Optimizing Credit Limit Allocation via K-Nearest Neighbors: A Behavioral Risk Profiling Approach

Abstract

Aim: The primary aim of this study is to optimize credit limit allocation using a data-driven behavioral risk profiling approach based on the K-Nearest Neighbors (KNN) algorithm. Traditional credit limit assignment often relies on static scoring systems that fail to incorporate dynamic customer behavioral patterns. This research proposes a machine learning-based framework to enhance precision in credit limit decisions. The study seeks to minimize default risk while maximizing customer credit utilization efficiency. It also aims to improve fairness and personalization in credit allocation. Ultimately, the objective is to balance profitability and risk exposure in financial institutions.

Method: The proposed methodology utilizes customer behavioral data including repayment history, credit utilization ratio, transaction frequency, income patterns, and delinquency records. Data preprocessing techniques such as normalization and outlier removal were applied to ensure model stability. The K-Nearest Neighbors algorithm was employed to classify customers into behavioral risk segments. Optimal credit limits were assigned based on proximity to low-risk behavioral clusters. Model performance was evaluated using accuracy, precision, recall, and ROC-AUC metrics. Cross-validation was conducted to enhance robustness and prevent overfitting.

Results: Experimental results indicate that the KNN-based behavioral profiling model significantly improves credit limit allocation accuracy compared to traditional rule-based systems. The model demonstrated improved classification accuracy and better identification of high-risk borrowers. Credit limits assigned through the proposed approach showed reduced default rates and improved portfolio stability. Low-risk customers received optimized higher credit limits, enhancing customer satisfaction and utilization rates. The system also reduced misclassification of medium-risk customers. Overall, predictive efficiency improved substantially.

Conclusion: The study confirms that KNN-based behavioral risk profiling is an effective tool for optimizing credit limit allocation. By incorporating dynamic behavioral indicators, financial institutions can make more personalized and risk-sensitive decisions. The model enhances portfolio quality while maintaining customer growth opportunities. It provides a scalable and interpretable alternative to complex black-box models. The framework can be integrated into existing credit management systems. Future research may explore hybrid ensemble models for further optimization.