Abstract:
Abstract: Measuring credit risk is essential for financial institutions because there is a high risk
level associated with incorrect credit decisions. The Basel II agreement recommended the use of
advanced credit scoring methods in order to improve the efficiency of capital allocation. The latest
Basel agreement (Basel III) states that the requirements for reserves based on risk have increased.
Financial institutions currently have exhaustive datasets regarding their operations; this is a problem
that can be addressed by applying a good feature selection method combined with big data techniques
for data management. A comparative study of selection techniques is conducted in this work to find
the selector that reduces the mean square error and requires the least execution time.
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