The Company maintains an ACL for loans and unfunded loan commitments in accordance with ASC 326 - Financial Instruments - Credit Losses. ASC 326 requires the Company to recognize estimates for lifetime credit losses on loans and unfunded loan commitments at the time of origination or acquisition. The recognition of credit losses at origination or acquisition represents the Company’s best estimate of lifetime expected credit losses, given the facts and circumstances associated with a particular loan or group of loans with similar risk characteristics. Determining the ACL involves the use of significant management judgement and estimates, which are subject to change based on management’s ongoing assessment of the credit quality of the loan portfolio and changes in economic forecasts used in the model. The Company uses a discounted cash flow model when determining estimates for the ACL for commercial real estate loans and commercial loans, which comprise the majority of the loan portfolio, and uses a historical loss rate model for retail loans. The Company also utilizes proxy loan data in its ACL model where the Company’s own historical data is not sufficiently available.

The discounted cash flow model is applied on an instrument-by-instrument basis, and for loans with similar risk characteristics, to derive estimates for the lifetime ACL for each loan. The discounted cash flow methodology relies on several significant components essential to the development of estimates for future cash flows on loans and unfunded loan commitments. These components consist of: (i) the estimated PD, (ii) the estimated LGD, which represents the estimated severity of the loss when a loan is in default, (iii) estimates for prepayment activity on loans, and (iv) the estimated exposure to the Company at default (“EAD”). These components are heavily influenced by changes in economic forecasts employed in the model over a reasonable and supportable period. The Company’s ACL methodology for unfunded loan commitments also includes assumptions concerning the probability an unfunded commitment will be drawn upon by the borrower. These assumptions are based on the Company’s historical experience.

The Company’s discounted cash flow ACL model for commercial real estate and commercial loans uses internally derived estimates for prepayments in determining the amount and timing of future contractual cash flows expected to be collected. The estimate of future cash flows also incorporates estimates for contractual amounts the Company believes may not be collected, which are based on assumptions for PD, LGD, and EAD. EAD is the estimated outstanding balance of the loan at the time of default. It is determined by the contractual payment schedule and expected payment profile of the loan, incorporating estimates for expected prepayments and future draws on revolving credit facilities. The Company discounts cash flows using the effective interest rate on the loan. The effective interest rate represents the contractual rate on the loan; adjusted for any purchase premiums or discounts, and deferred fees and costs associated with an originated loan. The Company has made an accounting policy election to adjust the effective interest rate to take into consideration the effects of estimated prepayments. The ACL for loans is determined by measuring the amount by which a loan’s amortized cost exceeds its discounted cash flows expected to be collected. The ACL for credit facilities is determined by discounting estimates for cash flows not expected to be collected.

The PD for investor loans secured by real estate is based largely on a model provided by a third party, using proxy loan information. The PDs generated by this model are reflective of current and expected economic conditions and conditions in the commercial real estate market, and how they are expected to impact loan level and property level attributes, and ultimately the likelihood of a default event occurring. This model incorporates assumptions for PD at a loan’s maturity. Significant loan and property level attributes include: loan-to-value ratios, debt service coverage, loan size, loan vintage, and property types.

The PD for business loans secured by real estate and commercial loans is based on an internally developed PD rating scale that assigns PDs based on the Company’s internal credit risk grades for loans. This internally developed PD rating scale is based on a combination of the Company’s own historical data and observed historical data from the Company’s peers, which consist of banks that management believes align with our business profile. As credit risk grades change for these loans, the PD assigned to them also changes. As with investor loans secured by real estate, the PD for business loans secured by real estate and commercial loans is also impacted by current and expected economic conditions.

LGDs for commercial real estate loans are derived from a third party, using proxy loan information, and are based on loan and property level characteristics for loans in the Company’s loan portfolio, such as: loan-to-value ratios (“LTV”), estimated time to resolution, property size, and current and estimated future market price changes for underlying collateral. The LGD is highly dependent upon LTV ratios, and incorporates estimates for the expense associated with managing the loan through to resolution. LGDs also incorporate an estimate for the loss severity associated with loans where the borrower fails to meet their debt obligation at maturity, such as through a balloon payment or the refinancing of the loan through another lender. External factors that have an impact on LGDs include: changes in the index for CRE pricing, GDP growth rate, unemployment rates, and the Consumer Price Index. LGDs are applied to each loan in the commercial real estate portfolio, and in conjunction with the PD, produce estimates for net cash flows not expected to be collected over the estimated term of the loan.

LGDs for commercial loans are also derived from a third party that has a considerable database of credit related information specific to the financial services industry and the type of loans within this segment, and is used to generate annual default information for commercial loans. These proxy LGDs are dependent upon data inputs such as: credit quality, borrower industry, region, borrower size, and debt seniority. LGDs are then applied to each loan in the commercial segment, and in conjunction with the PD, produce estimates for net cash flows not expected to be collected over the estimated term of the loan.

In order to develop reasonable and supportable forecasts of future conditions, the Company estimates how those forecasts are expected to impact a borrower’s ability to satisfy their obligation to the Bank and the ultimate collectability of future cash flows over the life of a loan. The Company uses macroeconomic scenarios from an independent third party. These scenarios are based on past events, current conditions, and the likelihood of future events occurring. These scenarios typically are comprised of: a base-case scenario, an upside scenario, representing slightly better economic conditions than currently experienced and, a downside scenario, representing recessionary conditions. Management periodically evaluates appropriateness of economic scenarios and may decide that a particular economic scenario or a combination of probability-weighted economic scenarios should be used in the Company’s ACL model. The economic scenarios chosen for the model, the extent to which more than one scenario is used, and the weights that are assigned to them, are based on the likelihood that the economy would perform better than each scenario, which is based in part on analysis performed by an independent third party. Economic scenarios chosen, as well as the assumptions within those scenarios, and whether to use a probability-weighted multiple scenario approach, can vary from one period to the next based on changes in current and expected economic conditions, and due to the occurrence of specific events. The Company’s ACL model at December 31, 2022 includes assumptions concerning the rising interest rate environment, ongoing inflationary pressures throughout the U.S. economy, higher energy prices, the potential impact of the ongoing war between Russia and Ukraine, general uncertainty concerning future economic conditions, and the potential for recessionary conditions.

The Company currently forecasts PDs and LGDs based on economic scenarios over a two-year period, which we believe is a reasonable and supportable period. Beyond this point, PDs and LGDs revert to their long-term averages. The Company has reflected this reversion over a period of three years in each of its economic scenarios used to generate the overall probability-weighted forecast. Changes in economic forecasts impact the PD, LGD, and EAD for each loan, and therefore influence the amount of future cash flows the Company does not expect to collect for each loan.

It is important to note that the Company’s ACL model relies on multiple economic variables, which are used in several economic scenarios. Although no one economic variable can fully demonstrate the sensitivity of the ACL calculation to changes in the economic variables used in the model, the Company has identified certain economic variables that have significant influence in the Company’s model for determining the ACL. These key economic variables include changes in the U.S. unemployment rate, U.S. real GDP growth, CRE prices, and interest rates.

The Company recognizes that historical information used as the basis for determining future expected credit losses may not always, by itself, provide a sufficient basis for determining future expected credit losses. The Company, therefore, considers the need for qualitative adjustments to the ACL on a quarterly basis. Qualitative adjustments may be related to and include, but not be limited to, factors such as: (i) management’s assessment of economic forecasts used in the model and how those forecasts align with management’s overall evaluation of current and expected economic conditions, (ii) organization specific risks such as credit concentrations, collateral specific risks, regulatory risks, and external factors that may ultimately impact credit quality, (iii) potential model limitations such as limitations identified through back-testing, and other limitations associated with factors such as underwriting changes, acquisition of new portfolios, and changes in portfolio segmentation, and (iv) management’s overall assessment of the adequacy of the ACL, including an assessment of model data inputs used to determine the ACL.

As of December 31, 2022, qualitative adjustments primarily relate to certain segments of the loan portfolio deemed by management to be of a higher-risk profile or other factors where management believes the quantitative component of the Company’s ACL model may not be fully reflective of levels deemed adequate in the judgement of management. Certain qualitative adjustments also relate to heightened uncertainty as to future macroeconomic conditions and the related impact on certain loan segments. Management reviews the need for an appropriate level of qualitative adjustments on a quarterly basis, and as such, the amount and allocation of qualitative adjustments may change in future periods.


	For the Year Ended December 31, 2022

(Dollars in thousands)	Beginning ACL Balance		Charge-offs		Recoveries		Provision for Credit Losses		Ending ACL Balance
Investor loans secured by real estate
CRE non-owner-occupied	$	37,380	$	(4,760)	$	—	$	1,072	$	33,692
Multifamily	55,209		—		—		1,125		56,334
Construction and land	5,211		—		—		1,903		7,114
SBA secured by real estate	3,201		(70)		—		(539)		2,592
Business loans secured by real estate
CRE owner-occupied	29,575		—		56		2,709		32,340
Franchise real estate secured	7,985		—		—		(966)		7,019
SBA secured by real estate	4,866		—		—		(518)		4,348
Commercial loans
Commercial and industrial	38,136		(8,387)		2,904		2,516		35,169
Franchise non-real estate secured	15,084		(448)		—		1,393		16,029
SBA non-real estate secured	565		(50)		51		(125)		441

Retail loans
Single family residential	255		—		148		(51)		352
Consumer loans	285		(4)		—		(60)		221
Totals	$	197,752	$	(13,719)	$	3,159	$	8,459	$	195,651


	For the Year Ended December 31, 2021
(Dollars in thousands)	Beginning ACL Balance		Charge-offs		Recoveries		Provision for Credit Losses		Ending ACL Balance

Investor loans secured by real estate
CRE non-owner-occupied	$	49,176	$	(154)	$	78	$	(11,720)	$	37,380
Multifamily	62,534		—		—		(7,325)		55,209
Construction and land	12,435		—		—		(7,224)		5,211
SBA secured by real estate	5,159		(424)		3		(1,537)		3,201
Business loans secured by real estate
CRE owner-occupied	50,517		—		56		(20,998)		29,575
Franchise real estate secured	11,451		—		—		(3,466)		7,985
SBA secured by real estate	6,567		(98)		130		(1,733)		4,866
Commercial loans
Commercial and industrial	46,964		(5,384)		4,720		(8,164)		38,136
Franchise non-real estate secured	20,525		(2,554)		405		(3,292)		15,084
SBA non-real estate secured	995		—		21		(451)		565

Retail loans
Single family residential	1,204		—		3		(952)		255
Consumer loans	491		(2)		3		(207)		285
Totals	$	268,018	$	(8,616)	$	5,419	$	(67,069)	$	197,752


	For the Year Ended December 31, 2020
	Beginning ACL Balance (1)		Adoption of ASC 326		Initial ACL Recorded for PCD Loans		Charge-offs		Recoveries		Provision for Credit Losses		Ending ACL Balance
Investor loans secured by real estate
CRE non-owner-occupied	$	1,899	$	8,423	$	3,025	$	(839)	$	44	$	36,624	$	49,176
Multifamily	729		9,174		8,710		—		—		43,921		62,534
Construction and land	4,484		(124)		2,051		(539)		—		6,563		12,435
SBA secured by real estate	1,915		(1,401)		—		(705)		34		5,316		5,159
Business loans secured by real estate
CRE owner-occupied	2,781		20,166		3,766		(1,739)		59		25,484		50,517
Franchise real estate secured	592		5,199		—		(932)		—		6,592		11,451
SBA secured by real estate	2,119		2,207		235		(338)		147		2,197		6,567
Commercial loans
Commercial and industrial	13,857		87		2,325		(6,891)		1,818		35,768		46,964
Franchise non-real estate secured	5,816		9,214		—		(6,731)		866		11,360		20,525
SBA non-real estate secured	445		218		924		(899)		14		293		995

Retail loans
Single family residential	655		541		206		(106)		2		(94)		1,204
Consumer loans	406		1,982		—		(139)		4		(1,762)		491
Totals	$	35,698	$	55,686	$	21,242	$	(19,858)	$	2,988	$	172,262	$	268,018

The decrease in the ACL for loans held for investment during the year ended December 31, 2022 of $2.1 million is reflective of $10.6 million in net charge-offs, partially offset by $8.5 million in provision for credit losses. The provision for credit losses during the year ended December 31, 2022 was largely impacted by changes to the overall size, composition, and asset quality trends of the loan portfolio. Charge-offs during the year ended December 31, 2022 were largely attributed to one C&I lending relationship and two CRE non-owner-occupied lending relationships.

The decrease in the ACL for loans held for investment during the year ended December 31, 2021 of $70.3 million was reflective of a $67.1 million provision recapture and net charge-offs of $3.2 million. The provision recapture for the year ended December 31, 2021 was reflective of improved economic forecasts used in the Company’s ACL model, including a more favorable outlook for real estate prices and commercial vacancy rates. A favorable credit quality profile of the loan portfolio also contributed to the provision recapture and was partially offset by increases in loans held for investment during 2021.

The Company maintains an ACL for off-balance sheet commitments related to unfunded loans and lines of credit, which is included in other liabilities of the consolidated statements of financial position. The allowance for off-balance sheet commitments was $23.6 million and $27.3 million at December 31, 2022 and 2021, respectively. The decline in the allowance for off-balance sheet commitments in 2022 is attributed to a provision recapture for off-balance sheet commitments of $3.6 million, which was related primarily to changes in the mix of unfunded commitments between various loan segments, as well as qualitative adjustments during 2022.

The ACL for off-balance sheet commitments was $27.3 million and $31.1 million at December 31, 2021 and 2020, respectively. The decline in the allowance for off-balance sheet commitments in 2021 was attributed to a provision recapture of $3.8 million, which was related primarily to improving economic conditions and forecasts reflected in the Company’s ACL model, partially offset by increases in unfunded loan commitments during 2021.