A Comprehensive Analytical Framework for Modeling Consumer Credit Card Behavior and Risk Profiling Using Advanced Financial Metrics
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V3I2P111Keywords:
Credit Card Behavior, Risk Profiling, Financial Metrics, Machine Learning, Consumer Segmentation, Composite Risk Index, Predictive Modeling, Credit ScoringAbstract
The conception of consumer behavior within the framework of credit card usage plays a central role in the effort of financial institutions to analyze risk and best practices in their customer relationship management initiatives. This paper offers an overall analytical construct to the modeling of consumer credit card behavior and risk profiling based on advanced financial measurements. Based on multidimensional data and using state-of-the-art statistical and machine learning techniques, the framework encloses behavioural segmentation, credit risk scoring and predictive analytics. Our model illustrates the way financial institutions could attain the greater stratification of risks and individualization of their propositions through incorporating transactional, demographic, and psychographic variables. This model employs the best methods (both supervised and unsupervised) of finding meaningful patterns in extensive data through the implementation of clustering approaches in the segmentation of consumers and logistic regression and gradient boosting machines in predicting risk. Such financial measures as the debt-to-income ratio, credit utilisation, and payment Records are inherent parts of the analysis process, increasing the precision and relevance of the model. The study also comes up with a new Composite Risk Index (CRI), which is constructed by using weighted financial indicators that are adjustable according to market conditions. We tested our implementation on a database of data obtained by top credit card issuers before May 2022, and we found a large difference in the performance of default prediction and customer classification. Compared to classical measurements of credit ratings, such as FICO, our model is more accurate and provides an early warning. The results are interpreted intuitively through visual analytics and dashboards that guide decision-makers on real-time adjustments to credit policy. This system provides the foundation of smart financial decision-making to create a sustainable credit ecosystem and sensible consumer habits
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