Abstract
The objective of this study was to apply the logistic regression technique in the development of a model for predicting credit scoring using data from a financial institution. From a sample of 20,000 data, three sub-samples were defined: one sample for model construction (8,000 data) and two other ones for validation, each one with 6,000 data. In the three sub-samples, there was an equitable distribution of good and bad clients, classified into these categories according to institutional standards. The logistic regression model presented adequate indicators of data adjustment in the results, which can be used in the decision-making process of bank credit concessions.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
