Knowing Ahead Mathematical Determinant of Bank Customers Credit Worthiness: A Safe Strategy for Funding Loan in a Critical Economy

The study was carried out to identify relevant attributes that signals the capacity of borrower to pay back the loan and determine the fit of mathematical scoring model to evaluate credit worthiness of a potential borrower. The data was taken from primary and secondary sources which was through the use of questionnaires (primary source) while the secondary source was collection of data from all the financial statements of selected business owners in Ekpoma, Edo State credits history of these business owners as well. The descriptive research and the explanatory research designs were employed in this study. Two research questions were raised while one hypothesis was formulated to guide the study. Thirty five (35) business owners were randomly selected from Ekpoma metropolis of Edo state for this study based on loan applications and business capacity. The data collected were analyzed using Altman Z-scores, frequencies and percentages while the Pearson Product Moment Correlation Co-efficient was used to determine the relationship between Mathematical Scoring model and credits worthiness. The result showed that credit scores developed from borrower financial and non-financial records and history such as turnover, assets, previous loan repayment rate and trading capital perfectly classified them into five risk classes of A (Worthy and very able to payback), B (worthy and less able to pay back) and D (not worthy at all). The result revealed that credit score can safe award banks and creditors against credit risk default and loss of money. It was therefore recommended among others, that banks and credit facilities handlers should adopt mathematical credit scoring techniques to avoid loss of their money.