Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Subject: search.filters.subject.Credit Scoring

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    Advancements in Credit Scoring, Profit Scoring, and Portfolio Optimization for P2P Lending
    (Institute of Electrical and Electronics Engineers Inc., 2024) Nayaka, P.; Hegde, A.; Bhowmik, B.
    The Peer-to-peer (P2P) lending platform allows borrowers to connect directly with lenders outside traditional banking systems. Therefore, for the sustainability of these platforms, they must accurately assess the credit risk and profitability of the loans. Various credit scoring techniques, including Logistic Regression, neural networks, and ensemble methods, can be used to estimate the likelihood of borrower default. It is imperative to analyze the profit the lenders generated and enhance the credit scoring so that the investors face minimum loss. Once the profit analysis is done, then it is crucial to advise the investors about the portfolio of loans. This paper presents recent credit scoring, profit scoring, and portfolio optimization trends for P2P lending. We highlight the significant issues in incorporating machine learning models into credit scoring systems. The analysis emphasizes the need for a data-driven approach to perfecting lending practices, thus benefiting both borrowers and investors in the rapidly changing P2P landscape. © 2024 IEEE.
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    Navigating Data Imbalances in Credit Risk Management: A One-Sided Selection Approach
    (Institute of Electrical and Electronics Engineers Inc., 2024) Bennehalli, S.J.; Vakkund, S.; Anusha Hegde, H.; Bhowmik, B.
    Credit scoring plays a vital role in mitigating the information asymmetry that is pervasive on platforms for peer-to-peer (P2P) lending. A considerable challenge stems from the disparity in loan repayment outcomes: a significant minority of loan applicants defaulting on their loans, while the majority fulfilling their repayment obligations. The presence of imbalance in the dataset has the potential to incorporate bias into predictive model, which could lower its performance. In order to address this issue, data balancing techniques are often employed to enhance the performance of credit scoring models through the generation of datasets that are more balanced. This work constructs a robust credit scoring model capable of precisely assessing the creditworthiness of individuals seeking P2P lending. Four distinct classifiers - Logistic Regression, Random Forest, LightGBM, and Support Vector Machine (SVM) are employed. In doing so, it effectively mitigates the distortions that can result from unbalanced data distributions. This work achieves data balance with One-Sided Selection methodology along with Information gain and Pearson correlation which mainly determine the features to include. The proposed model thus works on both balanced and unbalanced datasets. Experimental results show that the standard metrics like accuracy, precision, recall, and F1-Score achieves upto 90.41%, 89.51%, 90.40%, and 89.96%, respectively. © 2024 IEEE.