One of the aims of the banking business is to provide loans to applicants. Credit risk management plays an important role in banks, as loans generally account for half to three quarters of the total value of bank assets. Credit scoring is a systematic method for evaluating credit risk and assists decision makers determine whether or not to provide loans to applicants. Scoring models are systematic means of evaluating the
creditworthiness of a loan applicant. However, existing scoring models cause some loan applications to be rejected unnecessarily as their credit rates are lowered to rejection levels due to lack of information such as previous loan payment data. This might be
refusal of good credit, which potentially can cause the loss of future profit margins. This study aims at optimising one such credit scoring model to ensure that it uses only the critical scoring criteria to determine a credit score. The optimised model will not only reduce the proportion of unsafe borrowers, but also identify profitable borrowers.