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Automated Credit Scoring

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dc.contributor.author 03-134221-031, Muhammad Wahaaj Tauqir
dc.date.accessioned 2026-02-17T05:28:54Z
dc.date.available 2026-02-17T05:28:54Z
dc.date.issued 2026-01-01
dc.identifier.uri http://hdl.handle.net/123456789/20585
dc.description Dr. Muhammad Saqib Sohail en_US
dc.description.abstract Commercial clients (business owners, retailers) of wholesale distributors often rely on credit lines to purchase goods, especially during economic crises when banks hesitate to lend to retailers. This affects the sales of distributors, prompting them to offer products on credit. However, when distributors extend credit, they face the risk of customer default. They need a reliable method to evaluate the creditworthiness of their customers to minimize financial loss. To address these challenges, we propose an automated AI driven credit scoring system that automates the evaluation process by analysing several features including customer purchase behaviour, transaction history, and repayment patterns to the interpretable scorecard. The system integrates generative AI–powered variable classification, automated preprocessing, Auto-Monotonic Fine Binning, and advanced ensemble modeling and instant generation of points-based scorecard. Deploying the solution as a secure cloud-native application ensures rapid, accurate, and fully auditable credit decisions significantly improving risk management and operational efficiency for wholesale distributors. en_US
dc.language.iso en_US en_US
dc.relation.ispartofseries ;BULC1502
dc.title Automated Credit Scoring en_US


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