Sayar, AlperenAtes, YigitErtugrul, SeyitTuran, Elif NazDrias, YassineÇakar, Tuna2025-10-052025-10-0520259798331566555https://doi.org/10.1109/SIU66497.2025.11112027https://hdl.handle.net/20.500.11779/3097Isik UniversityCredit scoring systems are critical tools used by factoring institutions to assess the credit risks of SME businesses seeking microloans. This study presents a comprehensive predictive modeling framework that achieves 82.67% ROC-AUC with 65.34% Gini score on test data, demonstrating robust discriminative capability despite significant class imbalance. Our ensemble approach outperforms individual boosting models by leveraging their complementary strengths in payment behavior analysis and fraud detection. The raw data was cleaned, transformed, and optimized using the Polars library, with specialized features for detecting fraud patterns and time-based risk indicators. When implementing a score threshold of 950, our model significantly improves the detection of non-performing loans (NPL) compared to traditional rule-based approaches by reducing the net deficit from 6.59% to 2.62%. When applied to previously rejected applications, the model projects a potential 762.57% increase in transaction count and 747.05% growth in transaction volume. © 2025 Elsevier B.V., All rights reserved.trinfo:eu-repo/semantics/closedAccessCredit Risk ManagementData ProcessingFactoring ScorecardsFinancial Data AnalyticsFraud DetectionMicroloansNon Performing LoansPolarsCrimeInformation ManagementRisk AssessmentRisk ManagementData AnalyticsFactoring ScorecardFinancial DataFinancial Data AnalyticMachine-LearningMicroloanNon Performing LoanPolarData HandlingConference Object10.1109/SIU66497.2025.111120272-s2.0-105015565819