The following tables represent, by loan portfolio segment, a summary of changes in the ACL on loans for the nine months ended September 30, 2022 and 2021:


	Three Months Ended September 30, 2022
	Commercial		Real Estate		Mortgage Warehouse		Consumer		Total
Balance, beginning of period	$	34,802	$	4,422	$	1,067	$	12,059	$	52,350
Provision for credit losses on loans	(703)		640		(43)		(495)		(601)
PCD loan charge–offs	(242)		—		—		—		(242)
Charge–offs	(103)		(27)		—		(592)		(722)
Recoveries	52		102		—		430		584
Balance, end of period	$	33,806	$	5,137	$	1,024	$	11,402	$	51,369


	Three Months Ended September 30, 2021
	Commercial		Real Estate		Mortgage Warehouse		Consumer		Total
Balance, beginning of period	$	41,766	$	4,108	$	1,155	$	8,620	$	55,649

Provision for credit losses on loans	1,330		(400)		(101)		283		1,112
Charge–offs	(10)		—		—		(189)		(199)
Recoveries	35		29		—		153		217
Balance, end of period	$	43,121	$	3,737	$	1,054	$	8,867	$	56,779


	Nine Months Ended September 30, 2022
	Commercial		Real Estate		Mortgage Warehouse		Consumer		Total
Balance, beginning of period	$	40,775	$	3,856	$	1,059	$	8,596	$	54,286


Provision for credit losses on loans	(6,343)		1,236		(35)		3,395		(1,747)
PCD loan charge–offs	(612)		—		—		—		(612)
Charge–offs	(240)		(85)		—		(1,605)		(1,930)
Recoveries	226		130		—		1,016		1,372
Balance, end of period	$	33,806	$	5,137	$	1,024	$	11,402	$	51,369


	Nine Months Ended September 30, 2021
	Commercial		Real Estate		Mortgage Warehouse		Consumer		Total
Balance, beginning of period	$	42,210	$	4,620	$	1,267	$	8,930	$	57,027


Provision for credit losses on loans	1,090		(1,000)		(213)		110		(13)
PCD loan charge–offs	(6)		—		—		—		(6)
Charge–offs	(273)		—		—		(661)		(934)
Recoveries	100		117		—		488		705
Balance, end of period	$	43,121	$	3,737	$	1,054	$	8,867	$	56,779

The Company utilized the Cumulative Loss Rate method in determining expected future credit losses. The loss rate method measures the amount of loan charge–offs, net of recoveries, (“loan losses”) recognized over the life of a pool and compares those loan losses to the outstanding loan balance of that pool as of a specific point in time (“pool date”).

To estimate a CECL loss rate for the pool, management first identifies the loan losses recognized between the pool date and the reporting date for the pool and determines which loan losses were related to loans outstanding at the pool date. The loss rate method then divides the loan losses recognized on loans outstanding as of the pool date by the outstanding loan balance as of the pool date.

The Company’s expected loss estimate is anchored in historical credit loss experience, with an emphasis on all available portfolio data. The Company's historical look–back period includes January 2012 through the current period, on a monthly basis. When historical credit loss experience is not sufficient for a specific portfolio, the Company may supplement its own portfolio data with external models or data.

Qualitative reserves reflect management’s overall estimate of the extent to which current expected credit losses on collectively evaluated loans will differ from historical loss experience. The analysis takes into consideration other analytics performed within the organization, such as enterprise and concentration management, along with other credit–related analytics as deemed appropriate. Management attempts to quantify qualitative reserves whenever possible.

The Company’s CECL estimate applies to a forecast that incorporates macroeconomic trends and other environmental factors. Management utilized National, Regional and Local Leading Economic Indexes, as well as management judgment, as the basis for the forecast period. The historical loss rate was utilized as the base rate, and qualitative adjustments were utilized to reflect the forecast and other relevant factors.

The Company segments the loan portfolio into pools based on the following risk characteristics: financial asset type, loan purpose, collateral type, loan characteristics, credit characteristics, outstanding loan balances, contractual terms and prepayment assumptions, industry of the borrower and concentrations, and historical or expected credit loss patterns.