Keywords

Credit Card, Non-Performing Loan, Net Interest margin, Return on Assets.

Research Background

In the current era of digitalization, to carry out business transactions, you can use various payment facilities, ranging from conventional ones to the most modern ways. In line with the development of information technology found the most efficient and effective way to make payment transactions using a plastic card or known as a debit card or credit card that serves as a means of payment instead of cash. Debit cards circulating in Indonesia have reached 237.35 million pieces as of April 2022 while credit cards circulating in the same period reached 16.56 million pieces or 6.97% compared to debit cards in circulation, this is because debit cards were introduced earlier. Compared to a credit card and for applying for a credit card requires quite a lot of requirements.

The number of debit cards in circulation is 14.8 times the number of credit cards, but since 2013 the number of users and the number of credit card transactions have not decreased despite the high inflation rate which has impacted people's purchasing power. The development of debit cards and credit cards can be seen in the following table 1, table 2, table 3 & table 4:

Table 1 Debit Cards in Circulation and Transaction Volume in Indonesia
Year	Credit Card (Millions of Units)	Transaction Volume (in Thousands IDR)
		Cash Transaction			Shopping Transaction
		Domestic	International	Total	Domestic	International	Total
2013	15,09	3.310	93	3.403	218.834	16.862	235.696
2014	16,04	3.675	102	3.777	231.377	19. 1	250.543
2015	16,86	6.289	318	6.607	252.560	22. 159	274.719
2016	17,41	7.221	169	7.390	264.590	33.072	297.590
2017	17,24	7.697	119	8.086	282.49	36.801	319.292
2018	17,28	8.071	131	8.202	295.984	34.162	330. 146
2019	17,49	8.840	124	8.964	300.993	39.256	340.249
2020	16,94	6.413	60	6.473	233. 187	35.023	268.210
2021	16,51	4.803	47	4.850	240. 164	36.887	277.05
2022*)	16,56	1.641	5	1.646	103.9 17	2.369	106.286

Table 2 Debit Cards in Circulation and Transaction Value in Indonesia
Year	Debit Card (Millions of tmit)	Transaction Value (in billions of IDR)
		Cash	Shopping	Interbank Trf	Trf Between Bank	Total
2013	89,46	1.674.2 10	147. 113	1.507.368	468.679	3.797.370
20 14	105,83	1.920.781	180.64 1	1.705. 170	638.482	4.445.073
20 15	120,28	2. 100.785	210.386	1.847.494	739. 128	4.897.794
20 16	136,15	2.353.443	251.847	2. 121.258	897.365	5.623.913
20 17	164,48	2.528.879	286.2 14	2.3 17.678	1.067.666	6.200.438
20 18	161,33	2.837.544	292.830	2.586.868	1.209.577	6.926.8 19
20 19	183,43	3.204.459	332.906	2.648.794	1.288.665	7.474.824
2020	213,61	2.990.972	284.781	2.455.4 10	1.185.710	6.916.875
2021	226,30	3.143.608	336. 110	2.866.5 16	1.330.950	7.677. 185
2022*)	237,35	1.358.7 11	172.540	690.9 12	400.4 18	2.622.58 1

Table 3 Credit Cards in Circulation and Transaction Volume in Indonesia
Year	Credit Card (Millions of Units)	Transaction Volume (in Thousands IDR)
		Cash Transaction			Shopping Transaction
		Domestic	International	Total	Domestic	International	Total
2013	15,09	3.310	93	3.403	218.834	16.862	235.696
20 14	16,04	3.675	102	3.777	231.377	19.166	250.543
20 15	16,86	6.289	318	6.607	252.560	22. 159	274.7
20 16	17,4 1	7.221	169	7.390	264.590	33.072	297.590
20 17	17,24	7.697	119	8.086	282.49 1	36.801	319.292
20 18	17,28	8.071	131	8.202	295.984	34. 162	330.
20 19	17,49	8.840	124	8.964	300.993	39.256	340.249
2020	16,94	6.413	60	6.473	233. 187	35.023	268.2
2021	16,51	4.803	47	4.850	240. 164	36.887	277.05
2022*)	16,56	1.641	5	1.646	103.9 17	2.369	106.286

Table 4 Credit Cards in Circulation and Transaction Value in Indonesia
Year	Credit Card (Millions of Units)	Transaction Value (in billions of IDR)
		Cash Transaction			Shopping Transaction
		Domestic	International	Total	Domestic	International	Total
2013	15,09	4.070	273	4.343	194.27	24.756	219.027
2014	16,04	4.694	186	4.880	222. 158	28.020	250.178
2015	16,86	6.927	475	7.402	243.502	29.640	273. 142
2016	17,41	7.683	387	8.070	242.329	30.621	272.950
2017	17,24	8.478	370	8.848	254.326	34.587	288.913
2018	17,28	8.677	416	9.093	267.338	37.863	305.201
2019	17,49	9.626	412	10.038	290.483	42.162	332.645
2020	16,94	7.226	124	7.350	210.593	20.960	231.553
2021	16,5 1	6.667	100	6.767	214.272	23.477	237.749
2022*)	16,56	2.428	15	2.443	93.129	1.641	94.770

The community welcomes the existence of an easy and practical means of payment in transactions because there is no need to carry a lot of cash when traveling far, just bring a credit card. The credit card industry is one of the banking services that have grown very rapidly since its launch around 1989 until now showing an increasing trend from year to year. Therefore, it is an opportunity for the banking sector to develop the credit card business because it generates profitable margins.

Based on data from Bank Indonesia, the number of credit cards per 2013 reached 15.09 million pieces. This figure has increased until 2019 to 17.49 million pieces, an increase of 15.90% then in 2020 it decreased this was due to Bank Indonesia Regulation (PBI) No. 14/27/DAAP/2012, where BI limits credit card ownership, i.e. customers with incomes below IDR 10 million are allowed to have a maximum of two credit cards from two issuing banks. In 2021, according to a report by Bank Indonesia, the value of transactions with credit cards was IDR 25.91 trillion as of December 2021, an increase of 10.39% from November's IDR 24.47 trillion. Meanwhile, the volume of transactions with credit cards increased in December 2021 by 27.85 million transactions, up 5.57% from November's 26.38 million transactions.

In 2022, in the third year of the COVID-19 pandemic, the credit card business grew again, the volume grew 2.63% year on year to 281.9 times throughout 2021. In nominal terms the credit card business was able to grow 2.35% year on year to IDR 244.51 trillion last year. Meanwhile, the number of credit cards in circulation decreased by 2.52% year to year to 16.51 million pieces. This means that customers are starting to increase their transactions even though the pandemic is entering its third year. Bank Indonesia observes that payment transactions using cards are still able to grow in early 2022. In fact, digital economic and financial transactions are growing rapidly in the midst of the pandemic. Governor of Bank Indonesia (BI) Perry Warjiyo said payment transactions using ATM, Debit and credit cards also grew 14.39% year on year (YOY) to reach IDR 711.2 trillion in January 2022.

Seeing this potential, banking companies tried their luck and entered the business of providing credit card services. According to the General Manager of the Indonesian Credit Card Association (AKKI) Steve Marta, credit cards actually have fewer enthusiasts than debit cards. This happens because credit cards have more in-depth procedures and analysis for card holders. Steve also said that next year, AKKI projects that the number of credit card users will increase by 15 percent, reaching 16 million to 16.5 million and the transaction could increase by around 5 percent,” he added.

Credit cards circulating in Indonesia from Table 5 principals are as follows:

Table 5 List of Credit Card Principals
No.	Principal Name	Network Name
1	PT. JCB INTERNATIONAL INDONESIA	JCB
2	PT. MASTERCARD INDONESIA	MasterCard
3	PT. VISA WORLDWTDE INDONESIA	Visa card
4	PT. UNION PAY INDONESIA	CUP
5	AMERICAN EXPRESS	Amex

Since it was first issued in Indonesia, the number of credit cards has always increased from year to year. Accurately, the Indonesian Credit Card Association (AKKI) recorded that from 2009 to 2021 the number of cards, the number of transactions and the transaction value of card users in Indonesia always increased from year to year, which can be seen in tables 1 to 4.

Of the five principals of credit cards circulating in Indonesia, Indonesian people like Master card and Visa card because they are easy to use and work closely with Merchants in Indonesia. In transactions using cards, the required infrastructure is: 1. ATM machine, 2. EDC machine, and 3 Marchant.

Along with the increasing demand and increasingly consumptive in shopping, of course, banking companies as financial institutions have a very important role. This is an opportunity for banks to increase their income in the form of Net Interest Margin (NIM). Thus, the performance of the banking sector will increase. However, the banking sector must also pay attention to the assets quality of banking, the possible negative impact of using credit cards, namely the card owner does not pay on time and may not pay at all so that it will cause bad debts or Non-Performing Loans (NPL) which affect the bank's performance. Based on this background, the authors conducted a study entitled Analysis of asset quality and Net Interest Margin on the performance of credit card issuing banks.

Terms Related to Credit Cards

Visa

Visa is a credit card issuer that is recognized internationally. Is a Citigroup subsidiary that produces Citibank Visa is the largest credit card company in the world.

Master Card

MasterCard is one of the recognized credit card issuers in the international network. This credit card issued by Bank of America is one of the largest credit card companies in the world.

American Express

American Express is one of the recognized credit card issuers in the international network

Interest Rate

Interest is compensation/reward for services to lenders provided by borrowers of funds

Limit

The limit is the maximum amount set by the credit card for shopping which has been set by the credit card issuing bank. This limit can be increased but there will be an over limit fee

Over Limit

Over limit occurs when the credit card limit that has been spent is at its maximum. If the credit card user has used more than the credit card limit, an additional fee will be charged.

Annual Fee

Annual fee is a fee that credit card users must pay every year. Each credit card issuing bank has its own fee policy. And the dues that credit card users pay are different for each limit level.

Monthly Fee

Monthly fee is a fee that must be paid by credit card users every month. Usually this fee will be automatically accumulated with the total credit card bill each month. Each bank has a different policy in collecting fees from credit card users, this can be done on a monthly basis (Monthly Fee) or annually (Annual Fee).

Card Holder

Card Holder is the owner or credit card holder.

Withdraw Credit Card / Cash Advance

Cash Advance is one of the facilities of a credit card to withdraw money in cash by cutting the limit from the credit card. This cash advance can be made at ATMs or branch offices of the credit card issuing bank. However, keep in mind for those who want to make a cash advance, the interest charged will be higher.

Swipe Cash

Cash swipe is a withdrawal activity carried out by credit card users at certain merchants. So, it is as if the credit card holder makes a transaction through a credit card merchant. However, what the merchant provides the cash swipe service provider does not provide goods but cash.

Additional card

Supplementary Card is one of the bank facilities provided for family members of credit card users.

Credit Shield

Credit Shield is an additional facility for credit card holders if the credit card owner has an accident / dies so they cannot pay off the credit card bill. Credit Shield will make all bills on the credit card paid off, so that relatives or relatives do not need to pay off credit card bills.

Transfer Balance

Transfer Balance is the transfer of credit card bills to other credit cards in the same owner. Balance transfers can be made if the credit card owner wants to close his old credit card. Combined Limit Credit Combined Credit Limit is the limit amount for the main and additional credit cards.

Non-Performing Loans (NPL)

Non-performing loan is a comparison between non-performing loans and the total amount of loans disbursed to the community as a whole. According to Hariyani (2010) the NPL ratio is a ratio that shows the ability of bank management to manage bad loans provided by banks. The higher the NPL, the worse the credit quality of the bank which causes the bank to be in a bigger problem condition.

One of the risks faced by banks is the risk of non-payment of loans that have been given to debtors or called credit risk. According to Ghozali in Adicondro & Pangestuti (2015) credit risk is: "The risk faced by banks due to uncertainty or failure of installment payments (counterparties) in fulfilling their obligations".

Credit risk can be divided into 3 risks, namely:

1. Default Risk,

2. Exposure Risk and

3. Recovery Risk. Credit risk includes non-performing loans.

Non-performing loan (NPL) is a non-performing loan where the debtor is unable to meet the payment of loan arrears and interest within the timeframe as agreed in the agreement.

This is also explained in Financial Accounting Standard No. 31 (revised 2000) which states that: "Non-performing loans are generally loans for which the payment of principal/or interest installments has passed ninety days or more after maturity or loans whose timely payment is highly doubtful."

According to Iswi Hariyani (2010) the ratio of NPL or non-performing loans is a ratio that shows the ability of bank management to manage non-performing loans provided by banks. The higher the NPL, the worse the quality of bank credit which causes the number of non-performing loans to increase.

According to Mudrajad et al., (2011) based on the codification of Bank Indonesia Regulations: NPL is a ratio that measures the ratio of the number of non-performing loans to total loans, namely:

1. Credit is credit given to third parties (not including credit to other banks)

2. Non-performing loans are loans with substandard quality (KL), doubtful (D), and bad (M).

3. Non-performing loans are calculated on a gross basis.

4. Figures are calculated per position (not annualized)

Non-performing loans describe a situation where the approval of credit returns is at risk of failure, and even tends to lead to or experience potential losses. Please note that it is wrong to assume that non-performing loans are always due to customer error. Nonperforming loans can be caused by various things originating from customers, from internal conditions and credit providers in table 6.

Table 6 Non-performing Loan
Rating	Criteria	Description
I	NPL < 2 %	Very Good
2	2%< NPL <5%	Good
3	5%< NPL < 8%	Faorly Good
4	8%< NPL < l 2%	Poor
5	NPL > 12%	Not Good

Included in non-performing loans are substandard loans, doubtful loans and bad loans. According to Bank Indonesia Circular No. 13/30/DPNP on December 16, 2011 , Non- Performing Loans can be calculated by the formula:

NPL assessment criteria according to Bank Indonesia regulation no. 15/2/PBI/2013 as follows:

The increase in NPL in large numbers can cause problems for the health of banks, therefore banks are required to always maintain credit that is not in a high NPL position in table 7.

Table 7 Assessment of Non-Performing Loan Ratio
Predicate	NPL Ratio (%)	Credit Value (%)
Healthy	00,00 -NPL < 10,35	81- 100
Fairly Healthy	10,35 < NPL < 12,60	66 < 81
Un Healthy	12,60 < NPL < 14.85	51 < 66
Not Healthy	NPL >14,85	00 < 51

In order to determine the fair or healthy level, an appropriate standard measure for NPL is determined. In this case, Bank Indonesia stipulates that a reasonable level of NPL is 5% of the total loan portfolio.

Net Interest Margin (NIM)

Net Interest Margin is a ratio used to measure the ability of bank management in managing its productive assets to earn net interest. According to Riyadi & Slamet (2006) Net Interest Margin is the ratio between Interest Income (bank interest income) minus interest expense (bank interest costs that become expenses) divided by Average Interest Earnings assets (average earning assets used). The greater the NIM, the higher the interest income obtained, according to the standards set by Bank Indonesia, the NIM ratio is > 6%.

Net Interest margin (NIM) is the ratio of Net Interest Income to interest assets is also known as “net yield on interest earnings assets”

Or

Federal Financial Institutions Examination Council (FFIEC) release average NIM for all U.S. bank average 3,8% James (2018).

Based on the circular letter of Bank Indonesia No. 6/23/DPNP dated May 31, 2004 NIM is calculated by the formula:

NIM Credit Value = NIM Value

Positive NIM means the Investment strategy pays more interest than it cost. If NIM negative means the investment strategy cost more than it makes in table 8.

Table 8 Criteria for Determining the Net Interest Margin (NIM) Level
Rating	Ratio	Definition	Description
1	NIM>3%	The net interest margin is very high, meaning the NIM level is Very healthy	Very Healthy
2	2%< NIM =3%	High net interest margin, meaning a healthy NIM level	Healthy
3	1,5% < NIM = 2%	The- net interest margin is quite high, meaning that the NIM level is quite healthy	Healthy Enough
4	1% < NIM = 1,5%	Low net interest margin leads to negative, meanin2 that the NIM level is unwell	Unwell
T5	NIM = 1%	Net interest margin is very low or negative, meanin2 that the NIM level is not healthy	Not Healthy

Bank Financial Performance

The bank's financial performance is measured by profitability/profitability, namely the bank's ability to generate profits to support expansion and cover risks as well as the level of efficiency or measure the level of efficiency and effectiveness of management in carrying out bank operations in table 8, table 9, table 10 & figure 1. In accordance with Bank Indonesia Regulation Number 91/PBI/2007 the components of profitability are:

Table 9 Criteria for Deter Mining Return on Assets (ROA)
Rating	Criteria	Description
1	ROA =  O%	Inadequate profitability
2	0% < ROA = 0,5%	Low profitability is sufficient
3	0,5% < ROA = 1,25%	Profitability is quite adequate
4	1,25% < ROA = 1,5%	Adequate profitability
5	ROA > 1,5%	Profitability is very adequate

Table 10 Nilai Kredit DA Predikat ROA
ROA (%)	Credit Value BI Standard	Predicate
= 1,2 15	81 - 100	Healthy
= 0,99 - < 1,215	66 = 81	Healthy Enough
= O, 765 -< 0.999	51 = 66	Un well
< 0,765	0 = 51	Not Healthy

1. The ability to generate profits, the ability to support expansion and cover risks, as well as the level of efficiency.

2. Diversification of income includes the ability of banks to earn fee-based income, and diversification of investment funds, as well as the application of accounting principles in revenue and expense recognition.

In this study used Return on Assets with the formula:

Framework and Hypothesis Design

Hypothesis Design:

H1: Non-Performing Loans (NPL) have a significant negative effect on the performance of Credit Card Issuing Banks.

H2: Net Interest Margin (NIM) has a significant positive effect on the performance of Credit Card Issuing Banks

H3: Non-Performing Loans (NPL) and Net Interest Margin (NIM) have a significant effect on the performance of Credit Card Issuing Banks

Partially:

1) Y = a + β1X1 +εo 2) Y = a + β2X2 +εo

Y = Performance of Credit Card Issuing Banks

a = Constant

β1 = Non-Performing Loan determination coeficiente

X1 = Non-Performing Loan

β2 = Net Interest Margin determination coeficiente

X2 = Net Interest Margin

Simultaneously:

Y = a + β1X1 + β2X2 + εo

Y = Performance of Credit Card Issuing Banks

a = Constant

β1 = Non-Performing Loan determination coeficiente

X1 = Non-Performing Loan

β2 = Net Interest Margin determination coeficiente

X2 = Net Interest Margin

εo = Other factors not studied

Effect of Non-Performing Loan (NPL) on Profitability (ROA)

Credit is the main source of income for banks, good bank performance is indicated by the smooth distribution of bank credit to the public. However, the high credit disbursement carried out by banks will also provide a high risk for banks, namely the occurrence of nonperforming loans and high NPLs.

If the debtor is unable to repay the credit loan, it will pose a risk of non-performing loans or non-performing loans. The high NPL ratio owned by the bank will affect the value of the bank's assets and the bank's ability to generate profits, it will have an impact on the profitability of the bank itself. Lukman Dendawijaya (2005) suggests that the consequences of the emergence of non-performing loans can be in the form of:

1) With the existence of non-performing loans, banks will lose the opportunity to earn income from the loans they provide, thereby reducing profits and adversely affecting bank profitability or profitability.

2) Return on Assets (ROA) decreased.

Based on the description above, it can be seen that non-performing loans affect bank profitability as measured by the rate of return on assets (ROA). So that if there is a nonperforming loan (Non-Performing Loan) where the debtor cannot repay the loan, this can disrupt the composition of the company's assets which causes disruption of the smooth running of the bank's business activities. Then the design hypothesis is

H1: Non-Performing Loans have a significant negative effect on Banking Performance

This agrees with the research conducted by:

(1) Besimir & Muhamet Aliu (2021) research results show that Non-Performing Loans have a significant negative effect on bank profitability, Likewise research conducted by (2) Adamu Yahaya et al., (2021) in Nigeria gives the results that Non-Performing Loans and Net Interest margin significant effect on bank performance in sub-Saharan African Economies, (3) Stefano & Dewi (2022), (4) Barus (2016) analysis Non-Performing Loan on Public Bank in Indonesia , (5) Ferreira (2022), (6) Chowdhury(2020), Non-Performing Loan in Bangladesh and Adisaputra (2012) , Analysis Non-Performing Loan factors in Bank Mandiri.

The Effect of Nim on Bank Performance

NIM is used to measure management's ability to manage its productive assets so that it has a positive relationship with bank performance, the hypothesis design is:

H2: NIM has a significant positive effect on bank performance

This is in line with research conducted by: (1) Barus (2016) in Analysis Non- Performing Loan on Public Bank in Indonesia, (2) Adisaputra (2012), (3) Saksonova (2014), Alnabulsi et al., (2013), (4) Stefano & Dewi (2022) and (5) Listyorini (2012) Analysis the influence of CAMEL to Public Bank.

The Influence of NPL and NIM on Bank Performance

Non-Performing Loans and Net Interest Margin simultaneously affect bank performance, so the hypotheses are designed as follows:

H3: NPL and NIM have a positive and significant effect on bank performance

Research Results

The credit card issuing banks that have obtained permission from Bank Indonesia are as follows in table 11:

Table 11 List of Bank Issuing Credit Card Issuers
No.	Name of Bank Issuing Credit Card	No.	Nameof Bank Issuing Credit Card
1	BANK BUKOPIN	14	BANK PERMATA
2	BANK CENTRAL ASIA	15	BANK QNB INDONESIA
3	BANK CIMB NIAGA	16	BANK RAKYAT INDONESIA
4	BANKDANAMON   INDONESIA	17	BANK SINARMAS
5	BANK DBS INDONESIA	18	BANK UOB INDONESIA
6	BANK HSBC  INDONESIA	19	BANK SYARIAH INDONESIA
7	BANK MNC INTERNASIONAL	20	CITIBANK
8	BANK ICBC INDONESIA	21	PAN INDONESIA BANK
9	BANKMANDIRI(Persero)	22	STANDARD CHARTERED
10	BANK MAYBANK INDONESIA	23	BANK MAYAPADA
11	BANK MEGA	24	PT. AEON CREDIT SERVICES
12	BANK NEGARA INDONESIA	25	PT. SHINHAN INDO FINAN CE
13	BANK OCBC NISP

Of the 25-bank issuing credit card, 1 non-bank company, namely PT Aeon Credit Service and 1 bank, namely Bank Syariah Indonesia, were excluded in this study because they used a different basis. So, the number of credit card issuers is 23 credit card issuing banks with the year of observation from 2016 to 2021.

Statistics of descriptive Non-Performing Loan Bank issuing Credit card as follows:

Based on table 12 and Figure 2, it can be seen that the NPL of credit card issuing banks still meets the standards applied by Bank Indonesia, which is still below 5%, an average of 1.6 and the highest of 6.37 occurred at KB Bukopin bank in 2017 and the lowest of 0.01 occurred at Bank CIMB Niaga in 2019 using the credit scoring calculation formula, all obtained a score of 1, meaning that they were still in the healthy predicate category.

Table 12 Non-Performing Loans of Bank Issuing Credit Card in 2016 – 2021
No	Bank Issuing Credit Card	Non – Performing Loan ( NPL )						AVG		Credit Score	Descripti on	Predicate
		2016	201 7	201 8	2019	2020	2021
1	BRI	1,09	0,88	0,9 2	1,04	0,80	0,70		0,91	97,30	Very Good	Healthy
2	MANDIRI	1,38	1,06	0,6 7	0,84	0,43	0,41		0,80	98,01	Very Good	Healthy
3	BCA	0,31	0,45	0,4 5	0,47	0,74	0,78		0,53	99,78	Very Good	Healthy
4	BNI 46	0,44	0,70	0,8 5	1,25	0,95	0,73		0,82	97,87	Very Good	Healthy
5	MAYBANK	2,37	1,83	1,5 7	2,11 ER	2,52	2,82		2,20	88,64	Good	Healthy
6	PANIN	0,47	0,52	0,7 4	0,97	0,50	0,90		0,68	98,78	Very Good	Healthy
7	CIMB NIAGA	2,19	2,17	0,0 2	0,01	1,42	1,17		1,16	95,58	Very Good	Healthy
8	DANAMON	1,96	1,88	2,0 5	2,15	0,91	0,37		1,55	92,98	Very Good	Healthy
9	PERMATA	2,24	1,67	1,7 3	1,34	1,04	0,69		1,45	93,66	Very Good	Healthy
10	MEGA	2,59	1,41	1,2 7	2,25	1,07	0,81		1,57	92,89	Very Good	Healthy
11	CITIBANK	0,94	0,54	0,5 2	0,45	0,42	0,46		0,56	99,63	Very Good	Healthy
12	UOB IND	2,61	0,93	0,8 9	1,19	1,51	2,22		1,56	92,94	Very Good	Healthy
13	OCBC NISP	0,77	0,72	0,8 2	0,78	0,79	0,91		0,80	98,01	Very Good	Healthy
14	HSBC IND	3,90	1,70	1,2 2	1,22	0,94	0,79		1,63	92,48	Very Good	Healthy
15	STANDCH ART	1,44	1,11	0,4 9	0,98	0,43	0,48		0,82	97,86	Very Good	Healthy
16	ICBC IND	2,09	2,06	2,4 7	1,77	2,88	2,53		2,30	$ 8,00	Good	Healthy
17	KB BUKOPIN	2,79	6,37	4,7 5	4,45	4,95	4,91		4,70	71,98	Good	Healthy
18	MNC	2,38	2,82	3,4 3	3,57	3,63	2,81		3,11	82,62	Good	Healthy
19	SINAR MAS	1,47	2,34	2,7 3	4,33	1,39	1,18		2,24	88,40	Good	Healthy
20	QNB IND	2,94	1,14	1,4 7	4,45	1,21	0,04		1,88	90,83	Very Good	Healthy
21	MAYAPAD A	1,22	4,20	3,2 6	1,63	1,60	2,17		2,35	87,69	Good	Healthy
22	DBS INDONESI A	1,62	0,98	0,9 4	1,04	1,32	0,77		1,11	95,92	Very Good	Healthy
23	SHINHAN INDO	0,92	0,74	0,4 3	1,98	4,24	2,95		1,88	90,82	Very Good	Healthy

Statistical Descriptive Net Interest Margin of the bank issuing credit card as follows:

Based on table 13 and Figure 3, it is found that the average Net Interest Margin of credit card issuing banks is 4.54%, which is still above the Bank Indonesia regulation, which is 3%. The highest NIM was achieved by Bank Rakyat Indonesia at 8.27% in 2016 and the lowest was experienced by Bank CIMB Niaga at 0.05 in 2018 and 2019. Based on the credit score, it was obtained that there were 2 banks in rating 2, namely the NIM ratio in the range of 2 % < NIM < 3% with healthy category, namely bank ICBC Indonesia and KB Bukopin. There is 1 bank that is in rating 3, namely NIM in the range of 1.5% < NIM < 2% with a fairly healthy category, namely QNB Indonesia bank, while 20 other banks have a rating of 1 which is very healthy.

Table 13 Net Interest Margin of Bank Issuing Credit Card Issuing for 2016 – 2021
NO	Bank Issuing Credit Card	Net Interest Margin (NIM )						AVG	Credit Score	Description	Predicate
		2016	2017	2018	2019	2020	2021
1	BRI	8,27	7,93	7,45	6,98	6,00	6,89	7,25	7,25	Very Healthy	1
2	MANDIRI	6,29	5,63	5,52	5,46	4,48	4,73	5,35	5,35	Very Healthy	1
3	BCA	6,81	6,19	6,13	6,24	5,70	5,10	6,03	6,03	Very Healthy	1
4	BNI 46	6,17	5,50	5,29	4,92	4,50	4,67	5,18	5,18	Very Healthy	1
5	MAYBANK	4,59	4,49	4,35	4,14	3,79	3,95	4,22	4,22	Very Healthy	1
6	PANIN	4,94	4,49	4,61	4,63	4,46	4,88	4,67	4,67	Very Healthy	1
7	CIMB NIAGA	5,47	5,45	0,05	0,05	4,75	4,71	3,41	3,41	Very Healthy	1
8	DANAMON	7,36	7,03	6,22	5,31	5,02	5,19	6,02	6,02	Very Healthy	1
9	PERMATA	3,93	3,99	4,11	4,39	4,56	4,02	4,17	4,17	Very Healthy	1
10	MEGA	7,01	5,80	5,19	4,90	4,42	4,75	5,35	5,35	Very Healthy	1
11	CITI BANK	6,24	6.36	5,79	5,91	4,80	4,03	5,52	5,52	Very Healthy	1
12	UOB IND	4,30	3,85	3,79	3,65	3,82	3,81	3,87	3,87	Very Healthy	1
13	OCBC NISP	4,62	4,47	4,15	3,95	3,96	3,82	4,16	4,16	Very Healthy	1
14	HSBC IND	5,43	5,25	4,64	4,39	4,03	3,36	4,52	4,52	Very Healthy	1
15	STANDCHART	4,87	4,26	4,38	4,32	2,95	1,94	3,79	3,79	Very Healthy	1
16	ICBC IND	3,24	2,99	2,35	2,00	1,96	2,36	2,48	2,48	Healthy	2
17	KB BUKOPIN	3,88	2,89	2,83	2,08	0,61	1,25	2,26	2,26	Healthy	2
18	MNC	3,28	3,04	4,10	4,17	4,01	3,80	3,73	3,73	Very Healthy	1
19	SINAR MAS	6,44	6,46	7,61	7,31	6,25	5,79	6,64	6,64	Very Healthy	1
20	QNB IND	2,25	1,22	1,73	2,56	1,61	2,34	1,95	1,95	Healthy Enough	3
21	MAYAPADA	5,16	4,26	4,09	3,61	0,47	0,69	3,05	3,05	Very Healthy	1
22	DBS IND	4,96	5,21	5,35	5,25	5,53	5,13	5,24	5,24	Very Healthy	1
23	SHINHAN INDO	5,05	5,31	4,47	3,32	2,42	2,32	3,82	3,82	Very Healthy	1

Descriptive Statistics Return on Assets of Bank Issuing Credit Card 2016 – 2021

Based on table 14 and Figure 4, it is obtained that the average Return on Assets within a period of 6 years is 1.22%, maximum 4.67% and minimum -8.56%. Based on the standard credit score of Bank Indonesia, 10 banks were declared very adequate, 2 banks were declared adequate, 6 banks were declared enough adequate, 4 banks were declared inadequate and 1 bank was less adequate.

Table 14 Return on Assets of Bank Issuing Credit Card 2016 – 2021
N o	Bank Issuing Credit card	Return on Assets (ROA )						AV G	Credit Score	Description	Predicate
		201 6	201 7	201 8	201 9	202 0	202 1
1	ICBC IND	3,84	3,6 9	3,6 8	3,50	1,98	2,7 2	3,24	215,67	Very Adequate	Healthy
2	MANDIRI	1,95	2,7 2	3,1 7	3,03	1,64	2,5 3	2,51	167,11	Very Adequate.	Healthy
3	BCA	3.93	3,8 9	4,0 1	4,02	3,32	3,4 1	3,76	250,89	Very Adequate	Healthy
4	BNI 46	2,69	2,7 5	2,7 8	2,42	0,54	1,4 3	2,10	140,11	Very Adequate	Healthy
5	MAYBANK	1,48	1,2 3	1,4 8	1,09	0,82	1,0 0	1,18	78,89	Adequate	Enough healthy
6	PANIN	1,68	1,8 7	2,2 5	2,09	2,08	1,7 3	1,95	130,00	Very Adequate	Healthy
7	CIMB NIAGA	1,19	1,6 7	0,0 2	0,02	0,99	1,7 5	0,94	62,67	Enough Adequate	Enough healthy
8	DANAMON	2,26	3,0 0	2,9 9	2,95	0,87	1,0 2	2,18	145,44	Very Adequate	Healthy
9	PERMATA	4,89	0,6 1	0,7 8	1,30	0,97	0,7 3	0,08	5,56	Inadequate	Not healthy
1 0	MEGA	2,36	2,2 4	2,4 7	2,90	3,64	4,2 2	2,97	198,11	Very Adequate	Healthy
1 1	CITI BANK	4,14	4,3 4	3,2 2	4,67	3,91	1,5 6	3,64	242,67	Very Adequate	Healthy
1 2	UOB IND	0,77	0,3 2	0,7 1	0,87	0,70	0,7 1	0,68	45,33	Enough Adequate	Unwell
1 3	OCBC NISP	1,85	1,9 6	2,1 0	2,22	1,47	1,5 5	1,86	123,89	Very Adequate	Healthy
1 4	HSBC IND	0,47	1,7 8	1,1 3	2,72	1,56	1,5 3	1,53	102,11	Very Adequate	Healthy
1 5	STANDCHART	0,58	0,3 2	1,2 6	0,70	1,82	0,6 3	0,89	59,00	Enough Adequate	Unwell
1 6	ICBC IND	1,61	0,8 3	0,2 8	0,22	0,05	0,9 6	0,66	43,89	Enough Adequate	Not healthy
1 7	KB BUKOPIN	1,38	0,0 9	0,2 2	0,13	- 4,61	- 4,9 3	- 1,29	85,78	Inadequate	Not healthy
1 8	MNC	0,11	- 7,4 7	0,7 4	0,27	0,15	0,1 8	- 1,00	I 66,89	Inadequate	Not healthy
1 9	SINAR MAS	1,72	1,2 6	0,2 5	0,23	0,30	0,3 4	0,68	45,56	Enough Adequate	Not healthy
2 0	QNB IND	- 3,37	3,7 2	0,1 2	0,02	1,24	8,5 0	2,78	185,44	Inadequate	Not healthy
2 1	MAYAPADA	2,03	1,3 0	0,7 3	0,78	0,12	0,0 7	0,84	55,89	Enough Adequate	Unwell
2 2	DBS IND	1,30	1,0 2	0,0 5	0,28 -	0,20	0,9 5	0,47	31,56	Less adequate	Not healthy
2 3	SHINHAN INDO	0,75	2,1 9	1,9 8	0,43	0,86	0,7 6	1,16	77,44	Adequate	Enough healthy

The conclusions of the descriptive statistics are as follows in table 15:

Table 15 Descriptive Statistics
	N	Minimum	Maximum	Mean	Deviation Std.
X1 NPL	144	0.01	6.37	1.5972	1.17533
X2 NIM	144	0.05	8.27	4.5443	1.63293
Y ROA	144	-8.5	4.67	1.2172	1.94557
Valid N ( list wise )	144

Classical Assumption Test

Normality Test Results

The results of the Kolmogorov-Smirnov One-Sample test are presented in the following table:

Based on table 16 shows that the data is not normally distributed, then the data is transformed by means of rank cases. After rank cases were carried out, the data became normal with a residual probability value of 0.001 smaller than 0.05 which indicates that the data is not normally distributed to be normal, which is 0.200 greater than 0.05 which can be seen in the following figure:

Table 16 One-Sample Kolmogorov-Smirnov Test
		Unstandardized Residual
N		144
Normal Parameters  a,b	Mean	.0000000
	Std . Deviation	1.51795833
Most Extreme Differences	Absolute	0.134
	Positive	0.09
	Negative	-0.134
Test Statistic		0.134
Asymp , Sig . ( 2 - tailed )		.000c

The test results show that the data is normally distributed, indicated by the residual probability value of 0.200 which is greater than 0.05. This is also supported by the results of Figure 5 and Figure 6 histogram and p-plot (Table 17).

Table 17 One-Sample Kolmogorov-Smirnov Test
		Unstandardized Residual
N		40
Normal Parameters a,b	Mean	.0000000
	Std Deviation	.86410360
Most Extreme Differences	Absolute	.093
	Positive	.093
	Negative	-.058
Test Statistic		.093
Asymp Sig. (2-tailed)		200c,d

Multicollinearity Test Results

Table 18 shows that there is no multicollinearity, this is indicated by the Non- Performing Laon tolerance variable of 0.962 and the VIF value of 1.040, the Net Interest Margin variable with a tolerance value of 0.962 and a VIF value of 1.040. The tolerance and VIF values of all independent variables show that there is no multicollinearity, because the VIF value < 10 and the tolerance value > 0.01, there is no correlation between the independent variables.

Table 18 Multicollinearity Test Results
Coefficients
Model		B	Unstandardized Coefficients Std Error	Standardize d Coefficients Beta	t	Sig.	Collinea rity Toleran ce	Statistic s VIF
	(Constan	.000	.068		.002	999
	t)
	Normal
	Score
	of X1 NPL using	-410	.070	-,410	- 5.843	.000	.962	1.040
1	Blom's
	Formula
	Normal
	Score of
	X2 NIM using	419	.070	.419	5.973	.000	.962	1.040
	Blom's
	Formula

Heteroscedasticity Test Results

The heteroscedasticity test in this study uses a scatterplot, the results show the points of a certain pattern such as widening, wavy, or narrowing, meaning that heteroscedasticity occurs. The results of the scatterplot graph are as follows:

Figure 7 shows that the scatterplot graph shows that the points spread between the top and bottom of the zero point. No particular pattern is formed. So, it can be concluded that there is no heteroscedasticity in this study.

Autocorrelation Test Results

The test results of the Durbin-Watson Test method are as follows:

Table 19 results from the Durbin Watson score of 1.822 which lies between (DU) and (4-DU) with the criteria obtained are dU < d < 4-dU, namely 1.6589 < 1.822 < 2.3411. It can be concluded that there is no autocorrelation.

Table 19 UJI Durbin-Watson Test Result
Model Summaryb
Model	R R Square	Adjusted Square	Std. Error of the Estimate	Durbin-Watson
1	.642a         .412	.402	.766053	1.822

Multiple Linear Analysis Results

Multiple linear analysis test results:

Base on table 20 multiple linear equations are as follows:

Table 20 Multiple Linear Analysis
		Unstandardized Coefficients		Standardized Coefficients
Models		B	Std. Error	Beta	T	Sig.
1	(Constant)	.000	.068		.002	.999
	Normal Score of X1_NPL using Blom's Formula	-410	.070	-410	- 5.843	.000
	Normal Score of X2_NIM using Blom's Formula	.419	.070	419	5.973	.000

ROA = 0,000 – 0,410 NPL + 0.419 NIM

Coefficient of Determination (R²)

The coefficient of determination analysis was conducted to determine and measure how much influence the independent variable had on the dependent variable simultaneously.

Based on the calculation of table 21, the coefficient of determination shows 41.20% meaning : Non-performing loans and Net Interest Margin affect profitability. The remaining 59.80% is influenced by other variables.

Table 21 Pearson Product Moments
Model Summaryb
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate	Durbin-Watson
1	.642a	.412	.402	.7660533	1.822

Conclusion

Based on the framework, it can be concluded as follows:

1. Non-performing loans of credit card issuing banks from 23 banks studied, there are 6 banks (26.08%) in good category, the rest 17 banks (73.92%) in very good category, and still above the Bank Indonesia regulation, which is equal to 5%.

2. Net Interest Margin from 23 credit card issuing banks, 2 banks (8.69%) in good category and the remaining 21 banks (91.31%) in very good category, and still above the Bank Indonesia regulation, which is at least 3%.

3. Return on Assets from 23 credit card issuing banks here are very varied with the following details: 10 banks (43.48%) in the healthy category, 3 banks (13.04%) in the fairly healthy category, 3 banks (13.04 %) in the unhealthy category and 7 banks (30.44%) in the unhealthy category.

4. The results of hypothesis testing show that Non-Performing Loans on Return on Assets have a significant negative effect, while Net Interest Margin has a positive effect on Return on Assets and simultaneously Non-Performing Loans and Net Interest Margins affect Return on Assets by 41.20%, while 59.80% was influenced by other factors not examined.

Suggestions

The management of credit card issuing banks must improve their performance, especially return on Assets so that banks can increase their income in the form of Net Interest Margin and suppress Non-Performing Loans. The key is to control lending so that banks do not experience difficulties in increasing their income.

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