofor projected performance. The foundation of this approach is:  

  • Historical data can ground assumptions about projected performance 
  • A consistent approach from deal to deal will illuminate shifting risks from shifting collateral 
  • Scenario analysis will inform risk assessment and investment decision  

Analytical Framework 

RiskSpan’s modeling and analytics expert, Janet Jozwik, suggests a framework for analyzing a new RMBS deal with analysis of 3 main components:  deal collateral, historical performance, and scenario forecasting. Combined, these three components give portfolio managers a present, past, and future view into the deal.  

Present: Deal Collateral Analysis 

Deal collateral analysis consists of: 1) a deep dive into the characteristics of the collateral underlying the deal itself, and 2) a comparison of the collateral characteristics of the deal being analyzed to similar deals. A comparison to recently issued deals can highlight shifts in underlying collateral risk within a particular shelf or across issuers.  

Below, RiskSpan’s RS Edge provides the portfolio manager with a dashboard highlighting key collateral characteristics that may influence deal performance. 

Example 1Deal Profile Stratification 

deal-compare-in-rs-edge

Example 2Deal Comparative Analysis 

Deal Profile Stratification

Past: Historical Performance Analysis 

Historical analysis informs users of a deal’s potential performance under different scenarios by looking at how similar loan cohorts from prior deals have performedJozwik recommends analyzing historical trends both from the recent past and frohistorical stress vintages to give a sense for what the expected performance of the deal will be, and what the worst-case performance would be under stress scenarios. 

Recent Trend Analysis:  Portfolio managers can understand expected performance by looking at how similar deals have been performing over the prior 2 to 3 years. There are a significant number of recently issued PLS that can be tracked to understand recent prepayment and default trends in the market. While the performance of these recent deals doesn’t definitively determine expectations for a new deal (as things can change, such as rate environment), it provides one data point to help ground data-driven analyses. This approach allows users to capitalize on the knowledge gained from prior market trends.  

Historical Vintage Proxy Analysis:  Portfolio managers can understand stressed performance of the deal by looking at performance of similar loans from vintages that experienced the stress environment of the housing crisisThough potentially cumbersome to execute, this approach leverages the rich set of historical performance data available in the mortgage space 

For a new RMBS Dealportfolio managers can review the distribution of key features, such as FICO, LTV, and documentation typeThey can calculate performance metrics, such as cumulative loss and default rates, from a wide set of historical performance data on RMBS, cut by vintage. When pulling these historical numbers, portfolio managers can adjust the population of loans to better align with the distribution of key loan features in the deal they are analyzing. So, they can get a view into how a similar loans pool originated in historical vintages, like 2007, performed. There are certainly underwriting changes that have occurred in the post-crisis era that would likely make this analysis ultraconservative. These ‘proxy cohorts’ from historical vintages can provide an alternative insight into what could happen in a worst-case scenario.  

Future: Forecasting Scenario Analysis 

Forecasting analysis should come in two flavors. First, very straightforward scenarios that are explicitly transparent about assumptions for CPR, CDR, and severity. These assumptions-based scenarios can be informed with outputs from the Historical Performance Analysis above.  

Second, forecasting analysis can leverage statistical models that consider both loan features and macroeconomic inputs. Scenarios can be built around macroeconomic inputs to the model to better understand how collateral and bond performance will change with changing economic conditions.  Macroeconomic inputs, such as mortgage rates and home prices, can be specified to create particular scenario runs. 

How RiskSpan Can Help 

Pulling the required data and models together is typically a burdenRiskSpan’s RS Edge has solved these issues and now offers one integrated solution for:  

  • Historical Data: Loan-level performance and collateral data on historical and pre-issue RMBS deals 
  • Predictive Models: Credit and Prepayment models for non-agency collateral types 
  • Deal Cashflow Engine: Intex is the leading source for an RMBS deal cashflow library 

There is a rich source of data, models, and analytics that can support decision making in the RMBS market. The challenge for a portfolio manager is piecing these often-disparate pieces of information together to a cohesive analysis that can provide a consistent view from deal to dealFurther, there is a massive amount of historical data in the mortgage space, containing a vast wealth of insight to help inform investment decisions. However, these datasets are notoriously unwieldy. Users of RS Edge cut through the complications of large, disparate datasets for clear, informative analysis, without the need for custom-built technology or analysts with advanced coding skills.


FHFA 3Q2019 Prepayment Monitoring Report

FHFA’s 2014 Strategic Plan for the Conservatorships of Fannie Mae and Freddie Mac includes the goal of improving the overall liquidity of Fannie Mae’s and Freddie Mac’s (the Enterprises) securities through the development of a common mortgage-backed security. This report provides insight into how FHFA monitors the consistency of prepayment rates across cohorts of the Enterprises’ TBA-eligible MBS.

Download Report


Introducing: RS Edge for Loans and Structured Products

RiskSpan Introduces RS Edge for Loans and Structured Products  

RiskSpan, the leading mortgage data and analytics provider, is excited to announce the release of RS Edge for Loans and Structured Products. 

RS Edge is the next generation of RiskSpan’s data, modeling, and analytics platform that manages portfolio risk and delivers powerful analysis for loans and structured products.  Users can derive insights from historical trends and powerful predictive forecasts under a range of economic scenarios on our cloud-native solution. RS Edge streamlines analysis by bringing together key industry data and integrations with leading 3rd party vendors. 

An on-demand team of data scientists, quants, and technologists with fixed-income portfolio expertise support the integration, calibration, and operation across all RS Edge modules 

RMBS Analytics in Action 

RiskSpan has developed a holistic approach to RMBS analysis that combines loan collateral, historical, and scenario analysis with deal comparison tools to more accurately predict future performance. Asset managers can define an acceptable level of risk and ground pricing decisions with data-driven analysis. This approach illuminates risk from shifting collateral and provides investors with confidence in their positions. 

Loan Analytics in Action 

Whole loan asset managers and investors use RiskSpan’s Loan Analytics to enhance and automate partnerships with Non-Qualified Mortgage originators and servicers. The product enhances the on-boarding, pricing analytics, forecasting, and storage of loan data for historical trend analytics. RS Edge forecasting analytics support ratesheet validation and loan pricing 

About RiskSpan 

RiskSpan provides innovative technology and services to the financial services industry. Our mission is to eliminate inefficiencies in loans and structured finance markets to improve investors’ bottom line through incremental cost savings, improved return on investment, and mitigated risk.  

RiskSpan is holding a webinar on November 6 to show how RS Edge pulls together past, present, and future for insights into new RMBS deals. Click below to register.


Navigating the Impact of ASU 2016-13 on AFS Securities

In Collaboration With Our Partners at Grant Thornton

Navigating the impact of ASU 2016-13 on the impairment of AFS debt securities

When the Financial Accounting Standards Board (FASB) issued Accounting Standards Update (ASU) 2016-13, Financial Instruments – Credit Losses, in June of 2016, most of the headlines regarding the ASU focused on its introduction of Subtopic 326-20, commonly referred to as the Current Expected Credit Losses (or, “CECL”) framework.  The CECL framework requires entities to measure lifetime expected credit losses on all financial instruments measured at amortized cost – financial assets like loans receivable and held-to-maturity debt securities.  The focus on the CECL framework was understandable – it represents a sea change in the accounting for a significant class of assets for many entities, particularly lending institutions.

However, ASU 2016-13 affected the accounting for credit losses on other financial instruments as well, such as debt securities held as available-for-sale (or “AFS”).  Below, we will discuss how ASU 2016-13 changed the accounting for credit losses on AFS debt securities.

AFS Framework prior to adopting ASU 2016-13:  OTTI

Prior to an entity’s adoption of ASU 2016-13, the guidance concerning impairment of AFS debt securities is found in Subtopic 320-10, particularly in paragraphs 320-10-35-18 through 35-34, and is known as the Other-Than-Temporary Impairment (or “OTTI”) framework.

Generally, AFS debt securities are carried on the balance sheet at fair value, and changes in the fair value of AFS debt securities are recognized outside of earnings as a component of Other Comprehensive Income (OCI). However, if an AFS debt security’s fair value is less than its amortized cost – that is, the AFS debt security is impaired – the entity must evaluate whether the impairment is an OTTI.

An entity should recognize an OTTI on an impaired security when one of three conditions exists:

  1. The entity intends to sell the security
  2. It is more likely than not the entity will be required to sell the security prior to recovery of the amortized cost basis of the security
  3. The entity does not expect to recover the amortized cost basis of the security

If condition (1) or (2) exists, then the entity will reduce the amortized cost basis of the AFS debt security to its current fair value.  Any subsequent increases in the fair value of the AFS debt security would be recognized outside of earnings as a component of OCI until the gains are realized via cash collection or sale.

If neither condition (1) nor (2) exists, then the entity must evaluate whether it does not expect to recover the amortized cost basis of the security.  The entity may perform a qualitative analysis, considering factors such as the magnitude of the impairment, the duration of the impairment, factors relevant to the issuer of the security, factors relevant to the industry in which the issuer of the security operates, and any other relevant information.  Alternatively, an entity may perform a quantitative analysis by comparing the net present value (NPV) of expected cash flows of the AFS debt security to its amortized cost basis, as described below.

If the entity does not expect to recover the amortized cost basis of the security, an OTTI exists and the security should be written down to its fair value.  The entity must then separate the total impairment (the amount by which the AFS debt security’s amortized cost exceeds its fair value) between the amount of impairment related to (a) credit losses and (b) all other factors.  To make this distinction, the entity compares the NPV of the expected future cash flows on the debt security, discounted at the security’s effective interest rate (or “EIR”), to the amortized cost basis of the security.   The amount by which the amortized cost of the AFS debt security exceeds its NPV is recognized in earnings as a credit loss, while any remaining impairment is recognized outside of earnings as a component of OCI.

AFS Framework upon adopting ASU 2016-13

ASU 2016-13 largely keeps the OTTI framework from Subtopic 320-10 intact.  If either (1) an entity intends to sell, or (2) it is more likely than not that it will be required to sell an AFS debt security whose amortized cost exceeds its fair value, the entity shall write that AFS debt security’s amortized cost basis down to its fair value through earnings.  For AFS debt securities that are impaired, but for which neither (1) the entity intends to sell, nor (2) it is more likely than not that it will be required to sell an AFS debt security whose amortized cost exceeds its fair value, the entity will still need to assess whether it expects to recover the amortized cost basis of the impaired AFS debt security either via a qualitative analysis or via the same quantitative framework in Subtopic 320-10 today (as described above).

However, ASU 2016-13 makes a few important changes.  The most significant changes include:

  • Entities may no longer consider the duration of an impairment when qualitatively assessing whether the entity does not expect to recover the amortized cost basis of an impaired AFS debt security.
  • If an entity recognizes a credit loss on an AFS debt security, the entity will establish an allowance for credit loss (or “ACL”) rather than perform a direct write-down of the amortized cost basis of the AFS debt security. Accordingly, subsequent reductions in the estimated ACL will be recognized in earnings as they occur.
  • The amount of credit losses to be recognized is limited by a “fair value floor” – that is, total credit losses cannot exceed the total amount by which the amortized cost of the AFS debt security exceeds its fair value.

The following flow chart illustrates the how an entity would evaluate an AFS debt security for impairment upon adoption of ASU 2016-13:

Example

blog-chart

Background

  • Entity A has an investment in an AFS debt security issued by Company X with an amortized cost of $100
  • At 12/31/X1, the fair value of the AFS debt security is $90
  • The of the AFS debt security is 10% (as determined in accordance with ASC 310-20)

Entity A does not intend to sell the AFS debt security, nor is it more likely than not that Entity A will be required to sell the AFS debt security prior to recovery of the amortized cost basis.  Entity A elects to perform a qualitative analysis to determine whether the AFS debt security has experienced a credit loss.  In performing that qualitative assessment, Entity A consider the following:

  • Extent of impairment: 10%
  • Adverse conditions: Company X is in an industry that is in decline
  • Company X’s credit rating was recently downgraded

Accordingly, Entity A determines that a credit loss has occurred.  Next, Entity A makes its best estimate of expected future cash flows, and discounts those cash flows to their NPV at the AFS debt security’s EIR of 10% as follows:

Future Expected Cash Flows

In this case, the NPV is $85, which would indicate a $15 ACL.  However, the fair value of the AFS debt security is $90, so the ACL is limited to $10 due to the “fair value floor”.  Accordingly, Entity A would recognize a credit loss expense of $10 and create an ACL, also for $10.

In subsequent periods, Entity A would continue to determine the NPV of future expected cash flows and adjust the ACL up or down as those changes occur, subject to the fair value floor.[/vc_column_text][/vc_column][/vc_row][vc_row][vc_column][vc_empty_space][startapp_block_title animation=”” title=”About the Author”][/vc_column][/vc_row][vc_row][vc_column width=”1/6″][vc_single_image image=”2439″][/vc_column][vc_column width=”5/6″][vc_column_text]Graham Dyer, CPA Grant Thornton

Graham is a partner in Grant Thornton, LLP’s national office where he provides technical accounting guidance to clients across the globe.  Graham has a particular focus on financial institutions, including matters such as the ALLL, consolidations, Purchased Credit Impaired loan income recognition, complex financial instruments, business combinations, and SOX/FDICIA matters. ​

Graham also serves on a number of industry technical committees, including the IASB’s IFRS 9 Impairment Transition Group and the FASB’s CECL Transition Resource Group.  Graham was previously a professional accounting fellow at the OCC.


RiskSpan Joins AICPA for CECL Task Force Auditing Subgroup Meeting

RiskSpan joined a dozen other vendors and auditors from the top-ten accounting firms for the AICPA’s CECL Task Force Auditing Subgroup meeting at Ernst & Young’s offices in New York on April 29th. The AICPA just released the “Key takeaways” from the meeting.

Among those key takeaways are:

  • Overarching Themes:
    • CECL is a “fresh start” from the incurred loss model.
      • CECL model estimates will be evaluated against ASC 326, not anchored to incurred loss model estimates.
      • Management may find it useful in validating their CECL model to understand what drove changes from ALLL levels today. However, management should be aware of potential anchoring, confirmation, availability biases that might occur when implementing the new standard.
  • Qualitative Adjustment Factors:
    • Conceptually, qualitative adjustments compensate for known limitations of the model. A less sophisticated model will likely require more qualitative adjustments and those adjustments may be greater in magnitude. Conversely, a more sophisticated model will likely require fewer qualitative adjustments and those adjustments may be less in magnitude
    • Due to fundamental changes in the model, nature and magnitude of the qualitative adjustments in the CECL model should be independently generated and not anchored to, or grounded in, the qualitative adjustments used in the current incurred loss model.
    • Management should not pre-determine the magnitude of the adjustment and then produce documentation to support it – the amount should be determined by a rigorous, repeatable, well documented process with appropriate internal controls around that process.
    • Adjustments to historical information and forecasts could be negative, positive, or no change. Regardless, it is important for management to understand, document, and support their rationale in all three scenarios.
  • Forecasting/Reversion
    • Forecasting
      • Reasonable and supportable forecasts should be objectively supported, analyzed and appropriately updated in a timely manner.
        • Adjustments should be determined through a concrete sequential thought process (rather than calculated and backed into).
        • Transition from reasonable and supportable forecasts to reversion techniques should be specific to the circumstances (i.e. reversion period and method may change, depending on economic conditions).
      • Should be developed by parties with relevant expertise
      • Should have internal controls in place over the selection of forecasted data and the source
      • Forecasted economic data utilized should be relevant to the portfolio (i.e. data specific to lending market may be more relevant than general, country-wide data).
      • Multiple scenarios
        • No requirement to consider multiple scenarios but may be helpful
        • Need robust support for the weighting used, which may be challenging
  • Data
    • Data used in models should be subject to controls that are designed to ensure completeness, accuracy and relevance to the portfolio (i.e., similar economic conditions, loan structure and underwriting). Data will also need to be available to external auditors for substantive testing.
    • Data should be evaluated for consistency – is the data consistent period over period (i.e., definition of default)?
    • Data aggregated by vendors may not have previously been subject to traceable, internal controls. Vendors, management, auditors and other interested parties must consider how to address such industry limitations prior to standard implementation.
    • If management is not able to validate the data (relevance, reliability and consistency), that data may be difficult to use in the financial reporting process.

RiskSpan joined the AICPA’s CECL Task Force Auditing Subgroup for a second meeting on June 27th. We will publish the “Key Takeaways” from that meeting when they are released.

Institutions are invited to reach out to us with any questions.


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