Fintech Innovations in Credit Card Origination: A Multi-Stage Analysis of Algorithmic Lending Models
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V4I2P110Keywords:
Fintech, Credit Card Origination, Algorithmic Lending, Machine Learning, Risk Scoring, Financial InclusionAbstract
Financial Technology (fintech) is changing the landscape of past credit card origination processes through data-driven, algorithm-based models of lending to increase efficiency, accuracy and financial inclusion. This paper explores how the use of algorithmic decision-making is implemented and how it influences several steps of the credit card origination pipeline, such as prequalification, credit scoring, pricing, and approval. Through the multi-stage analytical framework, we test a variety of machine learning models, including logistic regression, gradient boosting and deep neural networks on both real-world and synthetic data reflecting various borrower profiles. This is achieved by complementing predictive performance measures (AUC, F1-score) with fairness requirements (disparate impact ratio, equal opportunity difference) as part of our approach to check the effectiveness and ethical validity of the models. The result suggests that algorithmic models are much superior to traditional rule-based systems in terms of predictive capability and provide greater segmentation of credit risk. Nonetheless, unequal modeling of different demographics indicates that there is a role to be played by fairness-sensitive design and regulations. The breadth of this research includes a detailed perspective on how fintech innovations can streamline credit card provision, as well as the dangers of algorithmic bias and explainability. The findings provide financial institutions, regulators, and technologists with best practices on the responsible deployment of AI in consumer credit markets
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