Based on SPSS Analysis of Lending Factors on Small Bank Enterprises Dataset

As the society is currently in a recession and loans have become the center of attention, the problem of loan defaults is also a major concern for lending institutions. In this paper, we use the Small Business Administration dataset to analyze the data to help credit institutions such as banks to issue loans more efficiently and accurately in order to reduce the amount of bad debts and the amount of guarantors' burden. Specifically, using chi-square tests, we found that old loans have significantly higher loan amounts and repayment rates compared to new loans, and that having a real estate guarantee significantly increases their loan amounts and repayment rates. We also conducted a factor analysis and found that total payments, loan amount, and guarantees for small bank businesses serve as the first principal component, i.e., for loans can provide the most effective amount of information. In addition, the results of ANOVA showed that as the number of lending operations in a state increases, total loans, small bank business guarantees, and total payments significantly increase. Our study is useful in helping credit institutions such as banks to provide more efficient and accurate loan disbursements in order to reduce bad loans and SBA guarantees.Keywords: Loan, Independent sample t-test, Chi-Squared Test, Cross Tabulations, Singer Factor ANOVA, Factor Analysis.