1. SME Loan Decision Making Process: A Declining Role of Human Capital NATIONAL CHENG KUNG UNIVERSITY – INSTITUTE OF CREATIVE INDUSTRY DESIGN Ottavia, Shao-Chi Chang Ph.D., Ding-Bang Luh Ph.D.

2. Agenda  Quick Review  Background & Motivation  Model & Hypotheses  Method used  Results & Discussion  Conclusion & Suggestions  Q & A APMC

3. Background & Motivation  Loan: the biggest contributor in bank income (Golin, 2001)  51.81% of bank income (Indonesian bank statistics, 2008)  Business vs. Risk  Wrong decision affects bank’s financial performance & reputation (Coleshaw, 1989)  Human factor in decision making process (Coleshaw, 1989, Van Buren, 1999) About Loan

4. Research Objectives  Borrower’s attributes vs. loan approval  Human capital’s influence in decision  Are there priorities? The Expected Outcomes

5. Research Model & Hypotheses Possibility of Loan Approval Borrower’s Attributes Loan officers’ Human Capital Education Banking experience Lending experience Exposure to SME Relationship with the bank Firm size Value of collateral Related business experience Share of investment M1M1 M2M2 M3M3 M4M4 X1X1 X2X2 X3X3 X4X4 X5X5 The Basic Model H 1, H 2, H 3, H 4, H 5 H 6, H 7 Hypotheses  Positive relationships (H 1 - H 5 ):  Borrower’s attributes vs. loan approval  Human capital’s influence (H 6 ):  Different judgments on attributes

6. Methodology  Survey:  Target: Bank loan officers  Location: Indonesia  Metric conjoint experiment  5 attributes  2 levels: high & low  Scenarios: 10 hypothetical companies  Approval rate (scale 1 – 7)

7. The results: 291 respondents Descriptive Data Analysis Demography 1.Education Up to high school248.20 College/academy4615.80 Bachelor16255.70 Master degree & more5920.30 2.Bank Experience < 5 years9131.30 6 - 10 years6522.30 11 - 15 years10435.70 > 15 years3110.70 3.Credit Experience < 5 years9030.90 6 - 10 years6522.30 11 - 15 years8127.80 > 15 years5518.90 4.SME Experience none4415.10 1 - 105017.20 11 - 204615.80 21 - 307626.10 > 307525.80 1.Age 20 - 307425.40 31 - 4017660.50 41 - 504114.10 2.Gender Male13847.40 Female15352.60 3.Department Risk10335.40 Marketing18864.60 Human Capital  291 responses:  255 on-the-spot  36 by email  59 not returned 15 branches 3 credit HQ

8. Borrower’s Attributes & Loan Approval Conjoint Analysis Result (1/3) No.ItemCodeUtility 1Relationship with the bankRel Low-0.403 Rel High0.403 2Value of collateralCol Low-0.616 Col High0.616 3Firm sizeSize Low-0.140 Size High0.140 4Related business experienceExp Low-0.729 Exp High0.729 5Share of investmentShare Low-0.346 Share High0.346 Results:  When attributes are low, the utility value is negative  When attributes are high, the utility value is positive  Positive relationships between borrower’s attributes & loan approval  Hypotheses H 1 – H 5 are proven Low High REL COL SIZEEXPSHARE

9. The Importance Values Conjoint Analysis Result (2/3) In their own words “The most important thing is business experience. It reflects the capacity of applicants; how well he/she can manage the company and cope with the nature of the business. The longer one survives; the better one’s capacity is to run the business.” S. D. Analyst - Jakarta branch 13 years in credit No.ItemCodeValue 1Relationship with the bankRel16.726 2Value of collateralCol25.031 3Firm sizeSize11.634 4Related business experienceExp29.238 5Share of investmentShare17.371 RELCOLSIZEEXPSHARE

10. The Least of All – Firm size Conjoint Analysis Result (3/3) In their own words “Size actually is not what matter most; as long as the business can make profit that is enough to repay the loan, why not?” M. S. Marketing - Bali branch 10 years in credit No.ItemCodeValue 1Relationship with the bankRel16.726 2Value of collateralCol25.031 3Firm sizeSize11.634 4Related business experienceExp29.238 5Share of investmentShare17.371 RELCOLSIZEEXPSHARE

11. The Influence of Human Capital One-way ANOVA (1/2)  Human capital factors have affect on the judgment over share of investment (except banking experience)  H 6 is partially proven. Human Capital Factors Relationship with the BankValue of CollateralFirm Size Related Business ExperienceShare of Investment LowHigh F value LowHigh F value LowHigh F value LowHigh F value LowHigh F value Mean Education Up to college/academy-0.4020.4020.529-0.6660.6660.954-0.1730.1730.560-0.8020.8021.657-0.4230.4234.073 ** Bachelor degree-0.4180.418 -0.5850.585 -0.1200.120 -0.7010.701 -0.3750.375 Master degree ++-0.3640.364 -0.6440.644 -0.1530.153 -0.7200.720 -0.1740.174 Banking Experience less than 5 years-0.4310.4310.571-0.5520.5522.045-0.0740.0741.458-0.6840.6842.461-0.4310.4311.202 6 - 10 years-0.4150.415 -0.7230.723 -0.1730.173 -0.6730.673 -0.2810.281 11 - 15 years-0.3880.388 -0.5950.595 -0.1720.172 -0.8110.811 -0.3160.316 16 years and more-0.3470.347 -0.6530.653 -0.1530.153 -0.7020.702 -0.3310.331 Lending experience less than 5 years-0.4570.4571.321-0.5680.5680.908-0.0540.0542.633-0.6930.6931.107-0.5760.57614.473 *** 6 - 10 years-0.4060.406 -0.6400.640 -0.2060.206 -0.7170.717 -0.3750.375 11 - 15 years-0.3780.378 -0.6710.671 -0.1680.168 -0.7950.795 -0.2890.289 16 years and more-0.3500.350 -0.5860.586 -0.1590.159 -0.7050.705 -0.0180.018 Exposure to SME none-0.4830.4831.327-0.4430.4432.522-0.0570.0571.864-0.6310.6311.692-0.5970.5977.316 *** 1 - 10-0.3650.365 -0.6500.650 -0.1150.115 -0.7100.710 -0.5450.545 11 - 20-0.4020.402 -0.7120.712 -0.2280.228 -0.7660.766 -0.3100.310 21 - 30-0.4340.434 -0.6580.658 -0.1880.188 -0.8060.806 -0.1610.161 more than 30-0.3520.352 -0.5950.595 -0.1020.102 -0.6890.689 -0.2750.275

12. More on Human Capital  Share of investment:  The higher the education, loan officer’s value toward share of investment decreases  Same tendencies on lending experience & exposure to SME  Bank experience – not influential? In their own words “I was an administration staff for 6 years before I started working as a loan officer 3 years ago. I don’t think it helped me understand the concept about credit at all. I got the grasp after attending several credit trainings, on-the-job training for two months and doing the real job.” E. H. Marketing - Surabaya branch 3 years in credit One-way ANOVA (2/2)

13. Human Capital: not as influential?  The transition from manual to computerized system  Traditional/ expert system: relies heavily on the expertise, subjective judgment, and weighting on borrower’s attributes by loan officers (Saunders & Allen, 2002)  Computerized system: more uniformed decision, based on more clear & rigid regulations. Automatically generate risk ratings (Saunders & Allen, 2002). Less room for subjective judgment.  Human factor’s role decreases in SME decision-making process Discussions

14. Research Contribution  Borrower’s attributes have positive relationship to the likelihood of loan approval  Reevaluate the result  Bank’s criteria in credit analysis  Create supporting analytical tools  Human capital: decreasing role  SME department – novice/ lower levels  High level of human capital – relocated for optimal utilization Banking industry research For academic and banking industry

15. Research Limitations  Actual settings cannot be fully represented  Simplified cases (5 attributes)  Loan officers were “forced” to plainly accept & make decisions  Universal basic theory vs. country-specific context  Hawthorne effect  Awareness of being the subjects in research (Robbins & Judge, 2007)  Possibility of different decisions in real setting Constraints & Limitations

16. Future Research  Different loan categories  Size: commercial & corporate loans  Usage: working capital vs. consumption  Link with default rate & credit profit  Compare manual & computerized system  Best composition – effective & efficient  Types of banks:  Commercial vs. state-owned  Local vs. foreign  Non-bank financial institutions  Cross-cultural studies Suggestions

17. Q & A