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Recent Accounting Standards Update (Policy)
12 Months Ended
Dec. 31, 2020
Recent Accounting Standards Update ("ASU")  
Adoption of New Accounting Standards

New Accounting Standards Adopted in 2020:

ASU 2017-12, Derivatives and Hedging (Topic 815): Targeted Improvements to Accounting for Hedging Activities

Issued: August 2017

Summary: ASU 2017-12 simplifies the application of hedge accounting. More specifically, the amendments in this update better align an entity’s risk management activities and financial reporting for hedging relationships through changes to both the designation and measurement guidance for qualifying hedging relationships and the presentation of hedge results. Furthermore, the amendments expand and refine hedge accounting for both nonfinancial and financial risk components and align the recognition and presentation of the effects of the hedging instrument and the hedged item in the financial statements. Additionally, amendments in this update require an entity to present the earnings effect of the hedging instrument in the same income statement line item in which the earnings effect of the hedged item is reported. edge ineffectiveness is no longer separately measured and reported.

Effective Date: The ASU is effective for fiscal years beginning after December 15, 2018 and interim periods within those years. The Company adopted the ASU in April 2020 when it established its first derivative contracts.

Pending Accounting Standards:

ASU 2016-13, Financial Instruments – Credit Losses (Topic 326): Measurement of Credit Losses on Financial Instruments

Issued: June 2016

Summary: ASU 2016-13 requires credit losses on most financial assets to be measured at amortized cost and certain other instruments to be measured using an expected credit loss model (referred to as the current expected credit loss (“CECL”) model). Under this model, entities will estimate credit losses over the entire contractual term of the instrument (considering estimated prepayments, but not expected extensions or modifications unless reasonable expectation of a troubled debt restructuring exists) from the date of initial recognition of that instrument.

The ASU also replaces the current accounting model for purchased credit impaired loans and debt securities. The allowance for credit losses for purchased financial assets with a more-than insignificant amount of credit deterioration since origination (“PCD financial assets”), should be determined in a similar manner to other financial assets measured on an amortized cost basis. However, upon initial recognition, the allowance for credit losses is added to the purchase price (“gross up approach”) to determine the initial amortized cost basis. The subsequent accounting for PCD financial assets is the same expected loss model described above.

Further, the ASU made certain targeted amendments to the existing impairment model for available for sale (“AFS”) debt securities. For an AFS debt security for which there is neither the intent nor a more-likely-than-not requirement to sell, an entity will record credit losses as an allowance rather than a write-down of the amortized cost basis.

Effective Date: On October 16, 2019, the FASB voted and approved to delay the effective date of this ASU for smaller reporting companies until fiscal years beginning after December 15, 2022. Since the Company is a smaller reporting company, the approved delay by the FASB is applicable. While the Company’s senior management is currently in the process of evaluating the impact of the amended guidance on its consolidated financial statements and disclosures, it expects the allowance for loan and lease losses (“ALLL”) to increase upon adoption given that the allowance will be required to cover the full remaining expected life of the portfolio, rather than the incurred loss under current U.S. GAAP. The extent of this increase is still being evaluated and will depend on economic conditions and the composition of the Company’s loan portfolio at the time of adoption. In preparation, the Company has taken steps to prepare for the implementation when it becomes effective by forming an internal taskforce, gathering pertinent data, participating in training courses, and partnering with a software provider that specializes in ALLL analysis, as well as assessing the sufficiency of data currently available through its core database.