Correction: Khoiriah et al. (2026). Basel III Capital and Conservation Buffers: Implications for the Credit Risk and Financial Stability of Indonesian Banks. Journal of Risk and Financial Management, 19(4), 291

Error in Table

In the original publication (Khoiriah et al., 2026), there was a mistake in Table 6 as published. There is an error in the table’s title. The corrected

Table 6. DID output countercylical capital buffer (in the additional document).

Variables	Total Credit/TA	MSME Credit/TA	Non-MSME Credit/TA
(Intercept)	75.763 ***	0.494 ***	−0.196 ***
PostReg	2.096 **	−0.030 ***	0.028 ***
CC × Treatments	2.581 +	0.021 *	0.033 **
Lag (size)	−4.414 ***	−0.027 ***	0.028 ***
Lag (loans to total assets)	6.521 **	0.255 ***	0.273 ***
Lag (total asset growth)	16.673	0.024	−0.758 ***
Missed (income diversity)	−4.166 ***	−0.053 ***	0.040 ***
PostReg × CC × Treat	1.343	0.015	0.064 ***
No. Note.	5432	5432	5432
R2	0.079	0.160	0.181
R2 Adj.	0.078	0.159	0.180
AIC	49,659.1	−5968.1	−2773.2
BIC	49,718.5	−5908.7	−2713.8
Log. Lik	−24,820,558	2,993,049	−1386.7
F	66,465	147,299	171,471
RMSE	23.35	0.14	0.19

Note: + indicates p < 0.1, * indicates p < 0.05, ** shows p < 0.01, and *** indicates p < 0.001.

appears below.

In the original publication, there was a mistake in Table 7. Conservative DID output capital buffer (in Additional Information) as published. The data in this table is incorrect and needs to be replaced. The corrected

Table 7. DID output of CCB on Credit Performance.

Variables	Total Credit/TA	MSME/TA Credit	Non-MSME/TA Credit
(Intercept)	34.467 ***	62.378 ***	−51.815 ***
PostReg	−4.820 ***	−4.114 ***	−0.006
CCB × Treatments	1.006	2.920 **	−13.012 ***
lag (Size)	1.774 ***	−2.494 ***	6.013 ***
lag (total asset growth)	−0.804 ***	−0.166	−1.118 ***
lag (income diversity)	−0.074	−5.537 ***	3.077 ***
PostReg × CCB × Treat	−0.369	2.216 *	1.377
No. Obs.	5432	5432	5432
R2	0.051	0.109	0.159
R2 Adj.	0.050	0.108	0.158
AIC	44,006.5	44,381.6	47,397.9
BIC	44,059.3	44,434.4	47,450.7
Log.Lik.	−21,995.271	−22,182.807	−23,690.973
F	48.778	110.108	171.488
RMSE	13.88	14.36	18.96

Note: * indicates p < 0.05 ** shows p < 0.01, and *** indicates p < 0.001.

. DID output of CCB on Credit Performance appears below.

Text Correction

There was an error in the original publication. Given that the original Table 7 contained errors requiring correction, the relevant explanations in the main text (Sections 3.1 and 4) have been corrected accordingly.

A correction has been made to 3. Results 3.1. Results in the Capital Buffer, Paragraph 3:

In general, as presented in Table 7, CCB showed a decrease in total credit relative to total assets, as reflected by the negative coefficient of PostReg (−4.820 ***, p < 0.01), indicating that the policy decreases the capacity of the banking sector to support credit expansion. However, this effect was more pronounced in large banks (KBMI III & IV), which experienced a larger increase in MSME credit than small banks (KBMI I), as indicated by the positive coefficient of Post Reg × CCB × Treat (2.216 *, p < 0.1). Banks in KBMI III & IV significantly increase MSME credit after the implementation of CCB, reflecting that large banks have a better capacity to adjust their strategies to this regulation. However, the combined effect of regulation and CCB on total credit was insignificant, as demonstrated by the coefficient of PostReg × CC × Treat, indicating that the additional benefits of this policy were limited.

A correction has been made to 4. Discussion, Paragraphs 11 and 12:

Regarding SME financing, the CCB policy had a less favorable impact, especially for small banks. The analysis shows that the ratio of SME loans to total assets de-clines after policy implementation, as indicated by the negative PostReg coefficient (−4.114 ***, p < 0.001). This decline confirms that the additional capital requirements imposed by the CCB burden small banks, thus limiting their capacity to support SME financing. Large banks (KBMI 3 and 4) show a better ability to maintain SME financing, as indicated by the positive CCB × Treat coefficient (2.920 **, p < 0.05). However, the combined impact of regulation and policy on SME financing is significant, indicating that the policy has been fully optimal for large banks (KBMI 3 and 4) in supporting the SME sector, especially nationally. Wibowo and Aumenboonsuke (2020) also analyzed the banking sector’s ability to channel credit to SMEs during economic changes and fintech companies’ growth in Indonesia.

Banks face challenges in disbursing credit to SMEs, especially in the midst of fintech competition. For non-SME loans, the CCB policy had a negative impact. The ratio of non-SME loans to total assets decreased after the regulation, as indicated by the negative coefficient of PostReg (−0.006, p < 0.001). Large banks showed a negative response to this policy, with a CC × Treat coefficient of (−13.012 *** p < 0.01) and a significant combined effect of regulation and policy on the PostReg × CC × Treat variable (1.377, p < 0.001). These results indicate that the CCB policy is more effective than SME financing in encouraging corporate and large business sector financing.

The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.