Transition to CECL with MIAC

FASB's ASC Topic 326 (CECL), in conjunction with SR 16-12 (Interagency Guidance on CECL), SR 17-8 (CECL FAQs) and SR 11-7 (Model Risk Management), effectively formalizes current best practices for the forecasting of expected credit losses on loan assets.

MIAC's industry and regulator validated software, MIAC Analytics™, models expected credit losses for loan valuations, and no firm prices more loan assets than MIAC.

MIAC's CECL Process

Loan Data Capture

Verify that supplied loan file data matches the information contained in the loan documents; standardize formats. Perform compliance testing to ensure adherence to a variety of rules, policies, and regulations. Confirm credit and collateral underwriting agreement, in accordance with specific credit policies.

Historical Performance Metrics Calculated

Measuring key loan performance metrics and then using them in the forecasted cash flows is the key to CECL. Metrics include all pertinent stats: exit rates by cohort, outputs stratified by note rate,  by vintage, geography, loan type, and more. The correlation of macroeconomic variables with the frequency, timing, and severity of loss is then mathematically constructed. Our Surveillance module will then link with and inform our behavioral model to forecast future results, for determination of loss allowances.

Portfolio Compared with National Population

Importantly, institution-specific historical performance metrics are utilized whenever the client data set is insufficient statistically. Observe performance vs. to appropriate national cohorts again by note rate, by vintage, by geography, loan type, and more. Then, begin to construct reasonable scenario projections.

Multi-Scenario Losses Modeled

CECL differs from precedent as follows; new standards for both reserving and capital adequacy are forward-looking, based upon expectations of future cash flows under plausible scenarios, rather than only on historical losses to date. FASB has encouraged lenders to tailor their allowances and projections to match with their historical experience and trends, MIAC has helped its clients rigorously establish reasonability of scenarios, and defend projections.

CECL Valuation Allowance Determined and Approved

Industry best practices for CECL will require firms to perform cash flow forecasting for collateral assets segmented into buckets of similar collateral attributes blended with lender-realized loss experience and in consideration of macro-economic scenarios. Expected future credit losses are included within the cash flow forecasts, which facilitates detailed analysis of the amount of expected losses, as well as when they are expected to occur. FASB’s pronouncement nearly mirrors MIAC’s long-established methodology, which has been vetted, audited, and chosen by leading participants including lenders, banks, accountants and regulatory bodies.

CECL – Current Expected Credit Loss: A CORE Competency?

In this MIAC Perspectives article, we discuss the new standard, and why working with MIAC is the only choice for your CECL transition.

MIAC's CECL Solutions

MIAC and MIAC Analytics™ have been modeling expected credit losses for loan valuations since our formation, and no firm prices more loan assets than MIAC.

CECL Case Study

In this case study, we highlight how MIAC Analytics™ was used to model current expected credit losses for acquired distressed assets subject to SOP 03-3 on behalf of a bank lender in 2014.

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Event announcements, infographics, implementation guide releases, and real-time updates on the new standard

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DataRaptor®

Normalize, audit and stratify loan data:
Identify errors and exceptions, apply customized settings, and prepare data for valuation and analysis on MIAC’s SSAE-16 compliant platform.

DR - Surveillance™

Measure historical loan portfolio performance:
View voluntary prepayments, delinquency statistic, and transition roll rates within your portfolio compared to national average.

MIAC CORE™

Forecast frequency, timing, and severity of loss:
Attribute conditional response to changes in macro-economic factors – GDP, unemployment, HPI, CPI, and interest rates. 

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