ADBI Working Paper Series

CREDIT RISK REDUCTION EFFECTON SMALL AND MEDIUM-SIZED ENTERPRISE
FINANCE THROUGH THE USE OFBANK ACCOUNT INFORMATION

Naoko Nemoto, Naoyuki Yoshino,
Yutaka Okubo, Daimei Inaba,
and Kentaro Yanagisawa

No. 857
August 2018

Asian Development Bank InstituteNaoyuki Yoshino is Dean and CEO of the Asian Development Bank Institute. Naoko
Nemoto is a financial economist at the Asian Development Bank Institute. Yutaka Okubo
is CEO of the Risk Data Bank of Japan. Daimei Inaba is Executive Director of the Risk
Data Bank of Japan. Kentaro Yanagisawa is Group Manager of the Risk Data Bankof Japan.
The views expressed in this paper are the views of the author and do not necessarilyreflect the views or policies of ADBI, ADB, its Board of Directors, or the governments
they represent. ADBI does not guarantee the accuracy of the data included in this paper
and accepts no responsibility for any consequences of their use. Terminology used may
not necessarily be consistent with ADB official terms.
Working papers are subject to formal revision and correction before they are finalized
and considered published.
The Working Paper series is a continuation of the formerly named Discussion Paper series;
the numbering of the papers continued without interruption or change. ADBI’s working
papers reflect initial ideas on a topic and are posted online for discussion. Some working
papers may develop into other forms of publication.
Suggested citation:
Nemoto, N., N. Yoshino, Y. Okubo, D. Inaba, and K. Yanagisawa. 2018. Credit Risk
Reduction Effect on Small and Medium-Sized Enterprise Finance through the Use of
Bank Account Information. ADBI Working Paper 857. Tokyo: Asian Development Bank
Institute. Available: https://www.adb.org/publications/credit-risk-reduction-effect-sme-financethrough-bank-account-information
Please contact the authors for information about this paper.
Email: nnemoto@adbi.org
The authors would like to thank Soheon Lee, a research associate at ADBI, for her
research assistance.

Asian Development Bank Institute
Kasumigaseki Building, 8th Floor
3-2-5 Kasumigaseki, Chiyoda-ku
Tokyo 100-6008, Japan
Tel:
Fax:
URL:
E-mail:+81-3-3593-5500+81-3-3593-5571
www.adbi.org
info@adbi.org© 2018 Asian Development Bank InstituteADBI Working Paper 857

Nemoto et al.

Abstract
This paper verifies the impact of bank account information, such as information on deposits
and withdrawals, that is not necessarily fully accounted for in conventional internal ratings
and that can affect the accuracy of the default predictions of small and medium-sizedenterprises (SMEs). The analysis demonstrates that the accuracy of default predictions
improves when a model based on bank account information is used in addition to the default
prediction model based on traditional financial information. The analysis also shows that thedegree of improvement increases when the size of the company is small. For small
companies, the quality of financial data is generally assumed to be low, but the bank accountinformation model can complement the incomplete data. In addition, for small firms, the bankaccount information model shows better default prediction capability compared to thefinancial model, which implies the possibility that banks could extend loans even if only thebank account information is available. The correlation coefficients of the financial model andthe bank account model are higher than 50% but not very high, suggesting that thesemodels evaluate borrowers from different perspectives.
This study suggests the possibility of analyzing credit risk more easily without past financial
information, especially for small enterprises. If the bank account information model is utilized,banks can reduce credit costs and loan review times and costs, which will make SME
financing more efficient and smooth. The empirical analysis in this paper focuses on SMEs
in Japan, but the results can also be applied to other countries, particularly emerging
countries in Asia.
Keywords: small and medium-sized enterprise finance, credit risk analysis, big data, bankaccount information
JEL Classification: G2, G21

ADBI Working Paper 857

Nemoto et al.

Contents
1.

INTRODUCTION ......................................................................................................... 1

2.

LITERATURE REVIEW ............................................................................................... 3

3.

THEORETICAL BACKGROUND................................................................................. 54.

ANALYSIS OF THE SME CREDIT RISK MODEL ...................................................... 6
4.1
4.2
4.3
4.4

Credit Risk Models Using Financial Information and Bank
Account Information ........................................................................................ 6
Characteristics of Financial Information and Bank Account Information ......... 7
Sample Data .................................................................................................. 10
Analysis Results ............................................................................................ 125.

VERIFICATION OF THE CREDIT RISK MODEL...................................................... 19

6.

CONCLUSIONS ........................................................................................................ 22

REFERENCES ..................................................................................................................... 24
APPENDIX: ACCURACY RATIO .......................................................................................... 26

ADBI Working Paper 857

Nemoto et al.

1. INTRODUCTION
The growth of small and medium-sized enterprises (SMEs) is a critical issue for the
economic development of Japan and other Asian countries. SMEs rely on bank loans
for their external financing, but they sometimes have difficulties in obtaining sufficient
funds from banks in a timely manner. This paper examines whether the utilization ofbank account information, such as information on deposits and withdrawals, which has
not been fully accounted for in Japanese banks’ loans to SMEs, can enhance theaccuracy of credit risk measurements.
SMEs play an important role in the development of the Japanese economy andaccount for about 40% of Japan’s total gross domestic product (GDP) and 74% of all
employers. Japanese companies mainly rely on indirect financing by banks, but thetrend is even stronger for SMEs, and banks make up much of external borrowing. The
lending volume of national banks for SMEs was ¥248 trillion in 1998. The figure hassince declined but has been recovering since 2012 and remained at ¥212 trillion as ofthe end of 2017. Bank lending as a proportion of lending also declined from 49.3% in1998 to 42.0% in 2016.
The lack of public information on SMEs’ corporate activities compared to large
enterprises, and the large asymmetry of information between borrowers and lenders,makes it difficult for banks to manage the credit risk of SMEs (Yoshino and Yamagami2013). Therefore, there is a tendency to rely on collateral, such as real estate, personal
assets, and the guarantees of CEOs, rather than judging the creditworthiness of thecompany itself (Financial Services Agency 2003). Small companies also tend to rely on
their own funds, funds from acquaintances, and public funds, etc. without depending onbanks (Small and Medium Enterprise Agency 2015; Yamori 2003).
Analysis of the creditworthiness of SMEs by banks has made much progress since thebad loan problems of the 1990s. One example is the implementation of the internal
credit rating system that ranks companies according to financial strength. In addition to
major financial indicators, such as the capital adequacy ratio, there are cases where
qualitative factors, such as management’s abilities and financial transparency, are also
taken into account. Also since, the 2000s, the financial scoring model has become
pervasive (Financial Services Authority 2003). Scoring is a lending model constructedby statistical methods that estimates the probability of bankruptcy of loan claims anduses the probability to determine loan extension and loan rate spreads. The scoring
method does not manage risks on a case-by-case basis but manages the risks onloans throughout the portfolio control based on the law of large numbers. Therefore,
the accuracy tends to increase as the data pool becomes larger, so the construction of
the database is important. The Japan Risk Data Bank (RDB) 1 , comprised of majorbanks and regional banks, was established in 2000 as the first data consortium inJapan, and, in 2001, the SME Credit Risk Information Database (CRD) 2 wasestablished under the initiative of the Ministry of Economy, Trade and Industry, and the
Small and Medium Enterprise Agency. These data are used for loan reviews, interest

1

2

The Japan RDB has more than 60 financial institutions, such as major Japanese banks and regionalbanks, as members and shares the credit risk information of 650,000 client companies on an
anonymous basis. In addition to financial information, operational risk information and bank accountdynamics information, etc. are also gathered.
Approximately 190 financial institutions, including credit unions, are members of the SME Credit Risk
Information Database (CRD) and share credit risk information on about 2 million credit companies on an
anonymous basis. The average size of SMEs is slightly smaller than in the RDB.

1

ADBI Working Paper 857

Nemoto et al.

rate setting, and portfolio management, etc. and have contributed to the advancementof credit risk analysis by banks.
On the other hand, there are limits to internal ratings and scoring by banks (Hirata
2005). First, in many cases, there are problems with the quality of the financial
statements of SMEs. According to a survey by the Small and Medium Enterprise
Agency, only about 30% of firms are considering preparing accounts based on
appropriate accounting for strengthening financing capability (Small and Medium
Enterprise Agency 2004). Second, there is a time lag of information. There have been
many cases where the latest financial statements for the settlement dates acquired
for examination were from 3–15 months ago, so the current state of the companies
was unclear. In addition, monitoring after financing is not sufficient. With financial
statements alone, banks have difficulties in grasping the situations of its clients, which
can change daily throughout the fiscal year.
To solve these problems, banks can detect the window dressing of accounts byinterviewing and researching changes in the business environment after the settlement
date to counteract the weaknesses of ratings and scoring. However, under prolonged
monetary easing, the profitability of financial institutions has deteriorated, and efficientmanagement is required now more than ever. Financial technology (fintech) companies
that provide loans to individuals and SMEs quickly and easily via personal computers
and mobile phones are also entering the market. Under such an environment, it isgetting harder for banks to continue traditional labor-intensive lending.
Utilizing bank account information, as considered in this paper, could increase theability of banks to analyze the credit risk of SMEs and contribute to reductions in
the time and costs required for review. In Japan, most money transfers andsettlements, which are the result of corporate activity, are done through bankaccounts, and “account history information” contains abundant information on
businesses—specifically, deposit account withdrawal information, inward and outward
remittance data, bills for collection, discounted commercial bills, electronically recordedmonetary claims, and loan execution collection details, etc. By continuously monitoringthese data, it is possible to continuously capture the actual business situations ofclients over time and predict changes in their credit situations.
Numerous academic studies have proved that SMEs can be ranked by credit risk using
models that utilize corporate financial data or bank lending data, such as delinquency
conditions (Behr, Guttler, and Plattner 2004; Yoshino and Taghizadeh-Hesary 2014).
There are also several empirical studies on the changes in the accuracy of the model
depending on the choice of variables and whether to incorporate qualitative information
when constructing the credit risk model. However, although the importance of bankaccount information is recognized in banking practices, research that testifies thevalidity of bank account information has been rare. The contribution of this paper is toshow empirical analysis of bank account information that utilizes a large pool of bothfinancial and bank account information (big data) held by the RDB.
This paper demonstrates that the accuracy of default prediction improves when amodel based on bank account information is used in addition to the default prediction
model based on traditional financial information. There is a tendency for the
improvement to increase when the size of a company is small. If the size of thecompany is small, the quality of financial data is generally assumed to be low, but
the bank account information model can complement this. In addition, for small firms,
the bank account information model shows better default prediction compared to thefinancial model, which implies the possibility that banks could extend loans even if only
the bank account information is available. The correlation coefficients of the financial

2

ADBI Working Paper 857

Nemoto et al.

model and the bank account model are higher than 50% but not very high, suggestingthese models evaluate borrowers from different perspectives.
If the utilization of the bank account information model spreads, banks can reduce their
credit costs and reviews time and costs, and make loans to SMEs more efficiently. The
empirical analysis in this paper is targeted at SMEs in Japan, but the results could also
be relevant for other countries, particularly in emerging countries in Asia.
SMEs in Asian countries have not obtained sufficient funds for growth, and according
to a survey by the International Finance Corporation, the supply shortage of loans to
SMEs as of the end of 2014 was $706 billion for East Asia and $2,060 billion for South
Asia. In Asia, the loan balance for SMEs accounts for 19% of bank loans overall, which
is lower than in other regions. There are several reasons why SMEs do not seek bankloans, such as the collateral requirements, complicated application procedures, banklending conditions not meeting their needs, and high loan interest rates (ADB 2015).
The asymmetry of information is large, especially in low-income countries, and thequality of the financial information on SMEs is low and many are not audited.
Regarding a common database, there are public credit information agencies in 8 of
the 20 ADB member countries. Information collected by credit information agencies
includes (1) information on business, (2) information on bank transactions (including
default information), and (3) information on firms’ financial situation. However,companies that do not have bank transactions do not have information on businessand bank transactions, and the credibility of the information provided by member banks
is not accurate enough in many cases, so it appears that banks only use thisinformation for supplementary purposes when making loans. In most of the countries,
there have not been solid systems for banks to share the credit risk information of theirclients or provide risk information, such as the estimated default rates of companies
such as by the RDB.
In Asia, the proportion of companies receiving bank loans is as small as 15.4%
for small companies, but 79.4% have bank accounts and use bank accounts forsettlements, etc. (ADB 2015). If bank account information were to be usedsystematically, we could expect the expansion of loans for SMEs.
The structure of this paper is as follows. Section 2 will look at the findings fromprevious research. Section 3 shows the theoretical background; and Section 4 explains
the credit risk prediction method; the variables of the respective models using financial
information, bank account information, and both methods together; and the results ofthe constructed credit models. Section 5 shows verification of the credit risk models
in terms of the default prediction ability, and Section 6 concludes. The Appendix
supplements the explanation of the methods used for verification.

2. LITERATURE REVIEW
There have been many studies, inside and outside Japan, which introduce credit risk
models for SMEs. Most of them demonstrate that credit risk models utilizing financial
statements appear to be useful in differentiating SMEs according to their credit risk
and thus in banks’ loan decisions (Behr, Guttler, and Plattner 2004; Yoshino andTaghizadeh-Hesary 2014). Behr, Guttler, and Plattner (2004) quantify the credit risk ofGerman SMEs based on a logistic regression model. The variables selected in theirmodel are mainly the financial statements of SMEs (e.g., the equity ratio), but themodel also incorporates qualitative features of companies, such as location, corporate
structure, and line of business. The default probability of large firms estimated by this3

ADBI Working Paper 857

Nemoto et al.

model shows a high correspondence with the credit ratings disclosed on them by global
credit rating agencies. Also, the accuracy ratio (AR) (see Appendix) for the creditscores estimated by this model is higher than 80, which proves that the model has agood prediction ability. Since the information on the firms tends to be concentrated on
major banks in Germany, thereby hindering the diversification of the funding channels
and, thus, the growth of SMEs (the hold-up problem), another advantage of the scoringmodel would be that it could mitigate this problem. By providing an objective
benchmark, the scoring model could eventually contribute to the diversification of thefunding channels of SMEs.
Grunert et al. (2005) demonstrate that the combination of a firm’s quantitative
information and qualitative information, such as on management efficiency, results in amild increase in the model’s default prediction ability. Similarly, a model by Anzai(2015) incorporates qualitative information on technology levels. It introduces atechnology level index for SMEs and shows that firms with a high level of technology
are capable of relatively swift recovery from the downturn of profitability. This implies acorrelation between SMEs’ financial statements and their business competitiveness.
On the other hand, Freichs and Wahrenburg (2003) point out that the model’sestimation ability can be improved enough to overcome the limitations from the lack ofqualitative information when the data pool covers vast amounts of quantitative
information from many banks.
Although most of the studies on the credit risk model focus on the case of advanced
countries, there exist a few studies that cover SME finance in emerging countries.
Yoshino and Taghizadeh-Hesary (2014) examine the effectiveness of credit risk
assessments of Iranian companies based on financial statements held by banks. Morespecifically, they present a model estimating the default probabilities of SMEs, with therelevant variables selected through principal component analysis and cluster analysis(clustering). The results of the study indicate that banks can classify SMEs according totheir credit risk based on the data of financial statements held by banks, and this canbe utilized in loan decisions and the pricing of loan rates. The study also emphasizes
the importance of a corporate credit information database. Similarly, Yoshino et al.
(2016) develop a model for assessing the default probability of SME lending in
Thailand through principal component analysis. Based on a database of SME lending
provided by the public credit information agency of Thailand, the model classifies SMEs
according to their estimated default probability.
On the other hand, there are studies which point out the limitations and room for
improvement of the scoring model while admitting the effectiveness of the approach
itself. Hirata (2005) examines the advantages and the disadvantages of the scoringmodel. For the advantages of the scoring model, the study mentions that (1) banks
can streamline the troublesome process of loan examination in SME lending; (2) byquantifying risk, banks can monitor the quality of portfolios as a whole, which canalso be utilized in new customer acquisition; and (3) the scoring model supports
market-based finance as it makes securitization easier. On the other hand, the paper
points out the disadvantages of the scoring model that (1) there will be sample
selection bias since the model only covers the information of firms who applied forbank loans; (2) the accuracy of the model is limited due to the low quality of SMEs’
financial statements; (3) the model does not necessarily guarantee more efficiency
since it often requires an additional process, such as in-person interviews, due to theimperfect information.

4

ADBI Working Paper 857

Nemoto et al.

This paper builds a credit risk model based on financial statements as well as a model
based on bank account information, such as the change in deposits or the levelof deposits and loans in comparison with sales. In building the credit risk model, thepaper refers to the methodology used in Yoshino and Taghizadeh-Hesary (2014) andYoshino et al. (2016 ). Then, we examine the accuracy of the default prediction of themodels. The results indicate that the accuracy of the default prediction of the model
based on financial statements is improved when combined with the model based onbank account information. Especially for the case of micro-sized firms, who are
generally assumed to have low-quality financial statements, the model tends to show alarge degree of improvement of the default prediction accuracy. This implies that themodel based on bank account information could mitigate the information quality issue.
We also find that the creditworthiness predicted by each model is correlated with eachother and that the accuracy of default prediction by the model based only on dynamic
bank account information is comparable to the model based on financial statements insome cases. Bank account information could reveal the actual condition of a firm’s
cash flow, and its time lag is much shorter than financial statements, which makes it agood complement to financial statements of low quality. Moreover, it could reduce
sample selection bias since it also contains the information of the firms without bankloans.
Many studies have indicated that the accuracy of the credit risk model depends on theselection of the variables. However, the effectiveness of the utilization of dynamic bankaccount information has not been examined before this paper, despite the growing
perception of its importance among bankers and financiers. Another new contributionof this paper is that it utilizes big data derived from the RDB’s database, which collects
both financial and bank account information from Japanese banks. In addition, thispaper applies principal component analysis, which has the advantage of reducing
multidimensional datasets to lower dimensions of analysis while minimizing the loss of
information. Principal component analysis also ensures objective and quantitative
assessment in selecting variables.

3. THEORETICAL BACKGROUND
Before delving into an empirical analysis of credit risk, here we describe the theoreticalbackground regarding the relationship between a bank’s profit maximization and theimprovement of credit risk analysis. A bank’s profit maximization behavior can be
written as follows, where Π denotes the bank’s profit.
𝑀𝑎𝑥 Π = 𝑟𝐿 (𝐿)𝐿 − 𝜌(𝐶𝑅𝐴, 𝑍𝑖 𝐿)𝐿 − 𝑟𝐷 𝐷 − 𝐶(𝐿, 𝐷)

s.t. kDeposit + Loan = Deposit + Capital
Assumptions:𝑟𝐿 (∙) denotes the loan rate, which is a function of the amount of the loan𝜌(∙)

denotes the default risk, which is a function of the credit risk analysis (CRA), theinformation of individual company(𝑍𝑖 ), and the loan amount (L)𝐶(∙)

denotes the operating cost, which is a function of the loan(L) and the deposit (D)𝑟𝐷denotes the interest rate on the deposit (𝐷)5

ADBI Working Paper 857

Nemoto et al.

For simplicity, the capital cost is abstracted from the model, and we assume thatthe bank uses all of the deposits (D) in making loans (L). Also, instead of assuming
perfect competition, we assume that banks are in oligopolistic competition. That is, theloan rate is not given but is determined by the supply and demand of the bank’sloanable funds.
Π = 𝑟𝐿 (𝐿) ∙ 𝐿 − 𝜌(𝐿, 𝑍) ∙ 𝐿 − 𝑟𝐷 (1 − 𝑘)𝐿 − 𝐶(𝐿, 𝐷)

∂Π
𝜕𝐿

=�

𝐿∙�

𝜕𝑟𝐿

𝜕𝑟𝐿

𝜕𝐿

∙ 𝐿 + 𝑟𝐿 � − �𝜕𝜌𝜕𝐿∙ 𝐿 + 𝜌� − (1 − 𝑘)𝑟𝐷 −

𝜕𝐿

−

𝜕𝜌
𝜕𝐿

� = −𝑟𝐿 + (1 − 𝑘)𝑟𝐷 +

�

−

�

𝜕𝐿

1

𝐿 = 𝜕𝑟𝐿 𝜕𝜌 �−𝑟𝐿 + (1 − 𝑘)𝑟𝐷 +𝜕𝐿

𝑟𝐿 = −

𝜕𝐿

𝜕𝑟𝐿
𝜕𝐿

∙𝐿+�

𝜕𝜌
𝜕𝐿

𝜕𝐶

𝜕𝐶
𝜕𝐿

+𝜌

+ 𝜌�

∙ 𝐿 + 𝜌� + (1 − 𝑘)𝑟𝐷 +

𝜕𝐶
𝜕𝐿

(1)
=

(2)
(3)
(4)

𝜕𝐶
𝜕𝐿

(5)

Equation (1) is the profit function of the bank: the loan revenue deducted by the credit
cost that occurs at the time of borrower’s default, the funding cost of deposits, and theoperating cost. The bank attempts to maximize its profit, which requires equation (2) tohold true as a first-order condition. Equation (2) can be developed into equation (4),keeping the left side as the loan (L). If the bank could reduce the default risk with the
newly introduced credit risk analysis, the marginal default risk, 𝜕𝜌, will decrease, andthe bank loan (L) will increase. Also, if the credit risk analysis is conductedsystematically and effectively, the marginal operating cost, 𝜕𝐶 , will also decrease,which will lead to an increase in the bank loan.

Equation (2) can also be developed into equation (5), keeping the left side as theinterest rate (r). If the bank can reduce the default risk with the newly introduced credit
risk analysis, the marginal default risk, 𝜕𝜌, will decrease. Also, if the credit risk analysis
is conducted systematically and effectively, the marginal operating cost, 𝜕𝐶, will also
decrease, which will lead to a decrease in the interest rate, r. Therefore, it can beconcluded that, other things being equal, the new methods of credit risk analysis
introduced in this paper could lead to an increase in the banks’ SME loans and adecrease in interest rates.

4. ANALYSIS OF THE SME CREDIT RISK MODEL
4.1 Credit Risk Models Using Financial Information
and Bank Account Information
For our analysis, we develop three credit risk models aimed at assessing the credit
quality of companies: (1) a model using financial information, such as balance sheets
and financial statements (financial model); (2) a model using bank account information(bank account model); and (3) a model using both financial information and bankaccount information (hybrid model).

6

ADBI Working Paper 857

Nemoto et al.

We employ the following logistic regression model to develop the credit risk
assessment model in this paper. The logistic regression model has been used byprevious research (Behr, Guttler, and Plattner 2003) and for Japanese banks’ credit
risk models.
1−𝜌� = 𝛽0 + 𝛽1 𝑥1 + 𝛽2 𝑥2 + ⋯ + 𝛽𝑘 𝑥𝑘ln �𝜌In this equation, ln(∙) is the natural logarithm; 𝜌 is the probability of default; 𝛽0 is theintercept; 𝑥𝑖 , 𝑖 = 1, ⋯ , 𝑘 are financial (dynamic) indicators, which are explanatory
variables; 𝛽𝑖 , 𝑖 = 1, ⋯ , 𝑘 are coefficients; and 𝑘 represents the number of indicators
used. In the process of developing the model, we assigned 1 to companies that
defaulted and 0 to companies that did not default during the observation period. Then,
we used these variables including 𝑥𝑖 , 𝑖 = 1, ⋯ , 𝑘 and estimated 𝛽𝑖 , 𝑖 = 0, ⋯ , 𝑘, using the
maximum likelihood approach. In addition, the following 𝑠 is called the “beta value”,
where the estimated values of the intercept and coefficients are 𝛽̂𝑖 , 𝑖 = 0, ⋯ , 𝑘.
s = 𝛽̂0 + 𝛽̂1 𝑥1 + 𝛽̂2 𝑥2 + ⋯ + 𝛽̂𝑘 𝑥𝑘

The next step is to identify the variables. For this study, we follow Yoshino andTaghizadeh-Hesary (2014) and Yoshino et al. (2016), who employed principal
component analysis for the credit risk model of SMEs. We applied component analysis
to the financial data and bank account data and then selected the components that hadeigenvalues of more than 1. Principal component analysis is a technique for simplifying
and reducing multidimensional datasets to lower dimensions of analysis while
minimizing the loss of information. This method has the additional advantage of being
able to select variables using quantitative and non-discretionary methods. Eachcomponent is designed to be uncorrelated so that multicollinearity issues are detected
and the prediction is reliable.
The principal component Z is shown by the following equations:𝑍1 = a11 x1 + a12 x2 + ⋯ + a1𝑃 x𝑃 = ∑a1𝑖 x𝑖 ;∑a1𝑖2 = 1

𝑍𝑘 = ak1 x1 + ak2 x2 + ⋯ + ak𝑃 x𝑃 = ∑a𝑘𝑖 x𝑖 ;∑ak𝑖2 = 1

Here, x is an indicator of the financial information and bank account information, a isthe loading vector of the indicator, and k is the number of components. In the processof creating the hybrid model, we apply principal component analysis for both thefinancial and bank account information and select components with higher eigenvalues.
As the hybrid model incorporates both financial and bank account information, it isexpected that the model will have higher default predictability compared to the modelusing only one factor.

4.2 Characteristics of Financial Information and Bank
Account Information
Financial information refers to financial statements consisting of a balance sheet andan income statement, which represent a brief picture of a company’s operational
performance and financial activities during a specific period. In general, Japanesefinancial institutions obtain the most recent financial information for multiple fiscal
periods from their prospective corporate borrowers prior to loan approvals. Corporate

7

ADBI Working Paper 857

Nemoto et al.

financial statements are highly reliable because companies are required to preparethem pursuant to the accounting standards set forth by law. They also contain a great
deal of information. However, financial statements are generally prepared only
annually, 3 and financial institutions need to wait for another three months or so toobtain such reports summarizing business results for the previous 12-month period.
Therefore, one could argue that banks may fail to identify changes in their customers’
business conditions that might have occurred over around the past 15 months.
Bank account information refers to information related to deposit account balances,transaction amounts, outstanding loan balances, and loan extensions and collections.
In the case of a company that has accounts with multiple banks, the information thateach bank can obtain is only that of the company’s account with that bank. That is, if
the bank is the main bank of the company and is used by the company for a significant
share of its activities, it can grasp the overall status of the company’s businessbecause it is able to accumulate broader bank account information than other banks. In
contrast, the bank account information is less useful if a bank has a weak business
relationship with the target company and may even be useless for a bank with which
the target company has no account.
For a financial institution that serves as the main bank for the target company, bankaccount information on the company has been already accumulated within its
database, so acquisition costs are low, and the data is more up-to-date compared withfinancial information. As such, the institution could use them effectively in monitoring
the target company’s day-to-day changing status.
In our financial model, we use 77 financial indicators, as shown in Table 1. Previous
research (Yanagisawa et al. 2007) used 91 financial indicators, from which we selected
82 indicators and omitted those with high proportions of zero value or values beyond
the limits of 2.5% and 97.5%. Furthermore, we omitted indicators with absolute values
to have consistency between the financial model and bank account model.
The bank account model uses the balances of liquid deposits, fixed deposits, andoutstanding loans as well as the amount of inflows and outflows of liquid deposits.
Checking and savings accounts are treated as liquid deposits, and term deposits aretreated as fixed deposits. In addition, the sum of the liquid deposit balance and thefixed deposit balance is defined as the total deposit balance; the total deposit balanceminus the outstanding loan balance is defined as the net deposit balance. For the useof the bank account model, 79 indicators are created in the form of ratios. To calculate
the ratios, the numerators are the amounts of deposits, loans, and net deposits at theend of the month. We also use the minimum and maximum amounts and the standard
deviations in a certain period as numerators. The denominators are the total sales orloan amounts. The indicators include the percentage of growth and the reduction ofdeposits and loans.

3

Some corporations, such as listed companies, are required by law to prepare quarterly financial reports.
However, most SMEs prepare statutory financial statements only once a year in Japan.

8

ADBI Working Paper 857

Nemoto et al.

Table 1: List of Financial Indicators
Cash ratio
Net-debt turnover period
Cash plus marketable securities
ratio
Interest coverage ratio
Cash flow to sales ratio

Indicators
Interval measure
Net interest-bearing debt
turnover period
Interest burden to sales ratio
EBIT to interest and discountrate expense ratio
Cash flow to total assets ratio

Cash flow to interest-bearing
debt ratio
Cash flow to total expense ratio
Labor share

Cash flow to debt ratio

Break-even point ratio
Working capital ratio

Ordinary profit and loss ratio
Quick (acid test) ratio

Trade payables to tradereceivables ratio
Fixed assets to long-term
liabilities ratio
Debt ratio

Equity ratio

Capitalization rate
Price book-value ratio

Debt to sales ratio
Working capital to total asset
ratio
Earnings before interest andtaxes to sales ratio

Sales, general, andadministrative expenses to sales
ratio
Income before tax to sales ratio
Earnings before interest andtaxes to total assets ratio
Return on assets
Return on equity
Current income to equity ratio
Fixed asset turnover
Trade payable turnover period
Interest-bearing debt to cash
Earnings before interest
depreciation and amortization tointerest-bearing debt

Cash flow to operating cost
Capital investment efficiency

Leverage
Equity to total asset ratio

Current income to sales ratio
Ordinary income to total
assets ratio
Unappropriated retained
earnings to total assets ratio
Earnings before interest andtaxes to equity ratio
Cash and deposits/short-termborrowings
Receivables turnover period
Net interest-bearing debt toassets ratio
Interest-bearing debt ratio
Debt capacity ratio

Source: Authors.

9

Debt to equity ratio
Gross profit to interest anddiscount expense ratio
Net interest burden to
sales
Cash to debt ratio
Cash flow to total liabilities
ratio
Credit interval
Value-added to sales ratio
Depreciation
costs/ordinary earnings
Cash asset ratio
Working capital to sales
ratio
Fixed assets to equity
ratio
Long-term debt ratio
Equity to total liabilities
ratio
Quick ratio
Gross profit to sales ratio
Ordinary income to sales
ratio
Return on total assets
Current income to total
assets ratio
Current ratio
Ordinary income to equity
ratio
Total asset turnover
Inventory turnover period
Reimbursement period
Debt to equity ratio
Turnover period ofinterest-bearing debt

ADBI Working Paper 857

Nemoto et al.

4.3 Sample Data
The data for both financial information and bank account information for this statisticalanalysis were provided by the RDB, one of the leading data consortiums in Japan. The
study also covers data on loan classifications, which are assigned by member banks of
the RDB for internal credit risk analysis. The base dates are set at the end of 2015 and2016. We apply the credit scoring model to all companies and compare the status of
those companies after a 1-year period to judge the accuracy of the credit scoring. The
financial information is annual data that were submitted to member banks between
June 2014 and September 2016. The bank account information is basically daily datafrom which we use 400 observation points during 1 December 2014–31 December
2016. 4 The study covers entities that meet all the following conditions:
1. Nondefault status 5 as of 31 March 2015 or 31 March 2016 (referred to as thebase dates);
2. Granted loans from the data-providing banks as of the base date;
3. Able to provide financial information for a period of 3–18 months prior to thebase date;
4. Able to provide bank account information for a period of 13 months prior to thebase date;
5. Sales below ¥10 billion; 6 and
6. Share of deposits at the bank 7 of above 50% but below 200%.
In the next section, we show the default predictability of each model that has been
described in this section. The default observation period is defined as 1 year from thebase date in that calculation. We classify companies that made full repayments of theirloans to the data-providing bank during the observation period as nondefaulted as ofthe end of the fiscal term. To show the distribution of the sample data, Figure 1 shows
the common logarithms of the sales of all companies used for the analysis. The medianfor sales is ¥150 million, and the peak of the distribution is around that point.

4

5

6
7

It could be argued that we could collect more information if we were to extend the observation periods.
On the other hand, longer periods means higher costs for acquiring the data. Therefore, we choose
13 months as the period is not too long, and we can calculate the annual growth rate.
With respect to the classification of obligor status, we include firms lower than the category of “in dangerof bankruptcy” under the Financial Restoration Law. This category is defined as those firms with anegative net worth or that are vulnerable to a change of environment. Under this standard, default
includes obligors classified as “in danger of bankruptcy,” “de facto bankrupt,” and “bankrupt.” We
consider obligors classified as “normal (seijo-saki),” “need special attention,” and “substandard” to be“nondefault.”
This is because the focus of this study is on SMEs.
The share of deposits represents the share of the target company’s account for settlement at the dataproviding bank. It is calculated by dividing the amount of “business-related” deposits for a period of
12 months prior to the end of the fiscal year by the total amount of sales in the financial statement forthat period. Business-related deposits are the amount of deposits that were deposited in the entity’s
account at the data-providing bank minus the amounts of deposits or withdrawals that are not directly
related with business, such as loans granted, withdrawals of term deposits, and transfers within theentity, leaving only the transactions that are the related sales and payments of the entity. The higher theshare of deposits or withdrawals, the more effectively the model functions.

10

ADBI Working Paper 857

Nemoto et al.

Figure 1: Distribution of Targeted Firm Sales

Note: Total number = 42,654; default = 441, non-default = 42,213.
Source: Authors’ calculations.

In the process of developing a credit risk assessment model, it is common to divide thetarget data into two sets, one for in-sample data and another for out-sample data. The
model can then be created based on the in-sample data and applied to the out-sample
data to check the validity of the model. We follow this approach in our analysis here,
dividing the target data into two sets in a way that the number of defaults and thenumber of nondefaults in the in-sample data and the out-sample data become 1-to-1. 8
Table 2 shows the results of the default ratios of the target companies.
Table 2: Default Frequency Data

In-sample dataOut-sample data
Total

Total
21,32821,32642,654

Default Frequency Data
Nondefault
Default
21,107
221
21,106220
42,213441

Default Ratio
1.04%
1.03%
1.03%

Source: Authors’ compilation.

The number in the “default” column indicates the number of companies that defaulted
in the observation period, and the “nondefault” column shows the number of companies
that did not default in the same period. Table 3 shows the sales comparisons forthe data.

8

To make sure that no large differences in the characteristics exist between the in-sample data and theout-sample data, we compared the sales of the two sets of data. The p-value in the t-test is sufficiently
large, which confirms that there is not a big discrepancy between the average sales in the in-sample
data and those in the out-sample data.

11

ADBI Working Paper 857

Nemoto et al.

Table 3: Comparison of Sales for the In-Sample and Out-Sample Data

In-sample data
Out- sample data
Total

Cases
21,32821,32642,654

Average
492
492
492

Corporate Sales Amounts (¥ million)
STD 25% 50% 75%
t-statistic
1,043
58
147
413
–0.04
1,048
58
145
418
1,045
58
146
415

p-value
0.9711

Source: Authors’ compilation.

4.4 Analysis Results
Using the datasets explained in Sections 4.2 and 4.3, we develop (1) a financial modelusing the financial information of SMEs, (2) a bank account model using the accountinformation from data-providing banks, and (3) a hybrid model using both the financial
and bank account information. The model development procedures are as follows:
1. Set up the upper and lower limits for the indicators 9
2. Apply principal component analysis to the indicators and find components andeigenvalues of each component.
3. Select those factors accounting for more than 10% of the variance (eigenvalues
are larger than 1), which are regarded as significant components for explaining
the variance
4. Develop the logistic model using factors as explanatory variables and find theestimated coefficients of the variables
The results of the principal component analysis for the financial model are shown in
Table 4, the bank account model results in Table 5, and the hybrid model in Table 6.
The first process through the third process are performed on the overall data including
the in-sample data and the out-sample data.
Table 4 shows that the first component with the highest eigenvalue has 22.6%
variance, which means it explains 22.6% of the total variance of the financial ratios.
Factors 1–16 are used for the financial model as they have eigenvalues higher
than 1. Factors 1–11 are used for the bank account model, and factors 1–26 are used
for the hybrid model. The cumulative variances of the models are 84.1%, 91.2%,
and 86.9%, respectively, indicating that a large part of the total variance is explained by
the components.

9

In setting the upper and lower limits for each indicator, we replace large (small) values that could impair
the estimation accuracy of coefficients of the logistic regression model with the upper (lower) limit. The
lower limit is set at 2.5% and the upper limit is set at 97.5%.

12

ADBI Working Paper 857

Nemoto et al.

Table 4: Results of Principal Component Analysis: Financial Information(Balance Sheets and Financial Statements)

Eigenvalue
17.37

Share of
Variance(%)
22.6

Cumulative
Variance
(%)
22.6

2

11.86

15.4

38.0

3

7.03

9.1

47.1

4

5.78

7.5

54.6

5

3.72

4.8

59.4

6

3.22

4.2

63.6

7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30

2.47
2.09
1.94
1.71
1.51
1.38
1.27
1.18
1.15
1.11
0.99
0.93
0.86
0.79
0.70
0.60
0.56
0.51
0.48
0.43
0.42
0.37
0.34
0.33

3.2
2.7
2.5
2.2
2.0
1.8
1.7
1.5
1.5
1.4
1.3
1.2
1.1
1.0
0.9
0.8
0.7
0.7
0.6
0.6
0.5
0.5
0.4
0.4

66.8
69.5
72.0
74.3
76.2
78.0
79.7
81.2
82.7
84.1
85.4
86.6
87.7
88.8
89.7
90.5
91.2
91.8
92.5
93.0
93.6
94.0
94.5
94.9

Principal
Component
1

Source: Authors’ calculations.

13

Characteristics of Major
Components
Reliance on borrowing, such as
total assets/borrowings.
Profitability measurement, including
earnings versus expenses.
Profitability and efficiency, such as
break-even point ratio.
Liquidity at hand, such as totalliquid deposits/total assets.
Costs and expenses in comparison
with sales.
Ability to cover debt payment(earnings/interest expense).

ADBI Working Paper 857

Nemoto et al.

Table 5: Results of Principal Component Analysis: Bank Account Information

Eigenvalue
24.73

Share of
Variance(%)
38.6

Cumulative
Variance
(%)
38.6

2
3

11.99
5.16

18.7
8.1

57.4
65.4

4

4.19

6.5

72.0

5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30

3.12
2.28
1.73
1.49
1.43
1.22
1.05
0.87
0.60
0.55
0.45
0.35
0.31
0.29
0.27
0.19
0.18
0.17
0.16
0.15
0.14
0.11
0.11
0.10
0.08
0.08

4.9
3.6
2.7
2.3
2.2
1.9
1.6
1.4
0.9
0.9
0.7
0.6
0.5
0.5
0.4
0.3
0.3
0.3
0.3
0.2
0.2
0.2
0.2
0.2
0.1
0.1

76.9
80.4
83.1
85.5
87.7
89.6
91.2
92.6
93.5
94.4
95.1
95.6
96.1
96.6
97.0
97.3
97.6
97.8
98.1
98.3
98.5
98.7
98.9
99.0
99.2
99.3

Principal
Components
1

Source: Authors’ calculations.

14

Characteristics of Major
Components
Total deposit amount versus sales,
reflecting liquidity and cash flow.
Total borrowing versus total sales.
Combination of deposits/sales andloan/deposits.
Total borrowings versus total
deposits.
Increase and decrease of loans.

ADBI Working Paper 857

Nemoto et al.

Table 6: Results of Principal Component Analysis: Hybrid

Eigenvalue
32.26

Share of
Variance(%)
22.9

Cumulative
Variance
(%)
22.9

2

22.22

15.8

38.6

3

11.59

8.2

46.9

4
5

7.28
5.53

5.2
3.9

52.0
55.9

6

5.01

3.6

59.5

7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30

4.33
3.58
3.13
2.96
2.43
2.26
2.24
1.86
1.80
1.61
1.53
1.43
1.39
1.33
1.28
1.17
1.14
1.12
1.08
1.05
0.98
0.96
0.91
0.86

3.1
2.5
2.2
2.1
1.7
1.6
1.6
1.3
1.3
1.1
1.1
1.0
1.0
0.9
0.9
0.8
0.8
0.8
0.8
0.7
0.7
0.7
0.6
0.6

62.6
65.1
67.3
69.4
71.1
72.7
74.3
75.6
76.9
78.1
79.1
80.2
81.1
82.1
83.0
83.8
84.6
85.4
86.2
86.9
87.6
88.3
89.0
89.6

Principal
Component
1

Source: Authors’ calculations.

15

Characteristics of Major
Components
Deposits versus total sales(bank account).
Reliance on borrowings (financial andbank account).
Profitability, such as breakeven point(financial).
Capital adequacy (financial).
Total borrowings versus deposits(bank account).
Increase and decrease of deposits(bank account).

ADBI Working Paper 857

Nemoto et al.

For the financial model, the main components have the following characteristics:
1. The first component has variables that reflect the reliance on borrowing. For the
first component, the variables with large loadings are the total assets divided bytotal borrowings, total assets divided by interest bearing debt, and the capitalratios (capital/ total assets).
2. The second component has variables that reflect profitability. For the second
component, the variables with large loadings are the indicators reflecting
earnings versus expenses, such as the total expenses cash flow ratio and theordinary income ratio. Other variables are those reflecting total sales versus
total borrowings (e.g., the interest-bearing debt turnover and the total sales
borrowing ratio).
3. The third component has variables that also reflect profitability. For the third
component, the variables with large loadings are the breakeven point ratio andthe return on equity.
4. The fourth component has variables that reflect liquidity at hand. For the fourthcomponent, the variables with large loadings are the total liquid deposits/total
assets, the cash ratio, and the quick ratio. Total capital/total earnings also has alarge loading, which implies that companies with high earnings tend to havegood liquidity.
5. The fifth component has variables that reflect costs and expenses incomparison with sales. For the fifth component, the variables with large
loadings are indicators such as operating expenses versus total sales and sales
margins. They also include interest expense and discounted notes/gross profits
and trade receivables turnover.
6. The sixth component has variables that reflect earnings capacity to coverdebt payment. For the sixth component, the variables with large loadings
are indicators reflecting operating profits and earnings before interest, tax
depreciation, and amortization (EBITDA) compared to interest expenses andinterest-bearing debt.
For the bank account model, the main components have the following characteristics:
1. The first component has variables that compare the total deposit amounts andtotal sales. The level of total deposits versus total sales reflects whether acompany has sufficient liquidity to support business activities and has the ability
to produce a stable cash flow.
2. For the second component, the variables with large loadings are the indicatorsreflecting total borrowings versus total sales. The higher total borrowing ratiomeans higher credit risk for the firm.
3. The third component has variables that are combinations of the deposit/total
sales ratios and loan/deposit ratios. The deposit ratios include liquid
deposits/total sales and the increase of total deposits/total sales.
4. For the fourth component, the variables with large loadings are indicatorsreflecting total borrowings in comparison with total deposits.
5. The fifth component has variables that reflect the increase and decrease ofloans. The variables with large loadings are the net increase of total loans/total
sales and the increase of total loans compared to the level in the previous year.

16

ADBI Working Paper 857

Nemoto et al.

For the hybrid model, which incorporates both the financial information and bankaccount information, the main components have the following characteristics:
1. The first component has variables that reflect deposits versus total sales (bankaccount information).
2. The second component has variables that reflect reliance on borrowings. The
variables with large loadings are the total loan amounts and a mixture offinancial and bank account information (financial and bank account information).
3. The third component has variables that reflect profitability. The variables withlarge loadings are the breakeven point and return on equity ratios (financial
information).
4. The fourth component has variables that reflect capital adequacy in comparison
with total assets and cash flow (financial information).
5. The fifth component has variables that reflect total borrowings compared todeposits (bank account information).
6. The sixth component has variables that reflect the increase and decrease of
deposits (bank account information).
The first component, namely deposits versus total sales, has a 22.9% variance. Amongthe top six components, bank account information is included in four components,
which indicates bank account information is critical for default prediction.
Table 7: Results of Estimates: Financial Model(Balance Sheets and Financial Statements)
VariableIntercept
Component 1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
AIC
McFadden R2

Estimated Coefficient
5.72560.36520.1112–0.01810.2026–0.10480.2678–0.1060
0.14070.25340.1122–0.1519–0.0635–0.0731–0.0266–0.2497–0.0552
2,068.4000
0.1728Source: Authors’ calculations.

17t-statistic
1,153.4066
68.3285
11.20910.249513.0828
2.76427.60292.85316.480223.92132.5644
7.23680.92611.16770.186413.0188
0.6541p-value
0.00000.00000.00080.61740.0003
0.09640.00580.09120.01090.00000.10930.00710.33590.27990.66600.00030.4186ADBI Working Paper 857

Nemoto et al.

Table 8: Results of Estimates: Bank Account Model
VariableIntercept
Component 1
2
3
4
5
6
7
8
9
10
11
AIC
McFadden R2

Estimated Coefficient
8.45850.7818–1.4290–1.28472.65970.06960.18410.13630.17310.12720.12570.00982,461.5000
0.1329t-statistic
264.0288
98.1440
55.8695
42.7388
40.8866
2.1601
11.1913
4.11283.56900.30092.51620.0264p-value
0.00000.00000.00000.00000.00000.14160.00080.0426
0.05890.58330.11270.8709Source: Authors’ calculations.

Table 9: Results of Hybrid Model
VariableIntercept
Component 1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
AIC
McFadden R2

Estimated Coefficient
7.52730.5122–0.65210.13910.25801.10700.5399–0.6395
0.3601
0.05380.40750.7058–0.2333–0.0460
0.13980.04210.07870.0135–0.2539–0.0930–0.25020.1906–0.0149–0.1501–0.29310.14720.0542
2,461.5000
0.2092

Source: Authors’ calculations.

18

t-statistic329.6084
76.3668
40.31717.301333.3141
22.071721.051326.0072
12.7396
1.083017.3367
17.0777
6.57080.8381
3.56770.3624
1.28670.04899.0493
1.67979.490310.9591
0.04164.42519.20462.41500.7916ADBI Working Paper 857

Nemoto et al.

Tables 7–9 provide the results of the estimation of the coefficients of the logistic
regression models based on selected components with eigenvalues of more than 1.
The regression was run using the in-sample data. Table 7 shows the financial model,
Table 8 the bank account information model, and Table 9 the hybrid model. In Table 7,
the hypothesis that the coefficient is zero is rejected at the 1% level for 7 out of
16 components, showing the validity of the model. In Table 9, the hypothesis thatthe coefficient is zero is rejected at the 1% level for 5 out of 11 components. In Table 9,
the hypothesis that the coefficient is zero is rejected at the 1% level for 14 out of
26 components. These results all demonstrate the validity of the model.
Looking at Akaike’s Information Criterion (AIC), the hybrid model has a lower AIC than
the financial model and bank account model, which shows that the hybrid model has asmaller prediction error compared to other two models.

5. VERIFICATION OF THE CREDIT RISK MODEL
To compare the default prediction abilities of each model, we measured the accuracy
ratios (ARs) (see Appendix). The results are shown in Tables 10, 11, and 12. We also
computed the ARs of each model by category and by the amount of sales and verified
whether the quality of the discrimination of the model differed in terms of firm scale.
Table 10: Accuracy Ratios of the In-Sample Data
Accuracy Ratio
Group Segment

Total

Nondefault

Default

Default
Ratio

Less than ¥30 million¥30 million–¥100 million¥100 million–¥300 million
More than ¥300 million
Total

2,680
5,634
6,329
6,685
21,328

2,643
5,557
6,259
6,648
21,107

37
77
70
37
221

1.4
1.4
1.1
0.6
1.0

FinancialBank
Account

Hybrid

67.6
67.6
72.3
74.6
71.6

63.2
67.9
62.1
69.6
66.8

71.8
74.4
77.0
79.9
76.5

Note: ‘Financial’ shows the results of the financial model using balance sheets and financial statements. ‘Bank account’shows the results of the bank account model. ‘Hybrid’ shows the results of the hybrid model using both financial data
and bank account data.
Source: Authors’ calculations.

Table 11: Accuracy Ratios of the Out-Sample Data
Group Segment

Total

Nondefault

Default

Default
Ratio
Total

Less than ¥30 million¥30 million–¥100 million¥100 million–¥300 million
More than ¥300 million
Total

2,718
5,722
6,116
6,770
21,326

2,675
5,638
6,063
6,730
21,106

43
84
53
40
220

1.6
1.5
0.9
0.6
1.0

Accuracy Ratio
Nondefault

Default

Total

55.3
53.4
72.8
80.7
65.1

59.5
59.9
73.7
58.9
64.6

62.6
62.7
80.2
78.4
71.4

Note: ‘Financial’ shows the results of the financial model using balance sheets and financial statements. ‘Bank account’shows the results of the bank account model. ‘Hybrid’ shows the results of the hybrid model using both financial data
and bank account data.
Source: Authors’ calculations.

19

ADBI Working Paper 857

Nemoto et al.

Table 12: Accuracy Ratios of the Total Data
Group Segment

Total

Nondefault

Default

Default
Ratio
Total

Less than ¥30 million¥30 million–¥100 million¥100 million–¥300 million
More than ¥300 million
Total

5,398
11,356
12,445
13,45542,654

5,318
11,195
12,322
13,37842,213

80
161
123
77
441

1.5
1.4
1.0
0.6
1.0

Accuracy Ratio
Nondefault

Default

Total

61.0
60.2
72.6
77.8
68.3

61.2
63.7
67.1
64.0
65.7

66.9
68.2
78.4
79.2
73.9

Note: ‘Financial’ shows the results of the financial model using balance sheets and financial statements. ‘Bank account’shows the results of the bank account model. ‘Hybrid’ shows the results of the hybrid model using both financial data
and bank account data.
Source: Authors’ calculations.

The followings are evaluations that can be inferred from the results of the verification.
•

The ARs tend to be lowest for the bank account model, followed by the financial
model. The ARs for the hybrid model are the highest. The combination of thefinancial information with the bank account information produces a model thathas the strongest capability to detect defaults.

•

The ARs of the out-sample data in the financial model are about 6.5 percentage
points lower than the ARs of the in-sample data. The ARs of the out-sample datain the bank account model are about 2.2% lower than the ARs of the in-sample
data. However, for both models, the ARs of the out-sample data are regarded ashigh at above 65% in comparison with other studies and results of rating
agencies. Considering that the differences in the ARs for the in-sample data andthe out-sample are not very large, and the absolute level of the ARs of the outsample data are high, we can confirm the validity of the model created from thein-sample data.

•

For those firms with sales less than ¥300 million, the ARs for the bank accountmodel are higher than the ARs for the financial model. The ARs of the bankaccount model are highest in the category of companies with sales ranging from¥100 million to ¥300 million. In addition, the improvements in the ARs by adding
the bank account information to the financial information are higher for thosecategories with lower sales. This shows that the bank account information isuseful for smaller firms, which tend to have lower quality financial statements.

For the next step, we derived the Pearson correlation coefficients between the financial
model and the bank account model. The results are shown in Tables 13, 14, and 15 forthe in-sample data, out-sample data, and the total data. The Pearson correlation
coefficients of the financial model and the bank account model are about 51%. Thissuggests that both models have correlation, but the degree of correlation is not veryhigh. This offers supporting evidence that the bank account model evaluates borrowers
from a viewpoint that is different from that of the financial model. As a result, the credit
discrimination capability may improve through the hybridization of the financial modeland the dynamic model. Additionally, banks will be able to expand their target area byadding the bank account information to the financial information.

20

ADBI Working Paper 857

Nemoto et al.

Table 13: Pearson Correlations: In-Sample Data

Financial model
Bank account modelHybrid model

Pearson Correlations
Financial
Bank AccountHybrid1.00000.56370.83770.56371.00000.88820.83770.88821.0000Source: Authors’ calculations.

Table 14: Pearson Correlations: Out-Sample Data

Financial model
Bank account modelHybrid model

Pearson Correlations
Financial
Bank AccountHybrid1.00000.56550.83850.56551.00000.88880.83850.88881.0000Source: Authors’ calculations.

Table 15: Pearson Correlations: Total

Financial model
Bank account modelHybrid model

Pearson Correlations
Financial
Bank AccountHybrid1.00000.56460.83810.56461.00000.88850.83810.88851.0000Source: Authors’ calculations.

Overall, the analysis verifies that the accuracy of default prediction improves when amodel based on bank account information is used in addition to the default prediction
model based on traditional financial information. The analysis shows that the degreeof improvement increases when the size of the company is small, and the effect issignificant for companies with less than ¥300 million in annual sales. For small
companies, the quality of financial data is generally assumed to be low, but the bankaccount information model can complement the incomplete data. Also, for small firms,
the bank account model has a higher default prediction ability than the financial model.
For the hybrid model, which incorporates both the financial information and the bankaccount information, the main components are (1) the deposit amount versus total
sales, (2) the loan amount versus total sales, (3) the indicators showing profitability,(4) the capital ratios, (5) the loan amount compared to deposits, and (6) the increase
and decrease of deposits. Among the six major components, four are derived from thebank account information, which offers supporting evidence that the bank accountinformation is critical for default prediction.

21

ADBI Working Paper 857

Nemoto et al.

6. CONCLUSIONSThis paper demonstrates that the accuracy of default prediction improves when amodel based on bank account information is used in addition to the default prediction
model based on traditional financial information. There is a tendency for the
improvement to increase when the size of the company is small. If the size of thecompany is small, the quality of financial data is generally assumed to be low, but thebank account information model can complement this.
For small firms, the accuracy of the default estimation of the bank account model issuperior to that of the financial model, supporting the possibility that banks will have theability to determine the credit risks of SMEs even if only the bank account information
is used.
If the utilization of the bank account information model spreads, banks can reduce
credit costs and review times and costs and make loans to SMEs more efficiently.
Bank account information cannot be manipulated by an information provider for the
purposes of tax returns or loan applications, as is the case with financial information, so
the bank receiving the account information can easily examine the data even if thetarget company is a new customer. Accordingly, concerns over the credibility of theinformation and the personnel costs to scrutinize it, which are intrinsic issues forfinancial scoring loans, are eliminated.
In addition, lending based on bank account information enables a bank to easily
calculate the upper lending limit. By grasping the annual cash flow of the borrower’s
account, a bank can estimate the realistic amount a borrower can repay. Considering
this estimate, the bank can then determine the loan amount. In contrast, the traditional
financial scoring model is based on the financial statements of the previous fiscal year,
so simulations of the possible lending amounts do not work well in some transactions.
As such, this may lead to excessive lending.
One of the limitations of the bank account model is that the level of information
depends on the depth of relations between banks and corporate customers. If a bankhas a weak relationship with a firm, the bank account information may not grasp thewhole picture of the firm’s business activities. However, recently, “cloud accounting
firms” 10 , which can be classified as fintech companies, are providing automatic
accounting services to banks. Cloud accounting systems enable accounting firms tohave easy access to bank account information from various institutions. Soon, bankswith small shares of deposits will not be significantly disadvantaged if they have tie-ups
with those accounting firms or corporate customers.
The use of bank account information can be an effective tool for banks in analyzing
the business and financial conditions of their customers and providing effective
consulting services. In addition, it can identify commercial distribution and potential
business opportunities for clients. The speed and accuracy of the data can also be

10

Users provide the account information of all their creditor banks on the accounting software(applications) of the cloud firm, making a journal entry for each deposit and withdrawal to streamline
their monthly accounting work and account settlement procedures. The users can then make inquiries
about their financing directly to financial institutions during the accounting processing. By collaboratingwith this accounting software, financial institutions can obtain the account information of other banks,
subject to the prior consent of the user; that is, banks can potentially obtain information on all of theclient’s accounts.

22

ADBI Working Paper 857

Nemoto et al.

useful for grasping the impact of macro shocks, such as currency appreciations or
natural disasters. 11
The empirical analysis in this paper is targeted at SMEs in Japan, but the results may
also be relevant for other countries, especially emerging countries in Asia. In Asia,
the proportion of companies receiving bank loans is as small as 15.4% for small
companies, but 79.4% have bank accounts in banks and use bank accounts forsettlements, etc. (ADB 2015). If bank account information can be used systematically,
we can expect the expansion of loans for SMEs.
The policy implications of this paper are that it is crucial for financial institutions toenhance their credit risk assessment and improve service quality by leveraging bankaccount information. It would be more efficient for financial institutions to have acommon database and share information rather than developing systems on their own.
As a possible solution for other Asian economies, we provide the example of the Risk
Data Bank in Japan and show how to create a credit risk model based on financial andbank account information data. This could be important for policy makers for providing
good guidance to financial institutions and supporting the development of the credit
information system. If the use of bank account information prevails, it could help SME
have easy access to finance and enhance growth and productivity.

11

The case of Kumamoto Bank, located in Kumamoto prefecture in Japan, is an example where bankaccount information has been used for disaster management. After the Kumamoto earthquake,
Kumamoto Bank fully utilized bank account information to identify which areas and sectors were most
hit by the earthquake and took proactive remedy measures in collaboration with local governments.

23

ADBI Working Paper 857

Nemoto et al.

REFERENCES
Anzai, K. 2015. Issues in the Assessment of Small and Medium-sized Enterprises by
Financial Institutions and Suggestions for a New Methodology (in Japanese).
PhD dissertation. Sakushin Gakuin University.
Asia Development Bank. 2014. Asia SME Finance Monitor 2013.
———. 2015. Asia SME Finance Monitor 2014.
Behr, P., A. Guttler, and D. Plattner. 2004. Credit Scoring and Relationship Lending:
The Case of German SME, University of Frankfurt, Version 16. March 2004.
Financial Service Agency. 2003. Progress of Action Program for Strengthening
Relationship-based Banking (as of the first half of FY2003) (in Japanese).
Goto, Y. 2014. Macro-performance of Small and Medium-sized Enterprises:
Explaining Their Contribution to the Japanese Economy (in Japanese).
Nikkei Publishing Inc.
Grunert, J., L. Norden, and M. Weber. 2005. The Role of Non-financial Factors inInternal Credit Ratings. The Journal of Banking and Finance, 29(2): 509–531.
Hergen, F., and M. Wahrenburg. 2003. Evaluating Internal Credit Rating Systems
Depending on Bank Size. Working Paper, Goethe University Finance &Accounting No. 115.
Hirata, H. 2005. Recent Development in Japan’s Small and Medium-sized Enterprise
Finance (in Japanese), Keiei Shirin, Hosei Journal of Business, 41(2): 31–51.
Itou, M. and N. Yamori. 2015. Revitalization of Regional Banks: Toward Contribution
Banking (in Japanese). Kinyu Zaisei Jijo Kenkyu Kai.
Maehara, Y. 2013. The Role of the Credit Risk Database in SME Finance(in Japanese). RIETI Discussion Paper Series No.13-J-067. RIETI.
Okubo, Y. 2015. Complete Overhaul of Spread Banking (in Japanese). Kinyu Zaisei
Jijo Kenkyu Kai.
Okubo, Y, and T. Inaba. 2007. The Era of Credit Rating on Small and Medium-sizedEnterprises: Benefits of ‘Japan SME Credit Scoring’ Exclusively for SMEs
(in Japanese). Kinyu Zaisei Jijo Kenkyu Kai.
Simizu, S. 2016. Structural Reforms of Economy and Maintenance of Financial
Infrastructure Required for Asian Economies (in Japanese). RIM Pacific
Business and Industries 16(63).
Small and Medium Enterprise Agency. 2015. White Paper on Small and Medium
Enterprise 2015.
Watanabe, T., and I. Uesugi, eds. 2008. Examining SME Finance (in Japanese). Nikkei
Publishing Inc.
Yamashita, S., N. Kawaguchi, and T. Tsuruga. 2003. Consideration and Comparison of
Evaluation Methods for the Credit Risk Model (in Japanese). FSA Financial
Research and Training Center Discussion Paper Series 2003.
Yamori, N. 2003. Financial Services Desirable for Enterprises and Issues in SME
Finance: Focusing on the Annual Survey of Corporate Behavior Regarding
Corporate Finance in the Kansai Region (in Japanese). RIETI Discussion Paper
Series, No.06-J-003.
24

ADBI Working Paper 857

Nemoto et al.

———. 2018. The Role of Regional Financial Institutions in Regional Revitalization:
Towards an Improvement in the Quality of Financial Intermediation(in Japanese). Chuo-Keizai Group Publishing.
Yanagaisawa, K., H. Shimoda, E. Okdada, N. Simizu, and M. Noguchi. 2007. An
Analysis on the Historical Change of Explanatory Power of Credit Risk Model
based on the RDB Database (in Japanese). Japanese Association of Financial
Econometrics and Engineering, Proceedings of 2007 Summer Conference:
249–263.
Yoshino, N., and F. Taghizadeh-Hesary. 2014. Analytical Framework on Credit Risks
for Financing Small and Medium-sized Enterprises in Asia. Asia-Pacific
Development Journal 21(2): 1–22.
Yoshino, N., F. Taghizadeh-Hesary, P. Charoensivakorn and B. Niraula. 2016. Smalland Medium-sized Enterprise Credit Risk Analysis Using Bank Lending Data:
An Analysis of Thai SMEs. Journal of Comparative Asian Development
15(3): 383–406.
Yoshino, N., and F. Taghizadeh-Hesary, eds. 2016. Japan’s Lost Decade: Lessons for
Asian Economy. Springer.
Yoshino, N., and H. Yamagami. 2017. Monetary Economics: Practice and Theory(in Japanese). Keio University Press.

25

ADBI Working Paper 857

Nemoto et al.

APPENDIX: ACCURACY RATIOThe accuracy ratio (AR) is a summary of the quantitative measure of the discriminatory
power in classification models, e.g., credit scoring models. The AR measure expresses
the ratio of the area above and under the power curve (cumulative accuracy profile ofthe model under consideration versus the “perfectly” discriminating models).
An AR can take a value between 0 and 1. The closer AR is to 1 (100%), the larger theexcess surface covered by the CAP curve, and the higher the discriminative power ofthe classification system.
The accuracy ratio is sometimes also denoted as the Gini coefficient. The procedure
for calculating the Gini coefficient is illustrated below. Area B is bounded by the random
curve and the Lorenz curve, while area A is bounded by the Lorenz curve and the ideal
curve. The Gini coefficient (AR) is defined as area B divided by the total of areas A
plus B.
Sample Lorenz Curve

Source: S&P Global Fixed Income Research.

26