An Early Warning Model of Financial Distress

Sharia Banks in Indonesia

Fadhil Yamaly, Sulastri, Syamsurijal AK, Isnurhadi

Business & Economics Faculty, University of Sriwijaya, Palembang, Indonesia

Keywords: Early Warning, Financial Distress, Discriminant Analysis, Islamic or Sharia banks

Abstract: Predictive models of financial distress presented by highly qualified researchers have identified

conclusively as an early warning of financial distress. This study aims to develop a model of an early

warning before the onset of financial distress in the group of Islamic or Sharia banks in Indonesia. Approach

to the development of models using multiple discriminant analysis (MDA) on panel data of 11 companies

studied Islamic banks in the period 2013-2017. The results showed that the financial variable flow cycle

(FFC), Cash Conversion Cycle (CCC) and Debt to Equity could be a differentiator. The discriminant model

can predict cases accurately at 81,8%. The study's findings provide empirical support to the stakeholder in

the identification of the financial distress in Sharia banks.

1 INTRODUCTION

Since the collapse of Arthur Andersen, Enron,

WorldCom, Lehman Brothers, American Airlines

and Kodak, the global economy is becoming

increasingly sensitive to signs of financial distress,

bankruptcy and bankruptcy of the company,

(Cunningham and Harris, 2006; Adnan and

Humayon 2006; Stinson, 2010; Muller, Steyn and

Hamman, 2012). So is various problems arise in the

process of growth and sustainability of financial

institutions in various countries. For example, the

collapse of financial institutions such as Ihlas

Finance in Turkey and Bank Taqwa in the Bahamas,

as well as aid the prevention of bankruptcy (bail-

in/bail-out) on banks as happened in Fortis Bank

Belgium, Commerzbank Germany, Dubai Islamic

Bank and Al-Rajhi Bank of Saudi Arabia, could

threaten the economic system as a whole (Iqbal,

2001; Ali, 2007; Hasan, 2010; Haron, 2012; Husna

and Rahman, 2012). Similarly, in Indonesia, in 2015,

showed that Bank Muamalat Indonesia is also

experiencing financial distress. On the other hand,

Sharia banks in Indonesia has proliferated with an

average growth of 5 percent per year (FSA, 2017). It

is essential fatherly reviewing financial distress in

Sharia banking in Indonesia.

Definition, classification and predictive

modeling of financial distress and bankruptcy of the

company is an exciting research topic (Ward, 1997;

Trussell, 2002; Kpodoh 2009; Senbet and Wang,

2012). Financial distress is the inability of the

company to pay its obligations, the case before the

company becomes insolvent or fails (Fitzpatrick,

1932; Beaver, 1966; Altman, 1968; Altman et al.

1977; Ohlson, 1980). While others, financial distress

is defined as the process of financial loss phase

(Hofer, 1980; Whitaker, 1999; Platt and Platt, 2002

in Atmini 2005). However, some researchers suggest

that financial difficulties are when a company fails

to pay its obligations ((Latinen, 1994; Ward and

Foster, 1997; Whitaker, 1999; Cybinski, 2001;

Kuruppu, 2003; Steyn-Bruwer and Hamman, 2006;

Arena, 2008). It becomes a real difference, to make a

distinction between companies that fail classification

(fraud) or non-fail. Differences due to the

determination of variable definition and analysis.

Some researchers suggest studying financial distress,

it is not a failure, because of the narrow definition of

failure (Keasey and Watson, 1991; Kahya and

Theodossiou, 1996; McLeay and Omar, 2000; Platt

and Platt, 2002). On the other hand, the importance

of developing specific technical models and different

analysis to financial distress. (Keasey and Watson,

1991; Hayden, 2003; Taffler and Agarwal, 2007,

Fitzpatrick and Ogden, 2011, Zhiyong Li et al.

2017). Because of the narrow definition of failure

(Keasey and Watson, 1991; Kahya and Theodossiou,

1996; McLeay and Omar, 2000; Platt and Platt,

Yamaly, F., Sulastri, ., Kadir, S. and Isnurhadi, .

An Early Warning Model of Financial Distress Sharia Banks in Indonesia.

DOI: 10.5220/0008444507230732

In Proceedings of the 4th Sriwijaya Economics, Accounting, and Business Conference (SEABC 2018), pages 723-732

ISBN: 978-989-758-387-2

723

2002). On the other hand, the importance of

developing specific technical models and different

analysis to financial distress. (Keasey and Watson,

1991; Hayden, 2003; Taffler and Agarwal, 2007,

Fitzpatrick and Ogden, 2011, Zhiyong Li et al.

2017). Because of the narrow definition of failure

(Keasey and Watson, 1991; Kahya and Theodossiou,

1996; McLeay and Omar, 2000; Platt and Platt,

2002). On the other hand, the importance of

developing specific technical models and different

analysis to financial distress. (Keasey and Watson,

1991; Hayden, 2003; Taffler and Agarwal, 2007,

Fitzpatrick and Ogden, 2011, Zhiyong Li et al.

2017).

This study will develop models and techniques

of financial analysis with consideration to determine

a good prediction related to the financial condition

and performance prediction. The financial analysis

involves static financial ratios have been widely

used in predicting the company's financial distress

and bankruptcy (Damodaran, 2001; Alkhatib and

Bzour 2011; Burksitiene and Mazintiene, 2011).

Over time, the static model of financial analysis is

less able to predict the financial distress in the

changing economic environment and changes in its

operations effectively and continuously (Sun and Li,

2011). On the one hand, some researchers argue

dynamic working capital can participate in providing

a simple methodology with the purpose of

evaluating financial performance (Flueriet 1980;

Kehdy and Blanc 2003; Costa and Gracias, 2009;

Fleuriet and Zeidan 2015). Along with this, working

capital can reflect the financial health of the

company related to profitability and liquidity

(Sagner 2014 Talonpoika, 2016). The use of total

debt in the funding of companies (financial leverage)

risk within the period, the volatility of the fund and

asset value growth, with leverage dynamic

measurement also able to generalize the risk of

failure of the company (Smirnov, 2004;

Xiadongzhang and Jonghe 2015; Marzo and Zhiguo,

2016).

The study was oriented management of Islamic

finance, especially in Islamic banking in Indonesia.

Islamic banking or Sharia banking (Al-Mashrafiyah

al-Islamiya) has a banking system operating with

Islamic principles. Assumed to have partial

resistance and health adequate (Saeed, 1996;

Rammal, 2007; Jaizah and Mehmet 2017). Even so,

some Sharia banks in Indonesia have indications of

financial distress. Below in Table 1 are presented the

phenomenon of Sharia banking, which processed

with priority dimensions of income (EBIT) of debt

relationships with the company's assets.

Table 1: Dimensions phenomenon Earning Before Income Tax (EBIT), Debt to Asset Ratio (DAR) of Sharia Banking in

Indonesia in 2012-2016

No.

EBIT TO DAR

2012

2013

2014

2015

2016

Bank Aceh Syariah

BASS

117,363

Bank Muamalat Indonesia

BMI

552,095

709,266

105,693

116,120

124,537

Bank Victoria Syariah

BVS

12,409

5,589

(28,717)

(36,261)

(31,671)

Bank BRI Syariah

BRIS

149,382

203,834

16,795

187,140

262,397

Bank Jabar Banten Syariah

BJBS

24,859

46,833

39,695

17,796

(618,860)

Bank BNI Syariah

BNIS

155,034

197,099

244,603

340,549

409,127

Bank Syariah Mandiri

BSM

1,188,779

956,543

118,535

406,559

473,065

Bank Mega Syariah

BMS

286,501

218,154

26,255

19,848

182,630

Bank Panin Dubai Syariah

BPD

60,705

33,511

115,733

89,939

32,106

Bank Syariah Bukopin

BSB

28,687

31,488

13,909

45,621

53,973

Bank BCA Syariah

BCAS

13,532

19,802

22,123

42,073

63,130

Bank Maybank Syariah Indonesia

BMSI

104,244

104,095

133,951

(519,050)

(292,558)

Bank Tabungan Pensiunan Nasional

Syariah

BTPNS

173,210

298,969

71, 202

Source: compiled from various sources for research purposes

Table 1 shows during the five years from 2012 to

2016, Sharia banking in Indonesia is experiencing

the dynamics and dimensions fluctuations EBIT of

DAR. Also, in 2014, several Sharia banks

experienced a decline in profitability. Sharia banking

development is also experiencing difficulties in

profitability, working capital and debt (Hasan, 2010;

Hassairi 2011; Husna and Rahman, 2012; Pappas

and Izzeldin 2012; Jaizah, 2013).

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724

This study will be different from previous

research that has done. As a comparison, it can serve

on the differentiation of the prediction model of

financial distress in the banking industry are

presented in Table 2. From Table 2, the apparent

disparity research, financial distress prediction

studies on Sharia banking is still minimal. Besides,

dynamic models in the prediction of financial

distress have begun to be used since 2012. The

current study aims to develop a new model of early

warning of financial distress in Sharia banks in

Indonesia, taking into account the dynamic and

leverage the working capital of the company.

Table 2: Differentiation Prediction Model for Financial Distress and Bankruptcy in Banking Industry

empirical era

Model Related features

Object of

research

1970

Using MDA multivariate discriminant analysis (Altman 1968; Meyer and Pifer,

1970; Sinkey, 1975; Sinkey, 1977), logit regression (Martin, 1977)

conventional

banking

1980

Using Logit and Probit models, the ratio of CAMEL (Bovenzi, 1983; West,

1985; Zavgren, 1985; Whalen and Thomson, 1988; Looney et al. 1989), Factor

Analysis (West, 1985).

conventional

banking

1990

Using a logit model, Neural Network, CAMELS ratio, macroeconomic approach

and professional assessment (Espahbodi, 1991; Thomson, 1992; Tam and Kiang

1992; Henage, 1995; Tan, 1996; Hermosillo, 1996; Bell 1997; Swicegood,

1998), Methods AEIS and neural networks (neural network) (Tam and Kiang,

1990; Tam, 1991; Swicegood and Clark, 2000).

conventional

banking

2000

During the 2000s, began to develop an early warning system (system EWS

warning) Using logit analysis of parametric and non-parametric, Genetic Neural

Network, MDA, Probit, research areas in the United States (Kolari, 2002; Tung,

2004, Canbas, 2005; Lanine and Vennet, 2006), the territory of Japan, Indonesia,

Malaysia, Turkey, Norway, Britain, Austria and Russia (Kutznetsov 2003,

Logan, 2003; Golovan, 2003; Hayden and Baeur 2004; Montgomery, 2005;

Lanine and Vennet 2006 ; Konstandina 2006; Halling and Hayden, 2006;

Andersen 2008; Bakir and Tahir 2009; Boyacioglu, 2009). The dynamic model

of financial distress in Banking (Kahl, 2002; Cole and Qionghy, 2009).

Conventional

banking, (starting

inferred from the

dynamic model)

2010 -2017

In addition to conventional banking, Islamic or Sharia banking, began to also

develop a model of financial distress (financial distress) in various countries,

(Hasan, 2010; Hassairi 2011; Zaabi, 2011; Husna and Rahman, 2012; Pappas

and Izzeldin 2012; Jaizah, 2013, Baklaci and Baydoan 2014; Nawaz, 2017; Laila

and Widihadnanto, 2017).

Some dynamic model predictions of financial distress in conventional banking

(Achsani, Nuryatono and Haymens 2010; Zhiyong, Crook and Andreeva, 2016).

Conventional and

Sharia (Islamic)

banking

Source: compiled from various sources for research purposes

This research is expected to explain the gap

between researchers on the issue of working capital

as a variable dynamic early warning of financial

distress so it can be used for the achievement of the

sustainability performance of Sharia banking.

Subsequently, in the next section, conducted a

literature review with the support of theories have

been developed previously. Moreover, it will focus

on the development of early warning models of

financial distress. Next, it presented a description of

the findings, discussion, and conclusion.

2 LITERATURE

Decisions on finance companies can be grouped

into three critical decisions: (1) a decision on the

investment (the investment decision): related to the

choice between investment potential and right, (2)

the funding decision: relates to collecting / obtain

funds for investment that create value and choice

mix money owners (equity) or borrowed money

(debt) must use the company. (3) The decision of

dividend: related to the number of funds to be

reinvested in the funding of business activities and

how much should be returned to the owner

An Early Warning Model of Financial Distress Sharia Banks in Indonesia

725

(Damodaran, 2004; Copeland, Weston and Shastri,

2005).

Funding decisions relating to the selection of the

company's financial resources. As a result, along

with the debt increases, the likelihood of financial

distress or even bankruptcy will increase with the

risk of bankruptcy is higher, the debt holders will

demand payment of the promised higher, which also

will increase the cost of pre-tax debt (Brigham and

Daves, 2007). The increase in debt (leverage) will

increase the likelihood of financial distress and

bankruptcy (Jensen and Smith, 1984). The trade-off

theory assumes the company will have a capital

structure that is optimal based on the balance (trade-

off) between the benefits to the cost of the use of

debt (Peirson, 2006). In turn, financial distress will

lead to a reduction in the company (firm value).

The initial literature, a relationship between

financial distress and bankruptcy on the company's

capital structure proposed by Modigliani and Miller

(1958, 1963), known as the capital-MM Structure

Theory. It also assumes that bankruptcy does not

cause economic hardship. There is confusion about a

pair of related but different concepts: financial

distress and economic hardship. Financial distress

means that the company promises to creditors

damaged (Haugen and Senbet 1978, Gertner and

Scharfstein 1991). This is directly related to utilizing

the company's decision. Furthermore, in the Pecking

Order Theory (Myers, 1984) states that there is a

sequence (hierarchy) for companies in the use of

capital. Companies prefer to use internal funding

sources, then with debt, and the latter with equity

funding. Debt as a source of funding that comes

from outside the company while in the company.

This debt, in turn, must be repaid. Besides that, in

the structure of the financial statements, the Sharia

banking is known that has not bound investment.

Which is the separation of the elements of the debt

or capital (Husna and Rahman, 2012).

This study develops a study on the management

of working capital finance (Fleuriet et al. 1978), as

part of a dynamic model of working capital. This

model divides the current assets and current

liabilities into financial and operational cycles are

flexible between assets and liabilities. Finance

working capital management is one of the

possibilities to get the dynamic flexibility of the

financial performance and optimize profitability,

with a focus on asset flexibility, cash flow and debt

management (Marttonen et al. 2013; Talonpoika,

2016). In this case, the approach of the cycle time to

be the size of financial working capital and liquidity,

showing how the company can operate with liquid

assets after closing the current liabilities and is

known as the cycle of financial flows or FFC

(Talonpoika, 2016).

On the other hand, the increase in working

capital funding will affect the operational working

capital, so that the performance measures in the form

of cash conversion cycle (CCC) is used to measure

the operational working capital (Knauer and

Wohrmann, 2013; Talonpoika 2016). FFC

performance measures are only able to be enhanced

by financial items and then shifted to the operational

steps. Thus, the FFC measurement should be used in

conjunction with the CCC, to produce an accurate

decision. Additionally, some researchers estimate the

company's financial distress, uses dynamic

prediction models with various approaches

(Fitzpatrick and Ogden, 2011; Konstantaras and

Siriopoulos, 2011; Kim and Partington, 2014;

Zhiyong, Crook and Andreeva, 2017).

3 RESEARCH METHODS

In many studies, a large number of ratios have

been used in predicting financial distress. The ratio

is dominant, with the main criteria: working capital,

profitability, liquidity, solvency generally better in

predicting potential and financial distress (Bhunia

and Sarkar, 2011; Fitzpatrick and Ogden, 2011,

Zhiyong Li et al. 2017). In this study the selected

ratio is as follows:

Financial flows Cycle (FFC): shows the gap of

time and value, operational between current assets

and current liabilities of operations, related to

finance working capital cycle. FFC consists of two

components: other current assets (Other Current

Assets) and other current liabilities (Current

Liabilities) (Flueriet, 1980; Silva, 2010; Rehn 2012;

Camargos and Leao in 2014; Talonpoika, 2016).

Cash Conversion Cycle (CCC): is a variant of

the cycle time inventory and accounts receivable

cycle with the cycle of debt. It is intended to test the

limits of financial and operational working capital.

Operating working capital includes three

components: inventory accounts receivable and

payable (Flueriet, 1980; Camargos and Leao in

2014; Talonpoika, 2016).

Debt to Equity Ratio (DER): indicates the

relative proportion or the extent to which companies

take on debt as a means to increase its value. More

debt used by the company about total assets, the

higher the risk can not meet debt payments are

contractual (Souza and Smirnov, 2012; Sagner 2014

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Xiadongzhang and Jonghe 2015; Marzo and Zhiguo,

2016; Saračević and Šarlija, 2017).

Debt to Assets Ratio (DAR): the extent to which

the company's ability to meet the total liabilities, the

capital structure which compares the size of debt

with asset management. Can also be used to measure

how much of the company's assets are financed by

debt. The higher the ratio, the higher the degree of

leverage and cause financial risks (Souza and

Smirnov, 2012; Sagner 2014; Xiadongzhang and

Jonghe, 2015; Marzo and Zhiguo, 2016; Saračević

and Šarlija, 2017).

Debt to EBITDA: shows the company's ability to

meet the total liabilities, the capital structure which

compares the size of the debt to EBITDA, ignore the

factor of interest, taxes, depreciation, and

amortization. The ratio of Debt / EBITDA are high

indicates that the company may not be able to

service the debt properly and ensure that the credit

ratings downgraded(Souza and Smirnov, 2012;

Sagner 2014; Xiadongzhang and Jonghe, 2015;

Marzo and Zhiguo, 2016; Saračević and Šarlija,

2017).

Samples of data derived from the financial

statements for five (5) years from 2013 to 2017. The

unit of analysis in this research is the Indonesian

Sharia banks, which is a company engaged in the

financial industry sector, as a Sharia commercial

bank. Data Sharia banks experiencing financial

distress is taken from the measurement approach the

level of performance and classified into 2 (two)

groups: low performance and high-performance

group. Performance is measured by ratings and an

average score of four financial ratios: profitability,

productivity, efficiency, and leverage. (Osaimy,

2004; Jaizah, 2013). Analyzes were performed with

Multiple discriminant analysis (MDA). MDA is a

statistical method that allows for the study of more

than two variables simultaneously and used to

understand the structure of high dimensional data

(Bryman and Cramer, 2005). Use of Multiple

discriminant analysis (MDA) has been widely

applied in research management and accounting

(Altman 1968, 1993, 2005; Syahida and Ameer

2010, Bhunia, 2011). As a multivariate technique,

Multiple Discriminant Analysis (MDA) has a

prevailing assumption in the multiple regression

analysis and perform well if the variable in the group

follow a normal distribution (Bhunia and Sarkar,

2011). Data analysis technique in this research is to

analyze the variable ratio in the company's financial

statements and processing discriminant analysis. The

processing stage of discriminant analysis consists of:

estimating the coefficients of the discriminant

function with stepwise, test the similarity of the

average group, significant test between the two

groups, test the accuracy of the model, determine the

functional equation and validation determine the cut-

off point.

4 FINDINGS

After identifying the research methodology, this

section presents the empirical findings by the

particular method used.

Test Similarity Average Group: this test uses two

methods; namely, Wilks 'lambda and significant

value to the F test value of Wilks' lambda close to 0

indicate increasingly significant. Here in Table 3 are

presented the results of median equality test groups

and test for normality

Table 3: Tests of Equality of Group Means & Normality

Wilks' Lambda

DF1

DF2

Sig.

Kolmogorov-Smirnova

statistical

Sig.

FFC

, 730

19.592

, 000

, 099

, 200 *

DebttoAsset

, 934

3.716

, 059

, 072

, 200 *

trans_CCC

, 794

13.726

.001

, 087

, 200 *

trans_DebtoEqu

, 880

7.217

.010

, 099

, 200 *

trans_DebttoEbitda

, 971

1,607

, 210

, 114

, 070

Source: (Data from SPSS, 2018)

Based on Table 3, the variables that can

distinguish between healthy and unhealthy groups

namely FFC, trans_CCC and trans_ DebttoEqu

because it has significant value <0.05 and has a

value of 0. The test results wilks'lamda approaching

normality Kolmogorov-Smirnova showed that the

independent variable distribution normal.

An Early Warning Model of Financial Distress Sharia Banks in Indonesia

727

Significant Test Between Two Variables: this test uses stepwise method to find the best variables.

Table 4: Significant Test (Variable Entered / Removeda, b, c, d)

Step

Entered

Min. D Squared

Statistics

Between

Groups

Exact F

statistics

DF1

DF2

Sig.

FFC

1,539

0 and 1

19.592

53,000

4,815E-005

trans_CCC

2,170

0 and 1

13.547

52,000

1,839E-005

trans_DebtoEqu

2.746

0 and 1

11.211

51,000

9,216E-006

Source: (Data from SPSS, 2018)

In Table 4 independent variables that satisfy the

requirements to enter the discriminant equation is

FFC, trans_CCC, and trans_DebttoEqu that have

significant value <0.05.

Accuracy Test of Discriminant: This test measured

using wilks'lamda.

Table 5: Wilks' Lambda

Test of Function (s)

Wilks' Lambda

Chi-square

Sig.

, 603

26.084

, 000

Source: (Data from SPSS, 2018)

Wilks'lamda value of 0.603 and 0.000 significant

value, which means there are significant differences

between healthy and unhealthy groups on the

discriminant model.

Discriminant function analysis: discriminant

function so formed can be seen in Table of

Canonical Discriminant Function Coefficients.

Table 6: Canonical Discriminant Function Coefficients

Function

FFC

.001

trans_CCC

, 037

trans_DebtoEqu

-1.170

(Constant)

-2.191

Source: (Data from SPSS, 2018)

Discriminant function which is formed by a table

of 6, namely:

Z = -2.191 + 0.001FFC + 0.037 trans_CCC –

1.170 trans_DebttoEqu

Cut-Off Point: used to group company based on

the value obtained. The following table is used to

determine the cut-off point.

Table 7: Functions at Group centroids

Dependen0_1

(Y)

Function

Healthy

-1.055

Not healthy

, 603

Source: (Data from SPSS, 2018)

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According to the table above, Sharia banks are

healthy have an average score: -1.055. Sharia banks

do not have an average score of 0.603. Cut off score

= (-1.055 + 0.603) / 2 = -0.226. Thus, if the value of

Z score <-0.226 classified as Sharia banks are

healthy and if the value of Z score> -0.226 classified

as Sharia banks are not healthy.

Hit ratio: is one of the criteria for assessing the

strength of the sensitivity of the discriminant

equation in classifying objects. From Table 8 below,

the discriminant function can predict accurately the

case of 81.8%.

Table 8: Classification Results

a,c

Dependen0_1

Predicted Group

Membership

Total

Original

Count

Healthy

Not healthy

ungrouped cases

Healthy

100.0

, 0

100.0

Not healthy

28.6

71.4

100.0

ungrouped cases

, 0

100.0

Cross-validatedb

Count

Healthy

Not healthy

Healthy

95.0

5.0

100.0

Not healthy

31.4

68.6

100.0

a. 81.8% of original grouped cases Correctly classified.

b. Cross validation is done only for Reviews those cases in the analysis. In cross validation, each case is classified by

the functions derived from all cases other than that case.

c. 78.2% of cross-validated grouped cases Correctly classified.

5 CONCLUSIONS AND

IMPLICATIONS

Based on the results of the discriminant analysis,

financial ratios that proved significant for Sharia

banks differentiate healthy and unhealthy in

Indonesia in 2013-2017, namely the Financial Flow

Cycle (FFC), conversion Cash Cycle (CCC) and

Debt to Equity. Discriminant function equations

were formed, namely Z = -2.191 + 0.001FFC +

0.037trans_CCC–1.170 trans_ DebttoEqu and

cut-off point that is formed is -0.226. CCC

has the highest coefficient in the formation of the

discriminant.

The results of the discriminant analysis, Sharia

banks are considered as healthy, if the value of FFC

and CCC is getting smaller or negative, so it will

minimize the value of the discriminant function.

Likewise, with the DebttoEqu coefficient being

negative, if the DebttoEqu value is greater, it will

reduce the value of the discriminant function.

Furthermore, Sharia banks are considered as

unhealthy, if the value of FFC and CCC is getting

bigger, so it will enlarge the value of the

discriminant function. Likewise, with a negative

DebttoEqu coefficient, if the DebttoEqu value gets

smaller, it will increase the value of the discriminant

function

Finally, this model can be used by government

regulators to monitor the performance of Sharia

banks are likely to experience financial problems.

On the one hand, from the standpoint of the

regulator, the ability to detect early performance of

Sharia banking using publicly available data will

have a significant impact on the cost of monitoring

and inspection. Given that the authentic character of

Sharia banks, which provide durability and

uniqueness partially operational, it is essential to

further develop the financial distress early warning

mechanisms to proactively by considering new

variables associated with the uniqueness of Sharia

banks.

An Early Warning Model of Financial Distress Sharia Banks in Indonesia

729

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