3.7 Credit scoring
3.7.5 Small business credit scorecard: variables
For commercial lending, a combination of qualitative judgment and quantitative measures to evaluate the creditworthiness of an applicant is recommended by Abdou et al. (2007). The reason for this is that small business lending decisions are more complex than for retail lending. Credit managers consider a wider range of factors for small business lending, such as the financial capacity of the business to repay the loan, the willingness of the owners to repay the loan, any collateral to be pledged, and the specific terms and conditions of the loan contract. At the same time, the bank does not want the credit manager to spend hours
analyzing small businesses’ financial statements to underwrite a small value loan. This is not cost effective and there is an opportunity cost for the credit manager to rather spend this time on larger loan values where the bank receives larger returns.
According to Berger et al. (2011), the most appropriate way to underwrite a large book of small-business loans is with a simple scorecard that evaluates a mix of financial and non- financial factors where the financial factors can be taken as independent variables. The findings of a study conducted by Altman et al. (2010) confirms that by including non-financial variables as predictors of failure of a small business significantly improves a prediction model’s accuracy. This finding is significant considering the limited or lack of financial information by many small businesses. Banks also incorporate macro-economic variables to accommodate the changing circumstances of a borrower (Bellotti & Crook, 2009). Furthermore, it is important that the scorecard is customized to the specific local conditions of the country and the bank (Jayagopal, 2004).
Banks usually have minimum lending criteria, which is the first point of assessment. If the applicant does not meet the minimum criteria at this point, the application is declined without any further assessment. The criteria may include the following (Frame et al., 2001):
Acceptable value loan to collateral value < 70%.
Annual turnover 3 times loan value.
Borrower years in business at least 1 year.
Current Ratio > 0.5; and
Total Assets > €100,000.
Using the 5C’s of credit, the following variables can be included:
Character
Under this section, data is collected on the business and the owners by accessing credit or public record information (Credit bureau). Examples of the data collected for the business and the owner/s is listed in Table 3-6, followed by a description of the information obtained from the credit bureaus (Bari & Cheema, 2005; Karsh & Abumwais, 2018):
Table 3-6: Character – Variables.
Owners/Members/Directors: Business:
Personal credit history.
Credit rating.
The residential status of the owner (own or rent place of dwelling).
Soft skills: ambition and drive,
innovativeness, communication skills, decision-making abilities.
Intellectual capital.
Level of education.
Experience in the same industry.
Experience in a different industry.
Administrative abilities.
Technical and operational abilities.
Age of the enterprise (time since the company was created/has been operational).
Industry sector.
Size of the business.
Type of legal entity.
Ownership structure.
Growth prospects.
Years with the bank.
Turnover.
Credit history.
Number of missed payments.
Credit rating.
Source: Authors own compilation
Public record information (credit bureau):
The four consumer credit bureaus in South Africa; TransUnion ITC, Experian, CompuScan, and XDS collect credit information when credit data on consumers exist. Information is reported by banks and providers of retail credit, where retail credit accounts constitute approximately 90% of trade line data in South African files (Turner et al., 2008).
The commercial credit reporting market is dominated by two bureaus: KreditInform and TransUnion ITC. Each bureau reports company profiles on more than two million businesses.
Many of these are historical data. The two bureaus also each have credit reports on approximately 500 000 to 600 000 businesses. One of the bureaus reports 280 000 credit- active businesses in these files, while the other reports 600 000, or combined between 10%
and 21% of the estimated number of active businesses in South Africa (Turner et al., 2008).
The credit bureau information shows the business’s payment performance history with other lenders and suppliers. According to Berger and Frame (2007), research has shown that in very small and newly established businesses, the future credit performance of the business is strongly reflected by the past credit performance of its owners. This is also true for existing businesses, where Samreen and Zaidi (2012) confirm that the most important factor to predict future default is the credit history of the business. Based on this, for existing businesses where
the credit performance information is available, the business credit history can be combined with the owner’s credit information which could add significant value to the credit risk assessment (Gupton, 2005). The business demographic information and the owners and management information which can be considered in a scorecard are included below.
As previously identified, the information can be obtained from bank application forms, bank statements and also by asking the owners to complete a specifically designed questionnaire to obtain information on the non-tangible characteristics of the owners. Furthermore, the bank official interviewing the business owner and visiting the business can rate the owners and management of the business in certain aspects to be included in a scorecard to contribute towards the final score of the applicant. Information can also be sourced by interviewing the key personnel of the business (applicant), customers of the business, speaking to members of the neighboring businesses, and information provided by suppliers of the applicant (Harif et al., 2011). Chen and Cheng (2013) confirm that by including the qualitative data (soft information) this significantly improves the power of default prediction models, which improves the estimation of expected loan loss.
Capacity
Under capacity, the financial data is obtained to determine the businesses liquidity position and profitability. The financial data includes the current assets, current liabilities, total assets and liabilities, working capital, revenue, profit and net worth (Berger & Frame, 2007). The projected earning potential of the business is usually assessed and rated by the credit analyst to include in the scorecard based on the guidelines provided in Table 3-7 below.
Table 3-7: Projected earning potential
Guidelines: Projected earning potential
Cash flow projection is accurate and realistic.
Forecasted earnings can meet the loan payments.
Future expansion can be financed through the projected earnings.
The expected growth rate is realistic.
Source: Karsh & Abumwais (2018)
For existing businesses which can provide financial statements, the following ratios can be calculated by the system and compared to industry norms. The financial ratios in Table 3-8 below were selected due to usage, appeal to researchers, and general acceptability and prediction power according to various authors (Farrell et al., 2011; Harif et al., 2011; Samreen
& Zaidi, 2012):
Table 3-8: Financial ratios
FINANCIAL RATIOS Liquidity ratios
Current Ratio: Total current assets / Total current liabilities.
Quick Ratio: (Current Assets - Inventory) / Current liabilities.
Working Capital to Total Assets Ratio: (Net Working Capital / Net Total Assets) x 100.
Profitability ratios
Gross Profit Margin: Gross income / Sales.
Operating Income Margin: Operating income / Sales.
Net Profit Margin: Net Income / Sales.
Retained earnings / Total assets.
Earnings before interest and taxes / Total assets.
Return on Assets (ROA): Net Income / Total assets.
Return on Equity (ROE): Net Income / Shareholder's Equity.
Sales growth (in the past 2 years): (Current Year's sales - Last Year's sales) / (Last Year's sales) * 100.
Financial leverage ratios
Debt to Equity Ratio: Total Debt / Total Equity.
Total Debt to Assets: Total Debt / Total Assets.
Coverage ratios
Interest Coverage Ratio: EBIT / Interest.
Debt Service Coverage Ratio: (Net Income + Finance Cost. + Depreciation) / (Repayments of long term loans + Finance Cost).
Debt leverage: Total Liabilities / EBITDA.
Activity/efficiency ratios
Receivable turnover: Days Sales / (Accounts Receivable/365).
Day’s sales in inventory: Inventory / (Cost of goods sold/365).
Payable turnover: Days Sales / (Accounts Payable/365).
Asset turnover ratio: Net sales / Average total assets.
Loan size as % of Sales.
Estimated annual sales of the business. The ‘sales to employee’ ratio would indicate whether there is insufficient staffing capacity, or overstaffing, depending on the type of business.
FINANCIAL RATIOS Market ratios
Earnings Per Share (EPS): Net Income / number of shares outstanding.
Price Earnings (PE): Ratio Market price per share / EPS.
Solvency ratios
Market debt ratio: Total liabilities / Total liabilities + Market value of equity.
Source: Authors own compilation.
The above provides an insight into the ratios which can be used to determine the creditworthiness of a small business. Samreen and Zaidi (2012) went one step further and based on questionnaires and interviews with credit managers and using statistical methods such as factor analysis, determined which ratios/variables can be considered more important and less important when determining the creditworthiness of a potential borrower. These are reflected in Table 3-9.
Table 3-9: Most important and least important predictors of default
Most important predictors of default Least important predictors of default
Credit rating.
Credit history.
Current ratio.
Quick ratio.
Operating income margin.
Gross profit margin.
Net margin.
Return on assets (ROA).
Return on equity (ROE).
Dividend per share ratio.
Market to book ratio.
Fixed asset turnover.
Capitalization ratio.
Current asset turnover.
Book value per share.
Dividend payout ratio.
Total asset turnover.
Source: Samreen & Zaidi (2012)
The above provided an insight on the ratios that can be considered under “Capacity”, below the variables to be considered under the remaining 5C’s of credit will be reviewed.
Capital
Table 3-10: Capital – Variables
Capital – Variables
Owner’s contribution to financing.
Capital – Variables
Average bank account turnover/Turnover from Income Statement.
Average bank account balance.
Debt-Equity Ratio: Long-term debt/Owners equity.
Source: Brown & Moles (2014) Collateral
Table 3-11: Collateral – Variables
Collateral – Variables
Loan to value ratio.
Sufficient collateral is offered.
Type of collateral.
Any additional guarantees.
The proportion of the business financed by the owner/manager.
Source: Golam et al., (2010) Conditions
The variables to be considered under ‘conditions,’ are included in Table 3-12.
Table 3-12: Conditions – Variables
Conditions – Variables
Type of finance, amount applied for, and term.
Sector risk – the current health of the sector.
Key buyer/supplier dependencies.
Strength of competition.
The market for this product/service is developed/growing or undeveloped/stagnant.
The existing competition who supply a similar product/service is: plentiful/limited.
Suppliers are: closely situated and plentiful.
The distribution system for the product/service is clearly defined.
Nature of product/service is marketable or undetermined.
The general economic trend at the time is conducive to the product/service marketability.
Source: Mandala et al., (2012)
It is important to note that for determining the conditions which may impact on a business requires experts to accurately derive and weigh each variable appropriately (Hahm & Lee,
2011). The scorecard for commercial lending is arguably far more complex than for consumer lending considering all the variables to be considered. An example of variables used for consumer lending is listed in Table 3-13 (Hahm & Lee, 2011; Shen et al., 2013; Annappindi, 2014; Karsh & Abumwais, 2018):
Table 3-13: Variables: Individual lending credit scoring model
Variables: Individual lending credit scoring model
Applicants’ age.
Gender: male/female.
Marital status.
Total monthly income.
Employer: applicant works in a credible company or not.
Guarantor: the existence of an alternative source of repayment if required.
The highest level of education, education discipline/concentration, year attained, educational institution.
Residency: years at current address, own/rent status.
Loan amount.
Loan purpose: car, housing, personal commitments, education, marriage, and so on.
Period with current employer.
Job experience with the current employer.
Resident/non – resident.
Duration since the first opening of a credit account.
Duration since the most recent opening of a credit account.
The total amount of bank loans.
No. of banks borrowed from (recent 2 years).
No. of credit guarantees.
No. of arrears and delinquencies.
Duration since the most recent arrear.
No. of days in the longest arrear.
Highest amount in current arrears.
No. of days in the longest arrear within recent 2 years.
No. of current loan arrears.
No. of credit inquiries.
Date of the most recent credit enquiry.
No. of loans.
Variables: Individual lending credit scoring model
No. of days since credit card opening.
Utilization rate in bank loan commitment.
Amount of the largest loan.
Utilization rate in card loan commitment (preceding 12 months).
Debt payment ratio (total debt/total income).
Debt ratio (debt/available debt).
Duration of credit.
Source: Authors own compilation
It is important to note that in South Africa, the NCA regulation does not allow for any unfair discrimination in the scorecards. Irrespective of what type of model, it is clear that it is not a simple procedure to develop a model. Sufficient and quality data is needed, together with the expert knowledge to implement. Other than continuous testing, it is also important to apply the most relevant variables. It is also important to custom make models for a relevant bank.
According to Beck et al. (2008) a ‘cut and paste’ of a competitors credit scoring model will not work. The motivation is that a powerful scoring model is highly personalized to a given bank – it should take into account the organizations’ tacit knowledge, brand perception, customer base, product lines, and history.
Due to the specialist nature of this field, it can be assumed that there must be many challenges faced by the banks to grant credit to small businesses (Beck et al., 2008). This aspect will be further explored.