Trade-Linked Credit Risk Assessment in Banking: An Applied Statistical Study

Abstract

This study develops and demonstrates a trade-linked statistical classification approach for assessing credit deterioration in branch banking. The focus is on borrowers whose repayment capacity is shaped by letters of credit, invoice settlement, foreign-exchange exposure, trade turnover and supplier or customer concentration. The study constructs a transparent risk-assessment design using borrower financial indicators, account-conduct signals and trade-transaction variables. A reproducible synthetic branch-portfolio dataset of 620 borrower-facility observations is used to demonstrate the method because customer-level banking records are confidential and were not available for public disclosure. Logistic regression, linear discriminant analysis and a classification tree are compared using AUROC, precision-recall area, sensitivity, precision, F1 score and operational workload indicators. The demonstration indicates that trade-linked indicators add practical early-warning value when they are combined with conventional credit variables. Logistic regression produced the strongest operational recall in the demonstration, while linear discriminant analysis produced the highest apparent accuracy at the selected threshold but missed more adverse migrations. Invoice overdue days, sector stress, collateral coverage, debt-service coverage, trade facility utilisation and trade turnover volatility were the most influential drivers in the transparent model. The study translates credit-risk classification into a branch-level monitoring process that connects statistical scoring with trade-finance information and managerial action. It shows how banks can use interpretable classification to move from periodic borrower review toward evidence-based watchlist management. The proposed approach can help banks prioritise trade-linked borrowers for review, standardise escalation, and improve documentation of credit-monitoring decisions.