Credit Scoring Model and Workflow Automation for Aluminium Bahrain

الموضوعات

اقتصاد

الملخص

Over the last decades, there has been considerable attention to bankruptcy prediction models and corporate failure prediction issues. The area of corporate failure prediction has turned into an important area of finance specifically because numerous scholars put forward various types of prediction model. A Logistic Regression-Based Credit Risk Model is developed to determine the creditworthiness of Aluminum Bahrain’s (ALBA) B2B customers, and it’s compared with Altman Z-score to determine with the aim of simplifying the credit risk evaluation process of the company and identifying which model has higher accuracy. The proposed model employs five financial ratios; Working Capital/Total Assets, Retained Earnings/Total Assets, EBIT/Total Assets, Market Value of Equity/Total Liabilities, and Sales/Total Assets, as independent variables to estimate the Credit Risk. A balanced dataset of 100 companies (50 approved, 50 rejected), has been given by ALBA based on ALBA's trade credit history, which is employed for model training and validation. The classification performance of the developed credit risk model is subsequently compared with that of the conventional Altman Z-Score model to ascertain which one has greater accuracy and better alignment with ALBA's credit assessment goals. Results showcased that the developed model has higher accuracy (98.04%) in contrast with the Altman Z-score model (86.21%). This research contributes a dual-model framework that reduces reliance on subjective credit evaluations, enhances operational efficiency, and introduces greater transparency in credit decisions. Overall, this thesis demonstrates how Credit Risk methodologies, both traditional and regression-based can be integrated into ALBA’s credit scoring system to objectively distinguish between financially stable and distressed clients.

روابط وملفات

التعريف والنوع

رقم الوثيقة

03231fb1-d211-49f3-b57f-fc17bc281193

رقم العقد

0

نوع الوسائط

Crawler

نوع المحتوى

الرسائل العلمية

صيغة المصدر

رسائل ماجيستير

نوع الملف

pdf text

أسماء الملفات

03231fb1-d211-49f3-b57f-fc17bc281193_1.pdf

بيانات النشر

ألقاب المؤلفين

[{"name_ar":" Ahmed Alhaiki","title_ar":"اشراف","title_en":"Supervision"},{"name_ar":" Maheen Ali Albaqali","title_ar":"اعداد","title_en":"Preparation"}]

اللغة

English

المصدر والدورية

اسم المصدر

Credit Scoring Model and Workflow Automation for Aluminium Bahrain

المحتوى والصفحات

عدد الصفحات

0

كلمات الباحثين

Financial Models, Bankruptcy Prediction, Credit Risk, Logistic Regression, Altman Z-Score, Aluminum Bahrain (ALBA), Financial Analysis, Prediction Accuracy

إشراف وإعداد

الإشراف

Ahmed Alhaiki

الإعداد

Maheen Ali Albaqali

الاقتباسات الببليوغرافية